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a/translations/he/1-Introduction/1-intro-to-ML/README.md +++ b/translations/he/1-Introduction/1-intro-to-ML/README.md @@ -1,12 +1,3 @@ - # מבוא ללמידת מכונה ## [שאלון לפני השיעור](https://ff-quizzes.netlify.app/en/ml/) diff --git a/translations/he/1-Introduction/1-intro-to-ML/assignment.md b/translations/he/1-Introduction/1-intro-to-ML/assignment.md index 0c95b63f6..9b9e66b7a 100644 --- a/translations/he/1-Introduction/1-intro-to-ML/assignment.md +++ b/translations/he/1-Introduction/1-intro-to-ML/assignment.md @@ -1,12 +1,3 @@ - # להתחיל לפעול ## הוראות diff --git a/translations/he/1-Introduction/2-history-of-ML/README.md b/translations/he/1-Introduction/2-history-of-ML/README.md index 8bfa51eef..30fb51973 100644 --- a/translations/he/1-Introduction/2-history-of-ML/README.md +++ b/translations/he/1-Introduction/2-history-of-ML/README.md @@ -1,12 +1,3 @@ - # ההיסטוריה של למידת מכונה ![סיכום ההיסטוריה של למידת מכונה בסקצ'נוט](../../../../sketchnotes/ml-history.png) diff --git a/translations/he/1-Introduction/2-history-of-ML/assignment.md b/translations/he/1-Introduction/2-history-of-ML/assignment.md index 768245a5c..9c513bbe5 100644 --- a/translations/he/1-Introduction/2-history-of-ML/assignment.md +++ b/translations/he/1-Introduction/2-history-of-ML/assignment.md @@ -1,12 +1,3 @@ - # יצירת ציר זמן ## הוראות diff --git a/translations/he/1-Introduction/3-fairness/README.md b/translations/he/1-Introduction/3-fairness/README.md index 9aac6b0a9..2ba72178f 100644 --- a/translations/he/1-Introduction/3-fairness/README.md +++ b/translations/he/1-Introduction/3-fairness/README.md @@ -1,12 +1,3 @@ - # בניית פתרונות למידת מכונה עם AI אחראי ![סיכום של AI אחראי בלמידת מכונה בסקיצה](../../../../sketchnotes/ml-fairness.png) diff --git a/translations/he/1-Introduction/3-fairness/assignment.md b/translations/he/1-Introduction/3-fairness/assignment.md index 4473fc6ed..2bb191e6f 100644 --- a/translations/he/1-Introduction/3-fairness/assignment.md +++ b/translations/he/1-Introduction/3-fairness/assignment.md @@ -1,12 +1,3 @@ - # חקור את ערכת הכלים של AI אחראי ## הוראות diff --git a/translations/he/1-Introduction/4-techniques-of-ML/README.md b/translations/he/1-Introduction/4-techniques-of-ML/README.md index 172a556ba..8ad70384d 100644 --- a/translations/he/1-Introduction/4-techniques-of-ML/README.md +++ b/translations/he/1-Introduction/4-techniques-of-ML/README.md @@ -1,12 +1,3 @@ - # טכניקות בלמידת מכונה תהליך הבנייה, השימוש והתחזוקה של מודלים בלמידת מכונה והנתונים שהם משתמשים בהם שונה מאוד מתהליכי פיתוח אחרים. בשיעור זה, נבאר את התהליך ונפרט את הטכניקות המרכזיות שעליכם להכיר. אתם תלמדו: diff --git a/translations/he/1-Introduction/4-techniques-of-ML/assignment.md b/translations/he/1-Introduction/4-techniques-of-ML/assignment.md index 81d01284e..962f43760 100644 --- a/translations/he/1-Introduction/4-techniques-of-ML/assignment.md +++ b/translations/he/1-Introduction/4-techniques-of-ML/assignment.md @@ -1,12 +1,3 @@ - # ראיון עם מדען נתונים ## הוראות diff --git a/translations/he/1-Introduction/README.md b/translations/he/1-Introduction/README.md index 1f5862f1b..02603feff 100644 --- a/translations/he/1-Introduction/README.md +++ b/translations/he/1-Introduction/README.md @@ -1,12 +1,3 @@ - # מבוא ללמידת מכונה בחלק זה של תוכנית הלימודים, תכירו את המושגים הבסיסיים שמאחורי תחום הלמידת המכונה, מה זה בעצם, ותלמדו על ההיסטוריה שלו ועל הטכניקות שהחוקרים משתמשים בהן כדי לעבוד איתו. בואו נחקור יחד את העולם החדש הזה של למידת מכונה! diff --git a/translations/he/2-Regression/1-Tools/README.md b/translations/he/2-Regression/1-Tools/README.md index 1b272135d..eb866cdf5 100644 --- a/translations/he/2-Regression/1-Tools/README.md +++ b/translations/he/2-Regression/1-Tools/README.md @@ -1,12 +1,3 @@ - # התחילו עם Python ו-Scikit-learn עבור מודלים של רגרסיה ![סיכום של רגרסיות בסקצ'נוט](../../../../sketchnotes/ml-regression.png) diff --git a/translations/he/2-Regression/1-Tools/assignment.md b/translations/he/2-Regression/1-Tools/assignment.md index a3d8bc7d3..22dcb6c7d 100644 --- a/translations/he/2-Regression/1-Tools/assignment.md +++ b/translations/he/2-Regression/1-Tools/assignment.md @@ -1,12 +1,3 @@ - # רגרסיה עם Scikit-learn ## הוראות diff --git a/translations/he/2-Regression/1-Tools/solution/Julia/README.md b/translations/he/2-Regression/1-Tools/solution/Julia/README.md index 42490383e..da0ed164b 100644 --- a/translations/he/2-Regression/1-Tools/solution/Julia/README.md +++ b/translations/he/2-Regression/1-Tools/solution/Julia/README.md @@ -1,12 +1,3 @@ - זהו מציין מקום זמני --- diff --git a/translations/he/2-Regression/2-Data/README.md b/translations/he/2-Regression/2-Data/README.md index d429100a8..df726fa35 100644 --- a/translations/he/2-Regression/2-Data/README.md +++ b/translations/he/2-Regression/2-Data/README.md @@ -1,12 +1,3 @@ - # בניית מודל רגרסיה באמצעות Scikit-learn: הכנת ויזואליזציה של נתונים ![אינפוגרפיקה של ויזואליזציה של נתונים](../../../../2-Regression/2-Data/images/data-visualization.png) diff --git a/translations/he/2-Regression/2-Data/assignment.md b/translations/he/2-Regression/2-Data/assignment.md index 0bcf2c2c4..484b76294 100644 --- a/translations/he/2-Regression/2-Data/assignment.md +++ b/translations/he/2-Regression/2-Data/assignment.md @@ -1,12 +1,3 @@ - # חקר ויזואליזציות ישנן מספר ספריות שונות זמינות ליצירת ויזואליזציות של נתונים. צרו כמה ויזואליזציות באמצעות נתוני הדלעת בשיעור זה עם matplotlib ו-seaborn במחברת לדוגמה. אילו ספריות קלות יותר לשימוש? diff --git a/translations/he/2-Regression/2-Data/solution/Julia/README.md b/translations/he/2-Regression/2-Data/solution/Julia/README.md index 8e5c1899d..7b3eb5a09 100644 --- a/translations/he/2-Regression/2-Data/solution/Julia/README.md +++ b/translations/he/2-Regression/2-Data/solution/Julia/README.md @@ -1,12 +1,3 @@ - זהו מציין מקום זמני --- diff --git a/translations/he/2-Regression/3-Linear/README.md b/translations/he/2-Regression/3-Linear/README.md index af0a5d08b..4833ba551 100644 --- a/translations/he/2-Regression/3-Linear/README.md +++ b/translations/he/2-Regression/3-Linear/README.md @@ -1,12 +1,3 @@ - # בניית מודל רגרסיה באמצעות Scikit-learn: רגרסיה בארבע דרכים ![אינפוגרפיקה של רגרסיה לינארית מול פולינומית](../../../../2-Regression/3-Linear/images/linear-polynomial.png) @@ -114,11 +105,11 @@ day_of_year = pd.to_datetime(pumpkins['Date']).apply(lambda dt: (dt-datetime(dt. מהשיעור הקודם כנראה ראיתם שהמחיר הממוצע עבור חודשים שונים נראה כך: -מחיר ממוצע לפי חודש +מחיר ממוצע לפי חודש זה מציע שיכול להיות מתאם, ואנו יכולים לנסות לאמן מודל רגרסיה לינארית כדי לחזות את הקשר בין `Month` ל-`Price`, או בין `DayOfYear` ל-`Price`. הנה גרף הפיזור שמראה את הקשר האחרון: -גרף פיזור של מחיר מול יום בשנה +גרף פיזור של מחיר מול יום בשנה בואו נראה אם יש מתאם באמצעות פונקציית `corr`: @@ -137,7 +128,7 @@ for i,var in enumerate(new_pumpkins['Variety'].unique()): ax = df.plot.scatter('DayOfYear','Price',ax=ax,c=colors[i],label=var) ``` -גרף פיזור של מחיר מול יום בשנה +גרף פיזור של מחיר מול יום בשנה החקירה שלנו מציעה שלזן יש השפעה גדולה יותר על המחיר הכולל מאשר תאריך המכירה בפועל. אנו יכולים לראות זאת עם גרף עמודות: @@ -145,7 +136,7 @@ for i,var in enumerate(new_pumpkins['Variety'].unique()): new_pumpkins.groupby('Variety')['Price'].mean().plot(kind='bar') ``` -גרף עמודות של מחיר מול זן +גרף עמודות של מחיר מול זן בואו נתמקד לרגע רק בזן אחד של דלעות, 'סוג פאי', ונראה מה ההשפעה של התאריך על המחיר: @@ -153,7 +144,7 @@ new_pumpkins.groupby('Variety')['Price'].mean().plot(kind='bar') pie_pumpkins = new_pumpkins[new_pumpkins['Variety']=='PIE TYPE'] pie_pumpkins.plot.scatter('DayOfYear','Price') ``` -גרף פיזור של מחיר מול יום בשנה +גרף פיזור של מחיר מול יום בשנה אם עכשיו נחשב את המתאם בין `Price` ל-`DayOfYear` באמצעות פונקציית `corr`, נקבל משהו כמו `-0.27` - מה שאומר שאימון מודל חיזוי הגיוני. @@ -227,7 +218,7 @@ plt.scatter(X_test,y_test) plt.plot(X_test,pred) ``` -רגרסיה ליניארית +רגרסיה ליניארית ## רגרסיה פולינומית @@ -256,7 +247,7 @@ pipeline.fit(X_train,y_train) ניתן להשתמש בצינורות באותו אופן כמו באובייקט `LinearRegression` המקורי, כלומר ניתן להשתמש ב-`fit` בצינור ואז ב-`predict` כדי לקבל את תוצאות הניבוי. הנה הגרף שמראה את נתוני הבדיקה ואת עקומת הקירוב: -רגרסיה פולינומית +רגרסיה פולינומית שימוש ברגרסיה פולינומית מאפשר לנו לקבל MSE מעט נמוך יותר ומקדם החלטיות גבוה יותר, אך לא באופן משמעותי. יש לקחת בחשבון תכונות נוספות! @@ -274,7 +265,7 @@ pipeline.fit(X_train,y_train) כאן ניתן לראות איך המחיר הממוצע תלוי בסוג הדלעת: -מחיר ממוצע לפי סוג +מחיר ממוצע לפי סוג כדי לקחת את סוג הדלעת בחשבון, תחילה עלינו להמיר אותו לצורה מספרית, או **לקודד** אותו. ישנן מספר דרכים לעשות זאת: diff --git a/translations/he/2-Regression/3-Linear/assignment.md b/translations/he/2-Regression/3-Linear/assignment.md index 93957c307..453e7f20d 100644 --- a/translations/he/2-Regression/3-Linear/assignment.md +++ b/translations/he/2-Regression/3-Linear/assignment.md @@ -1,12 +1,3 @@ - # יצירת מודל רגרסיה ## הוראות diff --git a/translations/he/2-Regression/3-Linear/solution/Julia/README.md b/translations/he/2-Regression/3-Linear/solution/Julia/README.md index 7af54768c..ee82f8bce 100644 --- a/translations/he/2-Regression/3-Linear/solution/Julia/README.md +++ b/translations/he/2-Regression/3-Linear/solution/Julia/README.md @@ -1,12 +1,3 @@ - זהו מציין מקום זמני --- diff --git a/translations/he/2-Regression/4-Logistic/README.md b/translations/he/2-Regression/4-Logistic/README.md index e09ba8bbe..db3554361 100644 --- a/translations/he/2-Regression/4-Logistic/README.md +++ b/translations/he/2-Regression/4-Logistic/README.md @@ -1,12 +1,3 @@ - # רגרסיה לוגיסטית לחיזוי קטגוריות ![אינפוגרפיקה של רגרסיה לוגיסטית מול רגרסיה ליניארית](../../../../2-Regression/4-Logistic/images/linear-vs-logistic.png) diff --git a/translations/he/2-Regression/4-Logistic/assignment.md b/translations/he/2-Regression/4-Logistic/assignment.md index 1fb50008c..5f79a40aa 100644 --- a/translations/he/2-Regression/4-Logistic/assignment.md +++ b/translations/he/2-Regression/4-Logistic/assignment.md @@ -1,12 +1,3 @@ - # ניסיון חוזר עם רגרסיה ## הוראות diff --git a/translations/he/2-Regression/4-Logistic/solution/Julia/README.md b/translations/he/2-Regression/4-Logistic/solution/Julia/README.md index 4f66a0dfb..da0ed164b 100644 --- a/translations/he/2-Regression/4-Logistic/solution/Julia/README.md +++ b/translations/he/2-Regression/4-Logistic/solution/Julia/README.md @@ -1,12 +1,3 @@ - זהו מציין מקום זמני --- diff --git a/translations/he/2-Regression/README.md b/translations/he/2-Regression/README.md index 76e331971..1000f462d 100644 --- a/translations/he/2-Regression/README.md +++ b/translations/he/2-Regression/README.md @@ -1,12 +1,3 @@ - # מודלים של רגרסיה ללמידת מכונה ## נושא אזורי: מודלים של רגרסיה למחירי דלעת בצפון אמריקה 🎃 diff --git a/translations/he/3-Web-App/1-Web-App/README.md b/translations/he/3-Web-App/1-Web-App/README.md index 11a12759a..6b63fb778 100644 --- a/translations/he/3-Web-App/1-Web-App/README.md +++ b/translations/he/3-Web-App/1-Web-App/README.md @@ -1,12 +1,3 @@ - # בניית אפליקציית אינטרנט לשימוש במודל למידת מכונה בשיעור הזה, תאמנו מודל למידת מכונה על סט נתונים יוצא דופן: _תצפיות עב"מים במאה האחרונה_, שנאספו ממאגר הנתונים של NUFORC. diff --git a/translations/he/3-Web-App/1-Web-App/assignment.md b/translations/he/3-Web-App/1-Web-App/assignment.md index 576c5d433..5cb5fcf5a 100644 --- a/translations/he/3-Web-App/1-Web-App/assignment.md +++ b/translations/he/3-Web-App/1-Web-App/assignment.md @@ -1,12 +1,3 @@ - # נסה מודל אחר ## הוראות diff --git a/translations/he/3-Web-App/README.md b/translations/he/3-Web-App/README.md index 8c3dda2cc..07cb9c6c9 100644 --- a/translations/he/3-Web-App/README.md +++ b/translations/he/3-Web-App/README.md @@ -1,12 +1,3 @@ - # בנה אפליקציית ווב לשימוש במודל ML שלך בחלק זה של הקורס, תיחשף לנושא יישומי בתחום למידת מכונה: כיצד לשמור את המודל שלך שנבנה ב-Scikit-learn כקובץ שניתן להשתמש בו כדי לבצע תחזיות בתוך אפליקציית ווב. לאחר שהמודל נשמר, תלמד כיצד להשתמש בו באפליקציית ווב שנבנתה ב-Flask. תחילה תיצור מודל באמצעות נתונים העוסקים בתצפיות על עב"מים! לאחר מכן, תבנה אפליקציית ווב שתאפשר לך להזין מספר שניות יחד עם ערכי קו רוחב וקו אורך כדי לחזות באיזו מדינה דווח על עב"ם. diff --git a/translations/he/4-Classification/1-Introduction/README.md b/translations/he/4-Classification/1-Introduction/README.md index 8b9f7ebd1..ef2c67435 100644 --- a/translations/he/4-Classification/1-Introduction/README.md +++ b/translations/he/4-Classification/1-Introduction/README.md @@ -1,12 +1,3 @@ - # מבוא לסיווג בארבעת השיעורים הללו, תחקור את אחד הנושאים המרכזיים בלמידת מכונה קלאסית - _סיווג_. נעבור יחד על שימוש באלגוריתמים שונים לסיווג עם מערך נתונים על כל המטבחים המדהימים של אסיה והודו. מקווים שאתה רעב! diff --git a/translations/he/4-Classification/1-Introduction/assignment.md b/translations/he/4-Classification/1-Introduction/assignment.md index 0f73c54d2..8f72f5925 100644 --- a/translations/he/4-Classification/1-Introduction/assignment.md +++ b/translations/he/4-Classification/1-Introduction/assignment.md @@ -1,12 +1,3 @@ - # חקור שיטות סיווג ## הוראות diff --git a/translations/he/4-Classification/1-Introduction/solution/Julia/README.md b/translations/he/4-Classification/1-Introduction/solution/Julia/README.md index 406708b74..7b3eb5a09 100644 --- a/translations/he/4-Classification/1-Introduction/solution/Julia/README.md +++ b/translations/he/4-Classification/1-Introduction/solution/Julia/README.md @@ -1,12 +1,3 @@ - זהו מציין מקום זמני --- diff --git a/translations/he/4-Classification/2-Classifiers-1/README.md b/translations/he/4-Classification/2-Classifiers-1/README.md index 647e7cf06..3bca12c7a 100644 --- a/translations/he/4-Classification/2-Classifiers-1/README.md +++ b/translations/he/4-Classification/2-Classifiers-1/README.md @@ -1,12 +1,3 @@ - # מסווגי מטבחים 1 בשיעור הזה, תשתמשו במאגר הנתונים ששמרתם מהשיעור הקודם, שמלא בנתונים מאוזנים ונקיים על מטבחים. diff --git a/translations/he/4-Classification/2-Classifiers-1/assignment.md b/translations/he/4-Classification/2-Classifiers-1/assignment.md index 8e43dd0d6..6d96e8d6c 100644 --- a/translations/he/4-Classification/2-Classifiers-1/assignment.md +++ b/translations/he/4-Classification/2-Classifiers-1/assignment.md @@ -1,12 +1,3 @@ - # למד את הפותרים ## הוראות diff --git a/translations/he/4-Classification/2-Classifiers-1/solution/Julia/README.md b/translations/he/4-Classification/2-Classifiers-1/solution/Julia/README.md index 368b0d4ea..da0ed164b 100644 --- a/translations/he/4-Classification/2-Classifiers-1/solution/Julia/README.md +++ b/translations/he/4-Classification/2-Classifiers-1/solution/Julia/README.md @@ -1,12 +1,3 @@ - זהו מציין מקום זמני --- diff --git a/translations/he/4-Classification/3-Classifiers-2/README.md b/translations/he/4-Classification/3-Classifiers-2/README.md index 17132347f..1eee10e58 100644 --- a/translations/he/4-Classification/3-Classifiers-2/README.md +++ b/translations/he/4-Classification/3-Classifiers-2/README.md @@ -1,12 +1,3 @@ - # מסווגי מטבחים 2 בשיעור הסיווג השני הזה, תחקור דרכים נוספות לסווג נתונים מספריים. בנוסף, תלמד על ההשלכות של בחירת מסווג אחד על פני אחר. diff --git a/translations/he/4-Classification/3-Classifiers-2/assignment.md b/translations/he/4-Classification/3-Classifiers-2/assignment.md index 53a05ba0f..1fc646ac8 100644 --- a/translations/he/4-Classification/3-Classifiers-2/assignment.md +++ b/translations/he/4-Classification/3-Classifiers-2/assignment.md @@ -1,12 +1,3 @@ - # משחק עם פרמטרים ## הוראות diff --git a/translations/he/4-Classification/3-Classifiers-2/solution/Julia/README.md b/translations/he/4-Classification/3-Classifiers-2/solution/Julia/README.md index 7b10519b7..7b3eb5a09 100644 --- a/translations/he/4-Classification/3-Classifiers-2/solution/Julia/README.md +++ b/translations/he/4-Classification/3-Classifiers-2/solution/Julia/README.md @@ -1,12 +1,3 @@ - זהו מציין מקום זמני --- diff --git a/translations/he/4-Classification/4-Applied/README.md b/translations/he/4-Classification/4-Applied/README.md index b6160ed16..db660dfd4 100644 --- a/translations/he/4-Classification/4-Applied/README.md +++ b/translations/he/4-Classification/4-Applied/README.md @@ -1,12 +1,3 @@ - # בניית אפליקציית המלצות למטבח בשיעור זה תבנו מודל סיווג באמצעות כמה מהטכניקות שלמדתם בשיעורים קודמים, ותשתמשו במאגר הנתונים הטעים של מטבחים שהשתמשנו בו לאורך הסדרה. בנוסף, תבנו אפליקציית אינטרנט קטנה שתשתמש במודל שמור, תוך שימוש ב-Onnx Web Runtime. diff --git a/translations/he/4-Classification/4-Applied/assignment.md b/translations/he/4-Classification/4-Applied/assignment.md index e88d0bda3..0535f2e53 100644 --- a/translations/he/4-Classification/4-Applied/assignment.md +++ b/translations/he/4-Classification/4-Applied/assignment.md @@ -1,12 +1,3 @@ - # בנה מערכת המלצות ## הוראות diff --git a/translations/he/4-Classification/README.md b/translations/he/4-Classification/README.md index a295e2097..3b2702761 100644 --- a/translations/he/4-Classification/README.md +++ b/translations/he/4-Classification/README.md @@ -1,12 +1,3 @@ - # התחלת עבודה עם סיווג ## נושא אזורי: מטבחים אסייתיים והודיים טעימים 🍜 diff --git a/translations/he/5-Clustering/1-Visualize/README.md b/translations/he/5-Clustering/1-Visualize/README.md index e4cf129c3..4ac36e533 100644 --- a/translations/he/5-Clustering/1-Visualize/README.md +++ b/translations/he/5-Clustering/1-Visualize/README.md @@ -1,12 +1,3 @@ - # מבוא לקיבוץ קיבוץ הוא סוג של [למידה ללא פיקוח](https://wikipedia.org/wiki/Unsupervised_learning) שמניח כי מערך הנתונים אינו מתויג או שהקלטים שלו אינם מותאמים לפלטים מוגדרים מראש. הוא משתמש באלגוריתמים שונים כדי למיין נתונים לא מתויגים ולספק קבוצות בהתאם לדפוסים שהוא מזהה בנתונים. diff --git a/translations/he/5-Clustering/1-Visualize/assignment.md b/translations/he/5-Clustering/1-Visualize/assignment.md index a2b4a617b..8504218ec 100644 --- a/translations/he/5-Clustering/1-Visualize/assignment.md +++ b/translations/he/5-Clustering/1-Visualize/assignment.md @@ -1,12 +1,3 @@ - # חקר ויזואליזציות אחרות עבור אשכולות ## הוראות diff --git a/translations/he/5-Clustering/1-Visualize/solution/Julia/README.md b/translations/he/5-Clustering/1-Visualize/solution/Julia/README.md index df1144298..7b3eb5a09 100644 --- a/translations/he/5-Clustering/1-Visualize/solution/Julia/README.md +++ b/translations/he/5-Clustering/1-Visualize/solution/Julia/README.md @@ -1,12 +1,3 @@ - זהו מציין מקום זמני --- diff --git a/translations/he/5-Clustering/2-K-Means/README.md b/translations/he/5-Clustering/2-K-Means/README.md index 5269e4689..b5921e823 100644 --- a/translations/he/5-Clustering/2-K-Means/README.md +++ b/translations/he/5-Clustering/2-K-Means/README.md @@ -1,12 +1,3 @@ - # אשכולות K-Means ## [מבחן מקדים](https://ff-quizzes.netlify.app/en/ml/) diff --git a/translations/he/5-Clustering/2-K-Means/assignment.md b/translations/he/5-Clustering/2-K-Means/assignment.md index b020d1f43..26440991e 100644 --- a/translations/he/5-Clustering/2-K-Means/assignment.md +++ b/translations/he/5-Clustering/2-K-Means/assignment.md @@ -1,12 +1,3 @@ - # נסו שיטות אשכולות שונות ## הוראות diff --git a/translations/he/5-Clustering/2-K-Means/solution/Julia/README.md b/translations/he/5-Clustering/2-K-Means/solution/Julia/README.md index 1034a0a12..7b3eb5a09 100644 --- a/translations/he/5-Clustering/2-K-Means/solution/Julia/README.md +++ b/translations/he/5-Clustering/2-K-Means/solution/Julia/README.md @@ -1,12 +1,3 @@ - זהו מציין מקום זמני --- diff --git a/translations/he/5-Clustering/README.md b/translations/he/5-Clustering/README.md index 1ae6208d9..30bbfa49a 100644 --- a/translations/he/5-Clustering/README.md +++ b/translations/he/5-Clustering/README.md @@ -1,12 +1,3 @@ - # מודלים של אשכולות ללמידת מכונה אשכולות הם משימה בלמידת מכונה שבה מחפשים למצוא אובייקטים הדומים זה לזה ולחבר אותם לקבוצות הנקראות אשכולות. מה שמבדיל אשכולות מגישות אחרות בלמידת מכונה הוא שהדברים מתרחשים באופן אוטומטי, למעשה, אפשר לומר שזה ההפך מלמידה מונחית. diff --git a/translations/he/6-NLP/1-Introduction-to-NLP/README.md b/translations/he/6-NLP/1-Introduction-to-NLP/README.md index 5defa4617..d2a416313 100644 --- a/translations/he/6-NLP/1-Introduction-to-NLP/README.md +++ b/translations/he/6-NLP/1-Introduction-to-NLP/README.md @@ -1,12 +1,3 @@ - # מבוא לעיבוד שפה טבעית השיעור הזה מכסה היסטוריה קצרה ומושגים חשובים של *עיבוד שפה טבעית*, תחום משנה של *בלשנות חישובית*. diff --git a/translations/he/6-NLP/1-Introduction-to-NLP/assignment.md b/translations/he/6-NLP/1-Introduction-to-NLP/assignment.md index a0f122cae..526475f88 100644 --- a/translations/he/6-NLP/1-Introduction-to-NLP/assignment.md +++ b/translations/he/6-NLP/1-Introduction-to-NLP/assignment.md @@ -1,12 +1,3 @@ - # חיפוש אחר בוט ## הוראות diff --git a/translations/he/6-NLP/2-Tasks/README.md b/translations/he/6-NLP/2-Tasks/README.md index 0b5aba06f..a7c54e370 100644 --- a/translations/he/6-NLP/2-Tasks/README.md +++ b/translations/he/6-NLP/2-Tasks/README.md @@ -1,12 +1,3 @@ - # משימות וטכניקות נפוצות בעיבוד שפה טבעית ברוב המשימות של *עיבוד שפה טבעית*, הטקסט שיש לעבד חייב להיות מפורק, נבדק, והתוצאות נשמרות או מושוות עם חוקים ומאגרי נתונים. משימות אלו מאפשרות למתכנת להסיק את _המשמעות_ או _הכוונה_ או רק את _תדירות_ המונחים והמילים בטקסט. diff --git a/translations/he/6-NLP/2-Tasks/assignment.md b/translations/he/6-NLP/2-Tasks/assignment.md index ca8c64d60..a17b21243 100644 --- a/translations/he/6-NLP/2-Tasks/assignment.md +++ b/translations/he/6-NLP/2-Tasks/assignment.md @@ -1,12 +1,3 @@ - # לגרום לבוט להגיב ## הוראות diff --git a/translations/he/6-NLP/3-Translation-Sentiment/README.md b/translations/he/6-NLP/3-Translation-Sentiment/README.md index c7480402e..9f162e222 100644 --- a/translations/he/6-NLP/3-Translation-Sentiment/README.md +++ b/translations/he/6-NLP/3-Translation-Sentiment/README.md @@ -1,12 +1,3 @@ - # תרגום וניתוח רגשות עם למידת מכונה בשיעורים הקודמים למדתם כיצד לבנות בוט בסיסי באמצעות `TextBlob`, ספרייה שמשתמשת בלמידת מכונה מאחורי הקלעים כדי לבצע משימות בסיסיות של עיבוד שפה טבעית כמו חילוץ ביטויי שם עצם. אתגר חשוב נוסף בבלשנות חישובית הוא תרגום מדויק של משפט משפה מדוברת או כתובה אחת לשפה אחרת. diff --git a/translations/he/6-NLP/3-Translation-Sentiment/assignment.md b/translations/he/6-NLP/3-Translation-Sentiment/assignment.md index ac9b2a17e..955af73e8 100644 --- a/translations/he/6-NLP/3-Translation-Sentiment/assignment.md +++ b/translations/he/6-NLP/3-Translation-Sentiment/assignment.md @@ -1,12 +1,3 @@ - # רישיון פואטי ## הוראות diff --git a/translations/he/6-NLP/3-Translation-Sentiment/solution/Julia/README.md b/translations/he/6-NLP/3-Translation-Sentiment/solution/Julia/README.md index 94f23c76d..7b3eb5a09 100644 --- a/translations/he/6-NLP/3-Translation-Sentiment/solution/Julia/README.md +++ b/translations/he/6-NLP/3-Translation-Sentiment/solution/Julia/README.md @@ -1,12 +1,3 @@ - זהו מציין מקום זמני --- diff --git a/translations/he/6-NLP/3-Translation-Sentiment/solution/R/README.md b/translations/he/6-NLP/3-Translation-Sentiment/solution/R/README.md index 449532146..7b3eb5a09 100644 --- a/translations/he/6-NLP/3-Translation-Sentiment/solution/R/README.md +++ b/translations/he/6-NLP/3-Translation-Sentiment/solution/R/README.md @@ -1,12 +1,3 @@ - זהו מציין מקום זמני --- diff --git a/translations/he/6-NLP/4-Hotel-Reviews-1/README.md b/translations/he/6-NLP/4-Hotel-Reviews-1/README.md index ac3c6b712..4265c622c 100644 --- a/translations/he/6-NLP/4-Hotel-Reviews-1/README.md +++ b/translations/he/6-NLP/4-Hotel-Reviews-1/README.md @@ -1,12 +1,3 @@ - # ניתוח רגשות עם ביקורות על מלונות - עיבוד הנתונים בפרק זה תשתמשו בטכניקות שלמדתם בשיעורים הקודמים כדי לבצע ניתוח נתונים חקרני על מערך נתונים גדול. לאחר שתבינו היטב את השימושיות של העמודות השונות, תלמדו: diff --git a/translations/he/6-NLP/4-Hotel-Reviews-1/assignment.md b/translations/he/6-NLP/4-Hotel-Reviews-1/assignment.md index 398b6b5b6..116984444 100644 --- a/translations/he/6-NLP/4-Hotel-Reviews-1/assignment.md +++ b/translations/he/6-NLP/4-Hotel-Reviews-1/assignment.md @@ -1,12 +1,3 @@ - # NLTK ## הוראות diff --git a/translations/he/6-NLP/4-Hotel-Reviews-1/solution/Julia/README.md b/translations/he/6-NLP/4-Hotel-Reviews-1/solution/Julia/README.md index 46959a681..eaaaf93e2 100644 --- a/translations/he/6-NLP/4-Hotel-Reviews-1/solution/Julia/README.md +++ b/translations/he/6-NLP/4-Hotel-Reviews-1/solution/Julia/README.md @@ -1,12 +1,3 @@ - זהו מציין מקום זמני --- diff --git a/translations/he/6-NLP/4-Hotel-Reviews-1/solution/R/README.md b/translations/he/6-NLP/4-Hotel-Reviews-1/solution/R/README.md index caca43653..ceb2903c1 100644 --- a/translations/he/6-NLP/4-Hotel-Reviews-1/solution/R/README.md +++ b/translations/he/6-NLP/4-Hotel-Reviews-1/solution/R/README.md @@ -1,12 +1,3 @@ - זהו מציין מקום זמני --- diff --git a/translations/he/6-NLP/5-Hotel-Reviews-2/README.md b/translations/he/6-NLP/5-Hotel-Reviews-2/README.md index 3117f6c19..b71147de2 100644 --- a/translations/he/6-NLP/5-Hotel-Reviews-2/README.md +++ b/translations/he/6-NLP/5-Hotel-Reviews-2/README.md @@ -1,12 +1,3 @@ - # ניתוח רגשות עם ביקורות על מלונות עכשיו, לאחר שחקרת את מערך הנתונים לעומק, הגיע הזמן לסנן את העמודות ולהשתמש בטכניקות עיבוד שפה טבעית (NLP) על מערך הנתונים כדי לקבל תובנות חדשות על המלונות. diff --git a/translations/he/6-NLP/5-Hotel-Reviews-2/assignment.md b/translations/he/6-NLP/5-Hotel-Reviews-2/assignment.md index e534331aa..82ff46294 100644 --- a/translations/he/6-NLP/5-Hotel-Reviews-2/assignment.md +++ b/translations/he/6-NLP/5-Hotel-Reviews-2/assignment.md @@ -1,12 +1,3 @@ - # נסה מערך נתונים אחר ## הוראות diff --git a/translations/he/6-NLP/5-Hotel-Reviews-2/solution/Julia/README.md b/translations/he/6-NLP/5-Hotel-Reviews-2/solution/Julia/README.md index 730ccb13e..7b3eb5a09 100644 --- a/translations/he/6-NLP/5-Hotel-Reviews-2/solution/Julia/README.md +++ b/translations/he/6-NLP/5-Hotel-Reviews-2/solution/Julia/README.md @@ -1,12 +1,3 @@ - זהו מציין מקום זמני --- diff --git a/translations/he/6-NLP/5-Hotel-Reviews-2/solution/R/README.md b/translations/he/6-NLP/5-Hotel-Reviews-2/solution/R/README.md index 09f4b81a7..7b3eb5a09 100644 --- a/translations/he/6-NLP/5-Hotel-Reviews-2/solution/R/README.md +++ b/translations/he/6-NLP/5-Hotel-Reviews-2/solution/R/README.md @@ -1,12 +1,3 @@ - זהו מציין מקום זמני --- diff --git a/translations/he/6-NLP/README.md b/translations/he/6-NLP/README.md index 7f1db56c5..9939e452e 100644 --- a/translations/he/6-NLP/README.md +++ b/translations/he/6-NLP/README.md @@ -1,12 +1,3 @@ - # התחלת עבודה עם עיבוד שפה טבעית עיבוד שפה טבעית (NLP) הוא היכולת של תוכנת מחשב להבין שפה אנושית כפי שהיא מדוברת ונכתבת - מה שמכונה שפה טבעית. זהו מרכיב של בינה מלאכותית (AI). תחום ה-NLP קיים כבר יותר מ-50 שנה ויש לו שורשים בתחום הבלשנות. כל התחום מכוון לעזור למכונות להבין ולעבד את השפה האנושית. לאחר מכן ניתן להשתמש בזה לביצוע משימות כמו בדיקת איות או תרגום מכונה. יש לו מגוון יישומים בעולם האמיתי בתחומים רבים, כולל מחקר רפואי, מנועי חיפוש ומודיעין עסקי. diff --git a/translations/he/6-NLP/data/README.md b/translations/he/6-NLP/data/README.md index 01c939015..2d0cddb34 100644 --- a/translations/he/6-NLP/data/README.md +++ b/translations/he/6-NLP/data/README.md @@ -1,12 +1,3 @@ - הורד את נתוני ביקורת המלון לתיקייה זו. --- diff --git a/translations/he/7-TimeSeries/1-Introduction/README.md b/translations/he/7-TimeSeries/1-Introduction/README.md index e3e7929b7..83fe4d9a9 100644 --- a/translations/he/7-TimeSeries/1-Introduction/README.md +++ b/translations/he/7-TimeSeries/1-Introduction/README.md @@ -1,12 +1,3 @@ - # מבוא לחיזוי סדרות זמן ![סיכום סדרות זמן בסקצ'נוט](../../../../sketchnotes/ml-timeseries.png) diff --git a/translations/he/7-TimeSeries/1-Introduction/assignment.md b/translations/he/7-TimeSeries/1-Introduction/assignment.md index 1d7ded713..9bc1ddf51 100644 --- a/translations/he/7-TimeSeries/1-Introduction/assignment.md +++ b/translations/he/7-TimeSeries/1-Introduction/assignment.md @@ -1,12 +1,3 @@ - # הצגת סדרות זמן נוספות ## הוראות diff --git a/translations/he/7-TimeSeries/1-Introduction/solution/Julia/README.md b/translations/he/7-TimeSeries/1-Introduction/solution/Julia/README.md index 78d683081..9634ec916 100644 --- a/translations/he/7-TimeSeries/1-Introduction/solution/Julia/README.md +++ b/translations/he/7-TimeSeries/1-Introduction/solution/Julia/README.md @@ -1,12 +1,3 @@ - זהו מציין מקום זמני --- diff --git a/translations/he/7-TimeSeries/1-Introduction/solution/R/README.md b/translations/he/7-TimeSeries/1-Introduction/solution/R/README.md index 19ae11869..7b3eb5a09 100644 --- a/translations/he/7-TimeSeries/1-Introduction/solution/R/README.md +++ b/translations/he/7-TimeSeries/1-Introduction/solution/R/README.md @@ -1,12 +1,3 @@ - זהו מציין מקום זמני --- diff --git a/translations/he/7-TimeSeries/2-ARIMA/README.md b/translations/he/7-TimeSeries/2-ARIMA/README.md index 9b22591e7..bf3906baa 100644 --- a/translations/he/7-TimeSeries/2-ARIMA/README.md +++ b/translations/he/7-TimeSeries/2-ARIMA/README.md @@ -1,12 +1,3 @@ - # חיזוי סדרות זמן עם ARIMA בשיעור הקודם, למדתם מעט על חיזוי סדרות זמן וטעינת מערך נתונים שמציג את התנודות בעומס החשמלי לאורך תקופת זמן. diff --git a/translations/he/7-TimeSeries/2-ARIMA/assignment.md b/translations/he/7-TimeSeries/2-ARIMA/assignment.md index cd3ae7132..52e4269e6 100644 --- a/translations/he/7-TimeSeries/2-ARIMA/assignment.md +++ b/translations/he/7-TimeSeries/2-ARIMA/assignment.md @@ -1,12 +1,3 @@ - # מודל ARIMA חדש ## הוראות diff --git a/translations/he/7-TimeSeries/2-ARIMA/solution/Julia/README.md b/translations/he/7-TimeSeries/2-ARIMA/solution/Julia/README.md index 136caf890..da0ed164b 100644 --- a/translations/he/7-TimeSeries/2-ARIMA/solution/Julia/README.md +++ b/translations/he/7-TimeSeries/2-ARIMA/solution/Julia/README.md @@ -1,12 +1,3 @@ - זהו מציין מקום זמני --- diff --git a/translations/he/7-TimeSeries/2-ARIMA/solution/R/README.md b/translations/he/7-TimeSeries/2-ARIMA/solution/R/README.md index 4a30092dd..7b3eb5a09 100644 --- a/translations/he/7-TimeSeries/2-ARIMA/solution/R/README.md +++ b/translations/he/7-TimeSeries/2-ARIMA/solution/R/README.md @@ -1,12 +1,3 @@ - זהו מציין מקום זמני --- diff --git a/translations/he/7-TimeSeries/3-SVR/README.md b/translations/he/7-TimeSeries/3-SVR/README.md index e134637b4..60899c649 100644 --- a/translations/he/7-TimeSeries/3-SVR/README.md +++ b/translations/he/7-TimeSeries/3-SVR/README.md @@ -1,12 +1,3 @@ - # חיזוי סדרות זמן עם Support Vector Regressor בשיעור הקודם, למדת כיצד להשתמש במודל ARIMA כדי לבצע תחזיות של סדרות זמן. עכשיו תכיר את מודל Support Vector Regressor, שהוא מודל רגרסיה המשמש לחיזוי נתונים רציפים. diff --git a/translations/he/7-TimeSeries/3-SVR/assignment.md b/translations/he/7-TimeSeries/3-SVR/assignment.md index 4dd8aff35..d59e2e951 100644 --- a/translations/he/7-TimeSeries/3-SVR/assignment.md +++ b/translations/he/7-TimeSeries/3-SVR/assignment.md @@ -1,12 +1,3 @@ - # מודל SVR חדש ## הוראות [^1] diff --git a/translations/he/7-TimeSeries/README.md b/translations/he/7-TimeSeries/README.md index 85ecc72e3..3587ba0ee 100644 --- a/translations/he/7-TimeSeries/README.md +++ b/translations/he/7-TimeSeries/README.md @@ -1,12 +1,3 @@ - # מבוא לחיזוי סדרות זמן מהו חיזוי סדרות זמן? מדובר בניבוי אירועים עתידיים על ידי ניתוח מגמות מהעבר. diff --git a/translations/he/8-Reinforcement/1-QLearning/README.md b/translations/he/8-Reinforcement/1-QLearning/README.md index 720524935..eb7be92e2 100644 --- a/translations/he/8-Reinforcement/1-QLearning/README.md +++ b/translations/he/8-Reinforcement/1-QLearning/README.md @@ -1,12 +1,3 @@ - # מבוא ללמידת חיזוק ולמידת Q ![סיכום למידת חיזוק בלמידת מכונה בסקצ'נוט](../../../../sketchnotes/ml-reinforcement.png) diff --git a/translations/he/8-Reinforcement/1-QLearning/assignment.md b/translations/he/8-Reinforcement/1-QLearning/assignment.md index 72cdfb3aa..f1128858b 100644 --- a/translations/he/8-Reinforcement/1-QLearning/assignment.md +++ b/translations/he/8-Reinforcement/1-QLearning/assignment.md @@ -1,12 +1,3 @@ - # עולם מציאותי יותר במצב שלנו, פיטר הצליח לנוע כמעט בלי להתעייף או להרגיש רעב. בעולם מציאותי יותר, הוא צריך לשבת ולנוח מדי פעם, וגם להאכיל את עצמו. בואו נעשה את העולם שלנו מציאותי יותר, על ידי יישום הכללים הבאים: diff --git a/translations/he/8-Reinforcement/1-QLearning/solution/Julia/README.md b/translations/he/8-Reinforcement/1-QLearning/solution/Julia/README.md index 6dbd9f03b..bd412fa60 100644 --- a/translations/he/8-Reinforcement/1-QLearning/solution/Julia/README.md +++ b/translations/he/8-Reinforcement/1-QLearning/solution/Julia/README.md @@ -1,12 +1,3 @@ - זהו מציין מקום זמני --- diff --git a/translations/he/8-Reinforcement/1-QLearning/solution/R/README.md b/translations/he/8-Reinforcement/1-QLearning/solution/R/README.md index 8a7da6652..fbfb57919 100644 --- a/translations/he/8-Reinforcement/1-QLearning/solution/R/README.md +++ b/translations/he/8-Reinforcement/1-QLearning/solution/R/README.md @@ -1,12 +1,3 @@ - זהו מציין מקום זמני --- diff --git a/translations/he/8-Reinforcement/2-Gym/README.md b/translations/he/8-Reinforcement/2-Gym/README.md index 905c0b00f..751cfd48f 100644 --- a/translations/he/8-Reinforcement/2-Gym/README.md +++ b/translations/he/8-Reinforcement/2-Gym/README.md @@ -1,12 +1,3 @@ - ## דרישות מקדימות בשיעור הזה נשתמש בספרייה בשם **OpenAI Gym** כדי לדמות **סביבות** שונות. ניתן להריץ את הקוד של השיעור הזה באופן מקומי (למשל, מ-Visual Studio Code), ובמקרה כזה הסימולציה תיפתח בחלון חדש. כאשר מריצים את הקוד אונליין, ייתכן שתצטרכו לבצע כמה התאמות בקוד, כפי שמתואר [כאן](https://towardsdatascience.com/rendering-openai-gym-envs-on-binder-and-google-colab-536f99391cc7). diff --git a/translations/he/8-Reinforcement/2-Gym/assignment.md b/translations/he/8-Reinforcement/2-Gym/assignment.md index 5107bd891..ba2a39d9b 100644 --- a/translations/he/8-Reinforcement/2-Gym/assignment.md +++ b/translations/he/8-Reinforcement/2-Gym/assignment.md @@ -1,12 +1,3 @@ - # אימון מכונית הרים [OpenAI Gym](http://gym.openai.com) תוכנן כך שכל הסביבות מספקות את אותו API - כלומר, אותן שיטות `reset`, `step` ו-`render`, ואותן הפשטות של **מרחב הפעולה** ו**מרחב התצפית**. לכן, אמור להיות אפשרי להתאים את אותם אלגוריתמים של למידת חיזוק לסביבות שונות עם שינויים מינימליים בקוד. diff --git a/translations/he/8-Reinforcement/2-Gym/solution/Julia/README.md b/translations/he/8-Reinforcement/2-Gym/solution/Julia/README.md index b5c9cd50d..ee82f8bce 100644 --- a/translations/he/8-Reinforcement/2-Gym/solution/Julia/README.md +++ b/translations/he/8-Reinforcement/2-Gym/solution/Julia/README.md @@ -1,12 +1,3 @@ - זהו מציין מקום זמני --- diff --git a/translations/he/8-Reinforcement/2-Gym/solution/R/README.md b/translations/he/8-Reinforcement/2-Gym/solution/R/README.md index ca4868660..da0ed164b 100644 --- a/translations/he/8-Reinforcement/2-Gym/solution/R/README.md +++ b/translations/he/8-Reinforcement/2-Gym/solution/R/README.md @@ -1,12 +1,3 @@ - זהו מציין מקום זמני --- diff --git a/translations/he/8-Reinforcement/README.md b/translations/he/8-Reinforcement/README.md index ee3d8d33d..271c66ae4 100644 --- a/translations/he/8-Reinforcement/README.md +++ b/translations/he/8-Reinforcement/README.md @@ -1,12 +1,3 @@ - # מבוא ללמידת חיזוק למידת חיזוק, RL, נחשבת לאחת מהפרדיגמות הבסיסיות של למידת מכונה, לצד למידה מונחית ולמידה בלתי מונחית. RL עוסקת בקבלת החלטות: קבלת ההחלטות הנכונות או לפחות ללמוד מהן. diff --git a/translations/he/9-Real-World/1-Applications/README.md b/translations/he/9-Real-World/1-Applications/README.md index ce46a6dc8..d03d66602 100644 --- a/translations/he/9-Real-World/1-Applications/README.md +++ b/translations/he/9-Real-World/1-Applications/README.md @@ -1,12 +1,3 @@ - # פוסטסקריפט: למידת מכונה בעולם האמיתי ![סיכום של למידת מכונה בעולם האמיתי בסקצ'נוט](../../../../sketchnotes/ml-realworld.png) diff --git a/translations/he/9-Real-World/1-Applications/assignment.md b/translations/he/9-Real-World/1-Applications/assignment.md index 72325f044..447f184e2 100644 --- a/translations/he/9-Real-World/1-Applications/assignment.md +++ b/translations/he/9-Real-World/1-Applications/assignment.md @@ -1,12 +1,3 @@ - # חיפוש אוצרות בלמידת מכונה ## הוראות diff --git a/translations/he/9-Real-World/2-Debugging-ML-Models/README.md b/translations/he/9-Real-World/2-Debugging-ML-Models/README.md index a349077a0..b2fb55cec 100644 --- a/translations/he/9-Real-World/2-Debugging-ML-Models/README.md +++ b/translations/he/9-Real-World/2-Debugging-ML-Models/README.md @@ -1,12 +1,3 @@ - # פוסטסקריפט: איתור שגיאות במודלים של למידת מכונה באמצעות רכיבי לוח מחוונים של AI אחראי ## [שאלון לפני השיעור](https://ff-quizzes.netlify.app/en/ml/) diff --git a/translations/he/9-Real-World/2-Debugging-ML-Models/assignment.md b/translations/he/9-Real-World/2-Debugging-ML-Models/assignment.md index f3cf51a01..d78c5278e 100644 --- a/translations/he/9-Real-World/2-Debugging-ML-Models/assignment.md +++ b/translations/he/9-Real-World/2-Debugging-ML-Models/assignment.md @@ -1,12 +1,3 @@ - # חקור את לוח המחוונים של AI אחראי (RAI) ## הוראות diff --git a/translations/he/9-Real-World/README.md b/translations/he/9-Real-World/README.md index c664cb03f..31d267dbe 100644 --- a/translations/he/9-Real-World/README.md +++ b/translations/he/9-Real-World/README.md @@ -1,12 +1,3 @@ - # פוסטסקריפט: יישומים בעולם האמיתי של למידת מכונה קלאסית בחלק זה של התוכנית, תיחשפו לכמה יישומים בעולם האמיתי של למידת מכונה קלאסית. חיפשנו ברחבי האינטרנט מאמרים ומחקרים על יישומים שהשתמשו באסטרטגיות אלו, תוך הימנעות מרשתות נוירונים, למידה עמוקה ובינה מלאכותית ככל האפשר. תלמדו כיצד למידת מכונה משמשת במערכות עסקיות, יישומים אקולוגיים, פיננסים, אמנות ותרבות, ועוד. diff --git a/translations/he/AGENTS.md b/translations/he/AGENTS.md index 61afa61b4..5e5df91f8 100644 --- a/translations/he/AGENTS.md +++ b/translations/he/AGENTS.md @@ -1,12 +1,3 @@ - # AGENTS.md ## סקירת הפרויקט diff --git a/translations/he/CODE_OF_CONDUCT.md b/translations/he/CODE_OF_CONDUCT.md index 95a945862..316a56f1c 100644 --- a/translations/he/CODE_OF_CONDUCT.md +++ b/translations/he/CODE_OF_CONDUCT.md @@ -1,12 +1,3 @@ - # קוד ההתנהגות של קוד פתוח של מיקרוסופט הפרויקט הזה אימץ את [קוד ההתנהגות של קוד פתוח של מיקרוסופט](https://opensource.microsoft.com/codeofconduct/). diff --git a/translations/he/CONTRIBUTING.md b/translations/he/CONTRIBUTING.md index 6f1ba870d..a5f4c6ae6 100644 --- a/translations/he/CONTRIBUTING.md +++ b/translations/he/CONTRIBUTING.md @@ -1,12 +1,3 @@ - # תרומה הפרויקט הזה מקבל בברכה תרומות והצעות. רוב התרומות דורשות ממך להסכים להסכם רישיון תורם (CLA) שמצהיר שיש לך את הזכות, ואתה אכן מעניק לנו את הזכויות להשתמש בתרומתך. לפרטים נוספים, בקר בכתובת https://cla.microsoft.com. diff --git a/translations/he/README.md b/translations/he/README.md index d6b1811aa..b4468b00d 100644 --- a/translations/he/README.md +++ b/translations/he/README.md @@ -1,207 +1,223 @@ - -### 🌐 תמיכה ברב-לשוניות - -#### נתמך באמצעות פעולה של GitHub (אוטומטית ותמיד מעודכנת) +[![רישיון GitHub](https://img.shields.io/github/license/microsoft/ML-For-Beginners.svg)](https://github.com/microsoft/ML-For-Beginners/blob/master/LICENSE) +[![תורמים ב-GitHub](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/) +[![בעיות ב-GitHub](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/) +[![בקשות משיכה ב-GitHub](https://img.shields.io/github/issues-pr/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/pulls/) +[![ברוכים הבאים לבקשות משיכה](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com) + +[![עוקבים ב-GitHub](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/) +[![פורקים ב-GitHub](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/) +[![כוכבים ב-GitHub](https://img.shields.io/github/stars/microsoft/ML-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/ML-For-Beginners/stargazers/) + +### 🌐 תמיכה בריבוי שפות + +#### נתמך באמצעות GitHub Action (אוטומטי ותמיד מעודכן) + + +[ערבית](../ar/README.md) | [בנגלית](../bn/README.md) | [בולגרית](../bg/README.md) | [בורמזית (מיאנמר)](../my/README.md) | [סינית (מפושטת)](../zh-CN/README.md) | [סינית (מסורתית, הונג קונג)](../zh-HK/README.md) | [סינית (מסורתית, מקאו)](../zh-MO/README.md) | [סינית (מסורתית, טייוואן)](../zh-TW/README.md) | [קרואטית](../hr/README.md) | [צ'כית](../cs/README.md) | [דנית](../da/README.md) | [הולנדית](../nl/README.md) | [אסטונית](../et/README.md) | [פינית](../fi/README.md) | [צרפתית](../fr/README.md) | [גרמנית](../de/README.md) | [יוונית](../el/README.md) | [עברית](./README.md) | [הינדי](../hi/README.md) | [הונגרית](../hu/README.md) | [אינדונזית](../id/README.md) | [איטלקית](../it/README.md) | [יפנית](../ja/README.md) | [קנדה](../kn/README.md) | [קוריאנית](../ko/README.md) | [ליטאית](../lt/README.md) | [מלאית](../ms/README.md) | [מלאלאית](../ml/README.md) | [מרטהית](../mr/README.md) | [נפאלית](../ne/README.md) | [פידג'ין ניגרי](../pcm/README.md) | [נורווגית](../no/README.md) | [פרסית (פרסי)](../fa/README.md) | [פולנית](../pl/README.md) | [פורטוגזית (ברזיל)](../pt-BR/README.md) | [פורטוגזית (פורטוגל)](../pt-PT/README.md) | [פנג'אבי (גורמוכי)](../pa/README.md) | [רומנית](../ro/README.md) | [רוסית](../ru/README.md) | [סרבית (קירילית)](../sr/README.md) | [סלובקית](../sk/README.md) | [סלובנית](../sl/README.md) | [ספרדית](../es/README.md) | [סווהילית](../sw/README.md) | [שוודית](../sv/README.md) | [טגלוג (פיליפינית)](../tl/README.md) | [טמילית](../ta/README.md) | [טלוגו](../te/README.md) | [תאית](../th/README.md) | [טורקית](../tr/README.md) | [אוקראינית](../uk/README.md) | [אורדו](../ur/README.md) | [ויטנאמית](../vi/README.md) > **מעדיפים לשכפל מקומית?** -> מאגר זה כולל יותר מ-50 תרגומים בשפות שונות, מה שמגדיל משמעותית את גודל ההורדה. לשכפול ללא תרגומים, השתמשו בבחירת שכפול חלקית: +> מאגר זה כולל יותר מ-50 תרגומים בשפות שונות, מה שמגדיל משמעותית את גודל ההורדה. לשכפול ללא תרגומים, השתמשו ב-sparse checkout: > ```bash > git clone --filter=blob:none --sparse https://github.com/microsoft/ML-For-Beginners.git > cd ML-For-Beginners > git sparse-checkout set --no-cone '/*' '!translations' '!translated_images' > ``` > זה נותן לכם את כל מה שצריך כדי להשלים את הקורס במהירות הורדה גבוהה יותר. + #### הצטרפו לקהילה שלנו -יש לנו סדרת לימוד ב-Discord בנושא AI, למדו עוד והצטרפו אלינו ב-[Learn with AI Series](https://aka.ms/learnwithai/discord) בין התאריכים 18 - 30 בספטמבר 2025. תקבלו טיפים וטריקים לשימוש ב-GitHub Copilot עבור מדעי הנתונים. +[![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) + +יש לנו סדרת Discord ללמידה עם AI מתמשכת, למדו עוד והצטרפו אלינו ב-[סדרת למידה עם AI](https://aka.ms/learnwithai/discord) מ-18 עד 30 בספטמבר 2025. תקבלו טיפים וטריקים לשימוש ב-GitHub Copilot למדע הנתונים. + +![סדרת למידה עם AI](../../translated_images/he/3.9b58fd8d6c373c20.webp) # למידת מכונה למתחילים - תוכנית לימודים -> 🌍 סיירו בעולם תוך כדי חקר למידת מכונה דרך תרבויות עולמיות 🌍 +> 🌍 טוסו ברחבי העולם בעודנו חוקרים למידת מכונה דרך תרבויות העולם 🌍 -סגל Cloud Advocates ב-Microsoft שמח להציע תוכנית לימודים של 12 שבועות, 26 שיעורים, הכוללת הכל על **למידת מכונה**. בתוכנית זו תלמדו על מה שלעיתים נקרא **למידת מכונה קלאסית**, תוך שימוש בעיקר בספריית Scikit-learn והימנעות מלמידה עמוקה, הנלמדת בתוכנית שלנו ל-[AI למתחילים](https://aka.ms/ai4beginners). שילבו את השיעורים האלו עם תוכנית ['מדעי הנתונים למתחילים'](https://aka.ms/ds4beginners) שלנו גם כן! +Advocates ענן במיקרוסופט מתרגשים להציע תוכנית של 12 שבועות, 26 שיעורים בנושא **למידת מכונה**. בתוכנית זו תלמדו על מה שלפעמים נקרא **למידת מכונה קלאסית**, תוך שימוש בעיקר ב-Scikit-learn כמח bibliothèque והימנעות מלמידה עמוקה, אשר מכוסה בתוכנית ה-[AI למתחילים שלנו](https://aka.ms/ai4beginners). שלבו שיעורים אלה עם תוכנית ['מדע הנתונים למתחילים'](https://aka.ms/ds4beginners), גם כן! -סיירו איתנו בעולם כשאנו מיישמים את הטכניקות הקלאסיות האלה על נתונים מאזורים שונים של העולם. כל שיעור כולל מבחני לפני ואחרי השיעור, הוראות כתובות להשלמת השיעור, פתרון, משימה ועוד. הפדגוגיה מבוססת הפרויקטים שלנו מאפשרת לכם ללמוד תוך כדי בנייה, שיטה מוכחת ללמידה יעילה. +טוסו איתנו ברחבי העולם כשאנו מיישמים את הטכניקות הקלאסיות הללו על נתונים מאזורים רבים בעולם. כל שיעור כולל בחני ידע לפני ואחרי השיעור, הוראות כתובות להשלמת השיעור, פתרון, מטלה ועוד. הפדגוגיה המבוססת על פרויקטים שלנו מאפשרת לך ללמוד תוך כדי בנייה, שיטה מוכחת להטמעת מיומנויות חדשות. -**✍️ תודה רבה למחברים שלנו** ג'ן לופר, סטיבן הוואל, פרנצ'סקה לזרי, טומומי אימורה, קאסי ברוויו, דמיטרי סושניקוב, כריס נורינג, אנירבן מיהרג'י, אורנלה אלתוניאן, רות יקובו ואיימי בויד +**✍️ תודה רבה למחברינו** ג'ן לופר, סטיבן האוול, פרנצ'סקה לזרי, טומומי אימורה, קאסי ברוויו, דמיטרי סושניקוב, כריס נורינג, אנירבן מוכהארג'י, אורנלה אלטוניאן, רות יקובו ואיימי בויד -**🎨 תודה גם לאיורים** טומומי אימורה, דאסאני מדיפאלי וג'ן לופר +**🎨 תודה גם לאנשי האיור** טומומי אימורה, דסאני מדיפאלי וג'ן לופר -**🙏 תודה מיוחדת 🙏 למחברי, הסוקרים ותורמי התוכן שגרירי הסטודנטים של מייקרוסופט**, במיוחד רישיט דגלי, מוחמד סאקיב חאן איאן, רוהן ראג', אלכסנדרו פטרסקו, אבישק ג'ייסוואל, נאורין טבאסם, יואן סאמיולה וסניגדה אגרוואל +**🙏 תודה מיוחדת 🙏 למחברי שגרירי סטודנטים מיקרוסופט, לסוקרים ולתורמים בתוכן**, במיוחד רישיט דגלי, מוחמד סאקיב חאן אינאן, רוהאן רג', אלכסנדרו פטרסקו, אבישק ג'איסוול, נאורין טבסם, יואן סאמויאלה, וסניגדה אגרוואל -**🤩 תודה מיוחדת נוספת לגרירי הסטודנטים של מייקרוסופט אריק וונג'או, ג'סלין סונדי ווידושי גופטה על שיעורי R שלנו!** +**🤩 תודה נוספת לשגרירי הסטודנטים מיקרוסופט אריק ונדגאו, ג'סלין סונדהי וידושי גופטה על שיעורי ה-R שלנו!** # התחלה -עקבו אחרי השלבים האלה: -1. **שכפלו את המאגר**: לחצו על כפתור "Fork" בפינה הימנית העליונה של הדף. +עקבו אחרי השלבים הבאים: +1. **צרו פורק למאגר**: לחצו על כפתור "Fork" בפינה הימנית העליונה של דף זה. 2. **שכפלו את המאגר**: `git clone https://github.com/microsoft/ML-For-Beginners.git` -> [מצאו את כל המשאבים הנוספים לקורס זה באוסף Microsoft Learn שלנו](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum) +> [מצאו את כל המשאבים הנוספים עבור קורס זה באוסף Microsoft Learn שלנו](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum) + +> 🔧 **צריך עזרה?** בדקו את [מדריך פתרון הבעיות](TROUBLESHOOTING.md) שלנו לפתרונות לבעיות נפוצות בהתקנה, הגדרה והרצת שיעורים. -> 🔧 **זקוקים לעזרה?** בדקו את [מדריך פתרון הבעיות](TROUBLESHOOTING.md) שלנו לפתרונות לבעיות נפוצות בהתקנה, בהגדרה ובהפעלה של השיעורים. -**[סטודנטים](https://aka.ms/student-page)**, כדי להשתמש בתוכנית זו, שייכו את כל המאגר לחשבון GitHub שלכם והשלימו את התרגילים בעצמכם או בקבוצה: +**[סטודנטים](https://aka.ms/student-page)**, כדי להשתמש בתוכנית זו, צרו פורק מלא של המאגר לחשבון GitHub הפרטי שלכם והשלימו את התרגילים לבד או בקבוצה: -- התחילו במבחן פתיחת שיעור. +- התחילו עם מבחן חימום לפני ההרצאה. - קראו את ההרצאה והשלימו את הפעילויות, עצרו והרהרו בכל בדיקת ידע. -- נסו ליצור את הפרויקטים באמצעות הבנת השיעורים במקום רק להריץ את קוד הפתרון; עם זאת הקוד זמין בתיקיות `/solution` בכל שיעור ממוקד-פרויקט. -- עברו את מבחן הסיום של השיעור. +- נסו ליצור את הפרויקטים על ידי הבנת השיעורים יותר מאשר על ידי הרצת קוד הפתרון; עם זאת, הקוד זמין בתיקיות `/solution` בכל שיעור ממוקד-פרויקט. +- עברו מבחן לאחר ההרצאה. - השלימו את האתגר. - השלימו את המטלה. -- לאחר השלמת קבוצת שיעורים, בקרו ב-[לוח הדיונים](https://github.com/microsoft/ML-For-Beginners/discussions) ו"למדו בקול" על ידי מילוי טופס PAT מתאים. PAT הוא כלי הערכה להתקדמות שנותן לכם למקד את הלמידה. תוכלו גם להגיב ל-PATים אחרים כדי ללמוד יחד. +- לאחר השלמת קבוצת שיעורים, בקרו ב-[לוח הדיון](https://github.com/microsoft/ML-For-Beginners/discussions) ו"למדו בקול" על ידי מילוי טופס PAT מתאים. 'PAT' הוא כלי הערכת התקדמות שהוא טופס שאתם ממלאים להעמקת הלמידה. ניתן גם להגיב ל-PATים אחרים כדי ללמוד יחד. -> ללמידה נוספת, מומלץ לעקוב אחר מודולות ונתיבי למידה של [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott). +> ללימוד נוסף, אנו ממליצים לעקוב אחרי מודולים ודרכי למידה ב-[Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott). **מורים**, כללנו [הצעות](for-teachers.md) כיצד להשתמש בתוכנית זו. --- -## סרטוני הסבר +## סרטוני הסברים -חלק מהשיעורים זמינים כסרטונים קצרים. ניתן למצוא את כולם בקו ישר בתוך השיעורים, או ברשימת ההשמעה ML for Beginners בערוץ היוטיוב של מפתחי מיקרוסופט: +חלק מהשיעורים זמינים כסרטונים קצרים. ניתן למצוא את כולם בתוך השיעורים או ברשימת ההשמעה [ML for Beginners בערוץ Microsoft Developer ב-YouTube](https://aka.ms/ml-beginners-videos) על ידי לחיצה על התמונה למטה. + +[![באנר ML למתחילים](../../translated_images/he/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos) --- ## הכירו את הצוות -> 🎥 לחצו על התמונה למעלה לסרטון על הפרויקט ועל היוצרים! +[![סרטון קידום](../../images/ml.gif)](https://youtu.be/Tj1XWrDSYJU) + +**גיף מאת** [Mohit Jaisal](https://linkedin.com/in/mohitjaisal) + +> 🎥 לחצו על התמונה למעלה לסרטון על הפרויקט והאנשים שיצרו אותו! --- ## פדגוגיה -בחרנו שני עקרונות פדגוגיים בבניית תוכנית הלימודים הזו: להבטיח שהיא פרקטית **מבוססת פרויקטים** וכוללת **מבחנים תכופים**. בנוסף, לתוכנית יש **נושא** משותף כדי להעניק לה עקביות. +בחרנו שני עקרונות פדגוגיים בעת בניית תוכנית הלימודים הזו: להבטיח שהיא מבוססת על פרויקטים מעשיים וכוללת מבחנים תכופים. בנוסף, לתוכנית זו יש **נושא משותף** כדי להקנות לה קוהרנטיות. -בהבטחת התאמת התוכן לפרויקטים, התהליך נעשה מרתק יותר עבור הסטודנטים ושימור המושגים יוגבר. בנוסף, מבחן בעל סיכון נמוך לפני הכיתה מגדיר את כוונת הלמידה של הסטודנט, בעוד שמבחן לאחר הלימוד מבטיח שמירת ידע נוספת. תוכנית זו תוכננה להיות גמישה ומהנה וניתן לקחת אותה בשלמות או בחלקים. הפרויקטים מתחילים קטנים והופכים למורכבים יותר לקראת סוף מחזור של 12 השבועות. לתוכנית זו יש גם נספח על יישומים אמיתיים של למידת מכונה, שאפשר להשתמש בהם כהערכה נוספת או כבסיס לדיון. +על ידי התאמת התוכן לפרויקטים, התהליך הופך למרתק יותר עבור הסטודנטים ותחזוקת המושגים תשתפר. בנוסף, מבחן ללא סיכון לפני השיעור מגדיר את כוונת הסטודנט ללמוד נושא, בעוד שמבחן נוסף לאחר השיעור מוודא שמירת ידע נוספת. תוכנית הלימודים הזו עוצבה להיות גמישה ומהנה וניתן לעשותה במלואה או בחלקה. הפרויקטים מתחילים קטנים והופכים מורכבים יותר לקראת סוף מחזור 12 השבועות. תוכנית זו כוללת גם נספח על יישומים בעולם האמיתי של למידת מכונה, שניתן להשתמש בו כנקודות נוספות או כבסיס לדיון. -> מצאו את [קוד ההתנהגות](CODE_OF_CONDUCT.md), [הנחיות לתרומה](CONTRIBUTING.md), [תרגום](TRANSLATIONS.md) ו-[מדריך פתרון בעיות](TROUBLESHOOTING.md). נשמח למשוב בונה שלכם! +> מצאו את [קוד ההתנהגות שלנו](CODE_OF_CONDUCT.md), [הנחיות לתרומה](CONTRIBUTING.md), [תרגום](TRANSLATIONS.md) ו-[פתרון בעיות](TROUBLESHOOTING.md). נשמח לקבל את המשוב הבונה שלכם! ## כל שיעור כולל -- איור סקיצי אופציונלי -- סרטון משלים אופציונלי -- סרטון הסבר (חלק מהשיעורים בלבד) -- [מבחן חימום לפני השיעור](https://ff-quizzes.netlify.app/en/ml/) +- שרטוט אופציונלי +- וידאו משלים אופציונלי +- סרטון הסבר (רק בחלק מהשיעורים) +- [מבחן חימום לפני ההרצאה](https://ff-quizzes.netlify.app/en/ml/) - שיעור כתוב -- לשיעורי פרויקט: מדריכים שלב-אחר-שלב לבניית הפרויקט +- בשיעורים מבוססי פרויקטים, מדריכים שלב-אחר-שלב לבניית הפרויקט - בדיקות ידע - אתגר -- קריאה נוספת +- קריאה משלימה - מטלה -- [מבחן סיום השיעור](https://ff-quizzes.netlify.app/en/ml/) - -> **הערה לגבי שפות**: רוב השיעורים כתובים בפייתון, אך רבים מהם זמינים גם ב-R. כדי להשלים שיעור ב-R, עברו לתיקיית `/solution` וחפשו שיעורי R. הם כוללים סיומת .rmd המייצגת קובץ **R Markdown** שניתן להגדיר בפשטות כהטמעת `בלוקים של קוד` (של R או שפות אחרות) ו`כותרת YAML` (שמנחה כיצד לעצב פלטים כגון PDF) במסמך `Markdown`. לפיכך, זה מסגרת יוצרת דוגמה עבור מדעי הנתונים כי היא מאפשרת לשלב את הקוד שלכם, הפלט, והמחשבות על ידי כתיבתן ב-Markdown. יתרה מזאת, מסמכי R Markdown יכולים להיות מיוצאים לפורמטים כמו PDF, HTML או Word. -> **הערה לגבי מבחנים**: כל המבחנים נמצאים בתיקיית [Quiz App](../../quiz-app), הכוללת סך של 52 מבחנים, כל אחד עם שלוש שאלות. הם מקושרים מתוך השיעורים, אך אפליקציית המבחנים יכולה לפעול מקומית; עקבו אחר ההוראות בתיקיית `quiz-app` לארח מקומית או לפרוס ב-Azure. - -| מספר שיעור | נושא | קיבוץ שיעור | מטרות הלמידה | שיעור מקושר | מחבר | -| :--------: | :-----------------------------------------------------------: | :---------------------------------------------: | ----------------------------------------------------------------------------------------------------------------------- | :----------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------: | -| 01 | מבוא ללמידת מכונה | [Introduction](1-Introduction/README.md) | למדו את ההבנות הבסיסיות מאחורי למידת מכונה | [שיעור](1-Introduction/1-intro-to-ML/README.md) | מוחמד | -| 02 | ההיסטוריה של למידת מכונה | [Introduction](1-Introduction/README.md) | למדו את ההיסטוריה שמאחורי התחום | [שיעור](1-Introduction/2-history-of-ML/README.md) | ג'ן ואיימי | -| 03 | הוגנות ולמידת מכונה | [Introduction](1-Introduction/README.md) | מהם הסוגיות הפילוסופיות החשובות סביב הוגנות שסטודנטים צריכים לקחת בחשבון כשבונים ומיישמים מודלים של למידת מכונה? | [שיעור](1-Introduction/3-fairness/README.md) | טומומי | -| 04 | טכניקות של למידת מכונה | [Introduction](1-Introduction/README.md) | אילו טכניקות משתמשים בהם חוקרי למידת מכונה לבניית מודלים? | [שיעור](1-Introduction/4-techniques-of-ML/README.md) | כריס וג'ן | -| 05 | מבוא לרגרסיה | [Regression](2-Regression/README.md) | התחילו עם פייתון ו-Scikit-learn למודלי רגרסיה | [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html) | ג'ן • אריק וואנג'או | -| 06 | מחירי דלועים בצפון אמריקה 🎃 | [Regression](2-Regression/README.md) | חזות ולטש נתונים כהכנה ללמידת מכונה | [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html) | ג'ן • אריק ואנג'או | -| 07 | מחירי דלועים בצפון אמריקה 🎃 | [Regression](2-Regression/README.md) | בניית מודלי רגרסיה ליניארית ופולינומית | [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html) | ג'ן ודמיטרי • אריק ואנג'או | -| 08 | מחירי דלועים בצפון אמריקה 🎃 | [Regression](2-Regression/README.md) | בניית מודל רגרסיה לוגיסטית | [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html) | ג'ן • אריק ואנג'או | -| 09 | אפליקציית ווב 🔌 | [Web App](3-Web-App/README.md) | בניית אפליקציית ווב לשימוש במודל שסיימתם לאמן | [Python](3-Web-App/1-Web-App/README.md) | ג'ן | -| 10 | מבוא לסיווג | [Classification](4-Classification/README.md) | ניקוי, הכנה והצגת הנתונים שלכם; מבוא לסיווג | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html) | ג'ן וקסי • אריק ואנג'או | -| 11 | מטעמים אסייתיים והודים טעימים 🍜 | [Classification](4-Classification/README.md) | מבוא לממיינים | [Python](4-Classification/2-Classifiers-1/README.md) • [R](../../4-Classification/2-Classifiers-1/solution/R/lesson_11.html) | ג'ן וקסי • אריק ואנג'או | -| 12 | מטעמים אסייתיים והודים טעימים 🍜 | [Classification](4-Classification/README.md) | עוד ממיינים | [Python](4-Classification/3-Classifiers-2/README.md) • [R](../../4-Classification/3-Classifiers-2/solution/R/lesson_12.html) | ג'ן וקסי • אריק ואנג'או | -| 13 | מטעמים אסייתיים והודים טעימים 🍜 | [Classification](4-Classification/README.md) | בניית אפליקציית ווב להמלצה באמצעות המודל שלכם | [Python](4-Classification/4-Applied/README.md) | ג'ן | -| 14 | מבוא לאשכולות | [Clustering](5-Clustering/README.md) | ניקוי, הכנה והצגת הנתונים שלכם; מבוא לאשכולות | [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html) | ג'ן • אריק ואנג'או | -| 15 | חקר הטעמים המוזיקליים של ניגריה 🎧 | [Clustering](5-Clustering/README.md) | חקרו את שיטת אשכולות K-Means | [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html) | ג'ן • אריק ואנג'או | -| 16 | מבוא לעיבוד שפה טבעית ☕️ | [Natural language processing](6-NLP/README.md) | למדו את הבסיס של עיבוד שפה טבעית דרך בניית בוט פשוט | [Python](6-NLP/1-Introduction-to-NLP/README.md) | סטיבן | -| 17 | משימות נפוצות ב-NLP ☕️ | [Natural language processing](6-NLP/README.md) | העמיקו את הידע שלכם ב-NLP על ידי הבנת המשימות השונות הנדרשות כשמתמודדים עם מבני שפה | [Python](6-NLP/2-Tasks/README.md) | סטיבן | -| 18 | תרגום וניתוח רגשות ♥️ | [Natural language processing](6-NLP/README.md) | תרגום וניתוח רגשות עם ג'יין אוסטן | [Python](6-NLP/3-Translation-Sentiment/README.md) | סטיבן | -| 19 | בתי מלון רומנטיים באירופה ♥️ | [Natural language processing](6-NLP/README.md) | ניתוח רגשות עם ביקורות על בתי מלון 1 | [Python](6-NLP/4-Hotel-Reviews-1/README.md) | סטיבן | -| 20 | בתי מלון רומנטיים באירופה ♥️ | [Natural language processing](6-NLP/README.md) | ניתוח רגשות עם ביקורות על בתי מלון 2 | [Python](6-NLP/5-Hotel-Reviews-2/README.md) | סטיבן | -| 21 | מבוא לחיזוי סדרות זמן | [Time series](7-TimeSeries/README.md) | מבוא לחיזוי סדרות זמן | [Python](7-TimeSeries/1-Introduction/README.md) | פרנצ'סקה | -| 22 | ⚡️ שימוש חשמל עולמי ⚡️ - חיזוי סדרות זמן עם ARIMA | [Time series](7-TimeSeries/README.md) | חיזוי סדרות זמן עם ARIMA | [Python](7-TimeSeries/2-ARIMA/README.md) | פרנצ'סקה | -| 23 | ⚡️ שימוש חשמל עולמי ⚡️ - חיזוי סדרות זמן עם SVR | [Time series](7-TimeSeries/README.md) | חיזוי סדרות זמן עם מכונת וקטור תמיכה (Support Vector Regressor) | [Python](7-TimeSeries/3-SVR/README.md) | אנירבן | -| 24 | מבוא ללמידה מחוזקת | [Reinforcement learning](8-Reinforcement/README.md) | מבוא ללמידה מחוזקת עם Q-Learning | [Python](8-Reinforcement/1-QLearning/README.md) | דמיטרי | -| 25 | עזרו לפיטר להימנע מהזאב! 🐺 | [Reinforcement learning](8-Reinforcement/README.md) | למידת תגמול ב-Gym | [Python](8-Reinforcement/2-Gym/README.md) | דמיטרי | -| פוסטסקריפט | תרחישים ויישומים של למידת מכונה בעולם האמיתי | [ML in the Wild](9-Real-World/README.md) | יישומים מעניינים וחושפים של למידת מכונה קלאסית | [שיעור](9-Real-World/1-Applications/README.md) | צוות | -| פוסטסקריפט | איתור תקלות במודלים של למידת מכונה באמצעות לוח בקרה RAI | [ML in the Wild](9-Real-World/README.md) | איתור תקלות במודלים של למידת מכונה באמצעות רכיבי לוח הבקרה האחריותית | [שיעור](9-Real-World/2-Debugging-ML-Models/README.md) | רות יקובו | - -> [מצאו את כל המשאבים הנוספים עבור הקורס הזה באוסף Microsoft Learn שלנו](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum) +- [מבחן לאחר ההרצאה](https://ff-quizzes.netlify.app/en/ml/) + +> **הערה על שפות**: שיעורים אלה כתובים בעיקר בפייתון, אך רבים זמינים גם ב-R. כדי להשלים שיעור R, כנסו לתיקיית `/solution` וחפשו שיעורי R. הם כוללים סיומת .rmd המייצגת קובץ **R Markdown** שמהווה במובן פשוט שילוב של `חלקי קוד` (של R או שפות אחרות) ו-`כותרת YAML` (המנחה כיצד לעצב פלטים כמו PDF) במסמך `Markdown`. ככזה, הוא משמש כמסגרת מחברת דוגמה למדעי המחשב כי הוא מאפשר לשלב את הקוד שלך, הפלט שלו, והמחשבות שלך על ידי כתיבתן ב-Markdown. בנוסף, ניתן להמיר מסמכי R Markdown לפורמטים שונים כגון PDF, HTML או Word. +> **הערה לגבי מבחנים**: כל המבחנים נמצאים בתיקיית [Quiz App folder](../../quiz-app), הכוללת בסך הכל 52 מבחנים עם שלוש שאלות כל אחד. הם מקושרים מתוך השיעורים אך ניתן להריץ את אפליקציית המבחנים מקומית; עקבו אחרי ההוראות שבתיקיית `quiz-app` כדי לארח או לפרוס מקומית או על Azure. + +| מספר השיעור | נושא | קיבוץ שיעורים | מטרות הלמידה | שיעור מקושר | מחבר | +| :-----------: | :------------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------: | +| 01 | מבוא ללמידת מכונה | [מבוא](1-Introduction/README.md) | למדו את המושגים הבסיסיים שמאחורי למידת מכונה | [שיעור](1-Introduction/1-intro-to-ML/README.md) | מוחמד | +| 02 | היסטוריית למידת מכונה | [מבוא](1-Introduction/README.md) | למדו את ההיסטוריה שעומדת בבסיס התחום | [שיעור](1-Introduction/2-history-of-ML/README.md) | ג'ן ואיימי | +| 03 | הוגנות ולמידת מכונה | [מבוא](1-Introduction/README.md) | מהם הנושאים הפילוסופיים החשובים סביב הוגנות שסטודנטים צריכים לשקול בעת בניה ויישום של מודלים ללמידת מכונה? | [שיעור](1-Introduction/3-fairness/README.md) | טומומי | +| 04 | טכניקות ללמידת מכונה | [מבוא](1-Introduction/README.md) | אילו טכניקות חוקרי למידת מכונה משתמשים כדי לבנות מודלים? | [שיעור](1-Introduction/4-techniques-of-ML/README.md) | כריס וג'ן | +| 05 | מבוא לרגרסיה | [רגרסיה](2-Regression/README.md) | התחילו עם Python ו-Scikit-learn למודלי רגרסיה | [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html) | ג'ן • אריק וanjau | +| 06 | מחירי דלעות בצפון אמריקה 🎃 | [רגרסיה](2-Regression/README.md) | ראו וויזואלית ונקו נתונים כהכנה ללמידת מכונה | [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html) | ג'ן • אריק וanjau | +| 07 | מחירי דלעות בצפון אמריקה 🎃 | [רגרסיה](2-Regression/README.md) | בנו מודלים של רגרסיה לינארית ופולינומית | [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html) | ג'ן ודמיטרי • אריק וanjau | +| 08 | מחירי דלעות בצפון אמריקה 🎃 | [רגרסיה](2-Regression/README.md) | בנו מודל רגרסיה לוגיסטית | [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html) | ג'ן • אריק וanjau | +| 09 | אפליקציית ווב 🔌 | [אפליקציית ווב](3-Web-App/README.md) | בנו אפליקציית ווב לשימוש במודל שאומן | [Python](3-Web-App/1-Web-App/README.md) | ג'ן | +| 10 | מבוא לסיווג | [סיווג](4-Classification/README.md) | נקה, הכין, והצג את הנתונים שלך; מבוא לסיווג | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html) | ג'ן וקסי • אריק וanjau | +| 11 | מטבחים אסייתיים והודים טעימים 🍜 | [סיווג](4-Classification/README.md) | מבוא לממיינים | [Python](4-Classification/2-Classifiers-1/README.md) • [R](../../4-Classification/2-Classifiers-1/solution/R/lesson_11.html) | ג'ן וקסי • אריק וanjau | +| 12 | מטבחים אסייתיים והודים טעימים 🍜 | [סיווג](4-Classification/README.md) | עוד ממיינים | [Python](4-Classification/3-Classifiers-2/README.md) • [R](../../4-Classification/3-Classifiers-2/solution/R/lesson_12.html) | ג'ן וקסי • אריק וanjau | +| 13 | מטבחים אסייתיים והודים טעימים 🍜 | [סיווג](4-Classification/README.md) | בנו אפליקציית ווב להמלצות תוך שימוש במודל שלכם | [Python](4-Classification/4-Applied/README.md) | ג'ן | +| 14 | מבוא לקיבוץ | [קיבוץ](5-Clustering/README.md) | נקה, הכין, והצג את הנתונים שלך; מבוא לקיבוץ | [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html) | ג'ן • אריק וanjau | +| 15 | חקר טעמי מוזיקה ניגרית 🎧 | [קיבוץ](5-Clustering/README.md) | חקור את שיטת קיבוץ K-Means | [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html) | ג'ן • אריק וanjau | +| 16 | מבוא לעיבוד שפה טבעית ☕️ | [עיבוד שפה טבעית](6-NLP/README.md) | למדו את היסודות על NLP על ידי בניית בוט פשוט | [Python](6-NLP/1-Introduction-to-NLP/README.md) | סטיבן | +| 17 | משימות NLP נפוצות ☕️ | [עיבוד שפה טבעית](6-NLP/README.md) | העמיקו את הידע ב-NLP על ידי הבנת משימות נפוצות הנדרשות לעבודה עם מבני שפה | [Python](6-NLP/2-Tasks/README.md) | סטיבן | +| 18 | תרגום וניתוח סנטימנט ♥️ | [עיבוד שפה טבעית](6-NLP/README.md) | תרגום וניתוח סנטימנט עם ג'יין אוסטן | [Python](6-NLP/3-Translation-Sentiment/README.md) | סטיבן | +| 19 | בתי מלון רומנטיים באירופה ♥️ | [עיבוד שפה טבעית](6-NLP/README.md) | ניתוח סנטימנט עם ביקורות על בתי מלון 1 | [Python](6-NLP/4-Hotel-Reviews-1/README.md) | סטיבן | +| 20 | בתי מלון רומנטיים באירופה ♥️ | [עיבוד שפה טבעית](6-NLP/README.md) | ניתוח סנטימנט עם ביקורות על בתי מלון 2 | [Python](6-NLP/5-Hotel-Reviews-2/README.md) | סטיבן | +| 21 | מבוא לחיזוי סדרות זמן | [סדרות זמן](7-TimeSeries/README.md) | מבוא לחיזוי סדרות זמן | [Python](7-TimeSeries/1-Introduction/README.md) | פראנססקה | +| 22 | ⚡️ שימוש חשמל עולמי ⚡️ - חיזוי סדרות זמן עם ARIMA | [סדרות זמן](7-TimeSeries/README.md) | חיזוי סדרות זמן עם ARIMA | [Python](7-TimeSeries/2-ARIMA/README.md) | פראנססקה | +| 23 | ⚡️ שימוש חשמל עולמי ⚡️ - חיזוי סדרות זמן עם SVR | [סדרות זמן](7-TimeSeries/README.md) | חיזוי סדרות זמן עם רגראסור וקטור תמיכה | [Python](7-TimeSeries/3-SVR/README.md) | אנירבן | +| 24 | מבוא ללמידת חיזוק | [למידת חיזוק](8-Reinforcement/README.md) | מבוא ללמידת חיזוק עם Q-Learning | [Python](8-Reinforcement/1-QLearning/README.md) | דמיטרי | +| 25 | עזרו לפיטר להימנע מהזאב! 🐺 | [למידת חיזוק](8-Reinforcement/README.md) | למידת חיזוק עם Gym | [Python](8-Reinforcement/2-Gym/README.md) | דמיטרי | +| נספח | תרחישים ויישומים של למידת מכונה בעולם האמיתי | [למידת מכונה בשטח](9-Real-World/README.md) | יישומים מעניינים וחושפניים של למידת מכונה קלאסית | [שיעור](9-Real-World/1-Applications/README.md) | צוות | +| נספח | איתור תקלות במודלים של למידת מכונה עם לוח בקרה של RAI | [למידת מכונה בשטח](9-Real-World/README.md) | איתור תקלות במודלים של למידת מכונה באמצעות רכיבי לוח הבקרה של Responsible AI | [שיעור](9-Real-World/2-Debugging-ML-Models/README.md) | רות יקובו | + +> [מצאו את כל המשאבים הנוספים לקורס זה באוסף Microsoft Learn שלנו](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum) ## גישה לא מקוונת -אתם יכולים להפעיל תיעוד זה ללא חיבור אינטרנט על ידי שימוש ב-[Docsify](https://docsify.js.org/#/). עשו Fork לרפוזיטורי הזה, [התקינו Docsify](https://docsify.js.org/#/quickstart) במחשב המקומי שלכם, ואז בתיקיית השורש של הרפוזיטורי כתבו `docsify serve`. האתר יהיה זמין בכתובת localhost בפורט 3000: `localhost:3000`. +ניתן להפעיל תיעוד זה לא מקוון באמצעות [Docsify](https://docsify.js.org/#/). עשו פורק למאגר זה, [התקינו Docsify](https://docsify.js.org/#/quickstart) במחשב המקומי שלכם, ואז בתיקיית השורש של המאגר, הקלידו `docsify serve`. האתר יופעל על פורט 3000 בכתובת localhost: `localhost:3000`. ## קבצי PDF -מצאו קובץ PDF של תוכנית הלימודים עם קישורים [כאן](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf). +מצאו קובץ pdf של תוכנית הלימודים עם קישורים [כאן](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf). -## 🎒 קורסים נוספים +## 🎒 קורסים נוספים -הצוות שלנו מייצר קורסים נוספים! בדקו: +צוותנו מייצר קורסים נוספים! בדקו: ### LangChain -[![LangChain4j for Beginners](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners) -[![LangChain.js for Beginners](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin) +[![LangChain4j למתחילים](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners) +[![LangChain.js למתחילים](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin) --- -### Azure / Edge / MCP / Agents -[![AZD for Beginners](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst) -[![Edge AI for Beginners](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst) -[![MCP for Beginners](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst) -[![AI Agents for Beginners](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst) +### Azure / Edge / MCP / סוכנים +[![AZD למתחילים](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst) +[![Edge AI למתחילים](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst) +[![MCP למתחילים](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst) +[![סוכני AI למתחילים](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst) --- -### Generative AI Series -[![בינה מלאכותית יוצרת למתחילים](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst) -[![בינה מלאכותית יוצרת (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst) -[![בינה מלאכותית יוצרת (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst) -[![בינה מלאכותית יוצרת (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst) +### סדרת AI יצירתית +[![אינטליגנציה מלאכותית יוצרת למתחילים](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst) +[![אינטליגנציה מלאכותית יוצרת (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst) +[![אינטליגנציה מלאכותית יוצרת (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst) +[![אינטליגנציה מלאכותית יוצרת (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst) --- -### למידה מרכזית +### למידה בסיסית [![למידת מכונה למתחילים](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst) [![מדעי הנתונים למתחילים](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst) -[![בינה מלאכותית למתחילים](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst) -[![סייברסקיוריטי למתחילים](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung) +[![אינטליגנציה מלאכותית למתחילים](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst) +[![אבטחת סייבר למתחילים](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung) [![פיתוח ווב למתחילים](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst) -[![אינטרנט של הדברים למתחילים](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst) -[![פיתוח מציאות מורחבת למתחילים](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst) +[![אינטרנט של חפצים למתחילים](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst) +[![פיתוח XR למתחילים](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst) --- ### סדרת Copilot -[![Copilot לתכנות במשותף עם AI](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) +[![Copilot לתכנות בשותפות עם AI](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) [![Copilot ל-C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst) -[![הרפתקת Copilot](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst) +[![הרפתקאות Copilot](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst) -## קבלת עזרה +## לקבלת עזרה -אם אתה נתקע או יש לך שאלות לגבי בניית אפליקציות בינה מלאכותית. הצטרף ללומדים אחרים ומפתחים מנוסים לדיונים על MCP. זו קהילה תומכת שבה שאלות מתקבלות בברכה והידע משותף בשפע. +אם אתה נתקע או יש לך שאלות בנוגע לבניית אפליקציות AI, הצטרף ללומדים אחרים ולמפתחים מנוסים בדיונים על MCP. זו קהילה תומכת שבה שאלות מתקבלות בברכה והידע משותף בחופשיות. [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -אם יש לך משוב על מוצר או שגיאות במהלך הבנייה בקר ב: +אם יש לך משוב על מוצר או שגיאות במהלך הבנייה, בקר: -[![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) +[![פורום מפתחים Microsoft Foundry](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) --- -**הנחיה משפטית**: -מסמך זה תורגם באמצעות שירות תרגום בינה מלאכותית [Co-op Translator](https://github.com/Azure/co-op-translator). למרות שאנו שואפים לדיוק, יש להבין כי תרגומים אוטומטיים עלולים להכיל שגיאות או אי דיוקים. המסמך המקורי בשפתו המקורית יש להיחשב כמקור הסמכותי. למידע קריטי מומלץ להשתמש בתרגום מקצועי בידי אדם. אנו לא נישא באחריות לכל אי הבנה או פרשנות שגויה הנובעים משימוש בתרגום זה. +**כתב ויתור**: +מסמך זה תורגם באמצעות שירות תרגום מבוסס בינה מלאכותית [Co-op Translator](https://github.com/Azure/co-op-translator). למרות שאנו שואפים לדייק, אנא שימו לב כי תרגומים אוטומטיים עלולים להכיל שגיאות או אי-דיוקים. המסמך המקורי בשפת המקור שלו חייב להיחשב כמקור סמכותי. עבור מידע קריטי מומלץ להשתמש בתרגום מקצועי אנושי. אנו לא אחראים לכל אי-הבנה או פרשנות שגויה הנובעת משימוש בתרגום זה. \ No newline at end of file diff --git a/translations/he/SECURITY.md b/translations/he/SECURITY.md index 558a98605..53ce28baa 100644 --- a/translations/he/SECURITY.md +++ b/translations/he/SECURITY.md @@ -1,12 +1,3 @@ - ## אבטחה מיקרוסופט מתייחסת ברצינות לאבטחת מוצרי התוכנה והשירותים שלה, כולל כל מאגרי הקוד המקוריים המנוהלים דרך הארגונים שלנו ב-GitHub, הכוללים [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet), [Xamarin](https://github.com/xamarin), ו-[ארגוני GitHub שלנו](https://opensource.microsoft.com/). diff --git a/translations/he/SUPPORT.md b/translations/he/SUPPORT.md index ee97b9a25..1bd38408a 100644 --- a/translations/he/SUPPORT.md +++ b/translations/he/SUPPORT.md @@ -1,12 +1,3 @@ - # תמיכה ## כיצד להגיש בעיות ולקבל עזרה diff --git a/translations/he/TROUBLESHOOTING.md b/translations/he/TROUBLESHOOTING.md index b596b2453..294b53b6b 100644 --- a/translations/he/TROUBLESHOOTING.md +++ b/translations/he/TROUBLESHOOTING.md @@ -1,12 +1,3 @@ - # מדריך לפתרון תקלות מדריך זה יעזור לכם לפתור בעיות נפוצות בעת עבודה עם תכנית הלימודים של "למידת מכונה למתחילים". אם לא מצאתם פתרון כאן, אנא בדקו את [דיוני הדיסקורד](https://aka.ms/foundry/discord) או [פתחו בעיה](https://github.com/microsoft/ML-For-Beginners/issues). diff --git a/translations/he/docs/_sidebar.md b/translations/he/docs/_sidebar.md index 7ba11f99e..db39bcaad 100644 --- a/translations/he/docs/_sidebar.md +++ b/translations/he/docs/_sidebar.md @@ -1,12 +1,3 @@ - - מבוא - [מבוא ללמידת מכונה](../1-Introduction/1-intro-to-ML/README.md) - [היסטוריה של למידת מכונה](../1-Introduction/2-history-of-ML/README.md) diff --git a/translations/he/for-teachers.md b/translations/he/for-teachers.md index ddf98743f..f36cd00a2 100644 --- a/translations/he/for-teachers.md +++ b/translations/he/for-teachers.md @@ -1,12 +1,3 @@ - ## למורים האם תרצו להשתמש בתוכנית הלימודים הזו בכיתה שלכם? אתם מוזמנים לעשות זאת! diff --git a/translations/he/quiz-app/README.md b/translations/he/quiz-app/README.md index de67d0350..46f4d3211 100644 --- a/translations/he/quiz-app/README.md +++ b/translations/he/quiz-app/README.md @@ -1,12 +1,3 @@ - # חידונים החידונים האלה הם חידוני טרום ואחרי הרצאה עבור תוכנית הלימודים של למידת מכונה בכתובת https://aka.ms/ml-beginners diff --git a/translations/he/sketchnotes/LICENSE.md b/translations/he/sketchnotes/LICENSE.md index 41c458b68..a71828307 100644 --- a/translations/he/sketchnotes/LICENSE.md +++ b/translations/he/sketchnotes/LICENSE.md @@ -1,12 +1,3 @@ - זכויות, 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{ + "original_hash": "fba3b94d88bfb9b81369b869a1e9a20f", + "translation_date": "2025-09-05T20:02:32+00:00", + "source_file": "sketchnotes/LICENSE.md", + "language_code": "nl" + }, + "sketchnotes/README.md": { + "original_hash": "a88d5918c1b9da69a40d917a0840c497", + "translation_date": "2025-09-05T20:01:08+00:00", + "source_file": "sketchnotes/README.md", + "language_code": "nl" + } +} \ No newline at end of file diff --git a/translations/nl/1-Introduction/1-intro-to-ML/README.md b/translations/nl/1-Introduction/1-intro-to-ML/README.md index e0773c08e..a8621c200 100644 --- a/translations/nl/1-Introduction/1-intro-to-ML/README.md +++ b/translations/nl/1-Introduction/1-intro-to-ML/README.md @@ -1,12 +1,3 @@ - # Introductie tot machine learning ## [Quiz voorafgaand aan de les](https://ff-quizzes.netlify.app/en/ml/) diff --git a/translations/nl/1-Introduction/1-intro-to-ML/assignment.md b/translations/nl/1-Introduction/1-intro-to-ML/assignment.md index af2be855a..07306fc6f 100644 --- a/translations/nl/1-Introduction/1-intro-to-ML/assignment.md +++ b/translations/nl/1-Introduction/1-intro-to-ML/assignment.md @@ -1,12 +1,3 @@ - # Aan de slag ## Instructies diff --git a/translations/nl/1-Introduction/2-history-of-ML/README.md b/translations/nl/1-Introduction/2-history-of-ML/README.md index dd2196d2e..dfe10b201 100644 --- a/translations/nl/1-Introduction/2-history-of-ML/README.md +++ b/translations/nl/1-Introduction/2-history-of-ML/README.md @@ -1,12 +1,3 @@ - # Geschiedenis van machine learning ![Samenvatting van de geschiedenis van machine learning in een sketchnote](../../../../sketchnotes/ml-history.png) diff --git a/translations/nl/1-Introduction/2-history-of-ML/assignment.md b/translations/nl/1-Introduction/2-history-of-ML/assignment.md index c565c85b4..9985ff8c8 100644 --- a/translations/nl/1-Introduction/2-history-of-ML/assignment.md +++ b/translations/nl/1-Introduction/2-history-of-ML/assignment.md @@ -1,12 +1,3 @@ - # Maak een tijdlijn ## Instructies diff --git a/translations/nl/1-Introduction/3-fairness/README.md b/translations/nl/1-Introduction/3-fairness/README.md index 4fb0c1076..1f8cadd31 100644 --- a/translations/nl/1-Introduction/3-fairness/README.md +++ b/translations/nl/1-Introduction/3-fairness/README.md @@ -1,12 +1,3 @@ - # Machine Learning-oplossingen bouwen met verantwoorde AI ![Samenvatting van verantwoorde AI in Machine Learning in een sketchnote](../../../../sketchnotes/ml-fairness.png) diff --git a/translations/nl/1-Introduction/3-fairness/assignment.md b/translations/nl/1-Introduction/3-fairness/assignment.md index 06385ce45..9d093d002 100644 --- a/translations/nl/1-Introduction/3-fairness/assignment.md +++ b/translations/nl/1-Introduction/3-fairness/assignment.md @@ -1,12 +1,3 @@ - # Verken de Responsible AI Toolbox ## Instructies diff --git a/translations/nl/1-Introduction/4-techniques-of-ML/README.md b/translations/nl/1-Introduction/4-techniques-of-ML/README.md index be81eb0bb..54165a200 100644 --- a/translations/nl/1-Introduction/4-techniques-of-ML/README.md +++ b/translations/nl/1-Introduction/4-techniques-of-ML/README.md @@ -1,12 +1,3 @@ - # Technieken van Machine Learning Het proces van het bouwen, gebruiken en onderhouden van machine learning-modellen en de gegevens die ze gebruiken, verschilt sterk van veel andere ontwikkelworkflows. In deze les zullen we het proces verduidelijken en de belangrijkste technieken bespreken die je moet kennen. Je zult: diff --git a/translations/nl/1-Introduction/4-techniques-of-ML/assignment.md b/translations/nl/1-Introduction/4-techniques-of-ML/assignment.md index bda32dce3..456e9e82d 100644 --- a/translations/nl/1-Introduction/4-techniques-of-ML/assignment.md +++ b/translations/nl/1-Introduction/4-techniques-of-ML/assignment.md @@ -1,12 +1,3 @@ - # Interview een data scientist ## Instructies diff --git a/translations/nl/1-Introduction/README.md b/translations/nl/1-Introduction/README.md index f204a2739..5639b08d8 100644 --- a/translations/nl/1-Introduction/README.md +++ b/translations/nl/1-Introduction/README.md @@ -1,12 +1,3 @@ - # Introductie tot machine learning In dit deel van het curriculum maak je kennis met de basisconcepten van machine learning, wat het is, en leer je over de geschiedenis en de technieken die onderzoekers gebruiken om ermee te werken. Laten we samen deze nieuwe wereld van ML verkennen! diff --git a/translations/nl/2-Regression/1-Tools/README.md b/translations/nl/2-Regression/1-Tools/README.md index 7fab569ab..650da2132 100644 --- a/translations/nl/2-Regression/1-Tools/README.md +++ b/translations/nl/2-Regression/1-Tools/README.md @@ -1,12 +1,3 @@ - # Aan de slag met Python en Scikit-learn voor regressiemodellen ![Samenvatting van regressies in een sketchnote](../../../../sketchnotes/ml-regression.png) diff --git a/translations/nl/2-Regression/1-Tools/assignment.md b/translations/nl/2-Regression/1-Tools/assignment.md index 7746630e5..671574819 100644 --- a/translations/nl/2-Regression/1-Tools/assignment.md +++ b/translations/nl/2-Regression/1-Tools/assignment.md @@ -1,12 +1,3 @@ - # Regressie met Scikit-learn ## Instructies diff --git a/translations/nl/2-Regression/1-Tools/solution/Julia/README.md b/translations/nl/2-Regression/1-Tools/solution/Julia/README.md index b32979253..a235eb554 100644 --- a/translations/nl/2-Regression/1-Tools/solution/Julia/README.md +++ b/translations/nl/2-Regression/1-Tools/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/nl/2-Regression/2-Data/README.md b/translations/nl/2-Regression/2-Data/README.md index 008a5e333..be3391f49 100644 --- a/translations/nl/2-Regression/2-Data/README.md +++ b/translations/nl/2-Regression/2-Data/README.md @@ -1,12 +1,3 @@ - # Bouw een regressiemodel met Scikit-learn: data voorbereiden en visualiseren ![Infographic over datavisualisatie](../../../../2-Regression/2-Data/images/data-visualization.png) diff --git a/translations/nl/2-Regression/2-Data/assignment.md b/translations/nl/2-Regression/2-Data/assignment.md index e2c24f328..6d6a085ac 100644 --- a/translations/nl/2-Regression/2-Data/assignment.md +++ b/translations/nl/2-Regression/2-Data/assignment.md @@ -1,12 +1,3 @@ - # Visualisaties Verkennen Er zijn verschillende bibliotheken beschikbaar voor datavisualisatie. Maak enkele visualisaties met behulp van de Pompoen-data in deze les met matplotlib en seaborn in een voorbeeldnotebook. Welke bibliotheken zijn gemakkelijker om mee te werken? diff --git a/translations/nl/2-Regression/2-Data/solution/Julia/README.md b/translations/nl/2-Regression/2-Data/solution/Julia/README.md index 228468475..148374632 100644 --- a/translations/nl/2-Regression/2-Data/solution/Julia/README.md +++ b/translations/nl/2-Regression/2-Data/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/nl/2-Regression/3-Linear/README.md b/translations/nl/2-Regression/3-Linear/README.md index 1cb6dce40..078bed5cb 100644 --- a/translations/nl/2-Regression/3-Linear/README.md +++ b/translations/nl/2-Regression/3-Linear/README.md @@ -1,12 +1,3 @@ - # Bouw een regressiemodel met Scikit-learn: regressie op vier manieren ![Lineaire vs polynomiale regressie infographic](../../../../2-Regression/3-Linear/images/linear-polynomial.png) @@ -114,11 +105,11 @@ Nu je een begrip hebt van de wiskunde achter lineaire regressie, laten we een re Uit de vorige les heb je waarschijnlijk gezien dat de gemiddelde prijs voor verschillende maanden er als volgt uitziet: -Gemiddelde prijs per maand +Gemiddelde prijs per maand Dit suggereert dat er enige correlatie zou moeten zijn, en we kunnen proberen een lineair regressiemodel te trainen om de relatie tussen `Maand` en `Prijs`, of tussen `DagVanJaar` en `Prijs` te voorspellen. Hier is het spreidingsdiagram dat de laatste relatie toont: -Spreidingsdiagram van Prijs vs. Dag van het Jaar +Spreidingsdiagram van Prijs vs. Dag van het Jaar Laten we kijken of er een correlatie is met behulp van de `corr`-functie: @@ -137,7 +128,7 @@ for i,var in enumerate(new_pumpkins['Variety'].unique()): ax = df.plot.scatter('DayOfYear','Price',ax=ax,c=colors[i],label=var) ``` -Spreidingsdiagram van Prijs vs. Dag van het Jaar +Spreidingsdiagram van Prijs vs. Dag van het Jaar Ons onderzoek suggereert dat de soort meer invloed heeft op de totale prijs dan de daadwerkelijke verkoopdatum. We kunnen dit zien met een staafdiagram: @@ -145,7 +136,7 @@ Ons onderzoek suggereert dat de soort meer invloed heeft op de totale prijs dan new_pumpkins.groupby('Variety')['Price'].mean().plot(kind='bar') ``` -Staafdiagram van prijs vs soort +Staafdiagram van prijs vs soort Laten we ons voorlopig alleen richten op één pompoensoort, de 'pie type', en kijken welk effect de datum heeft op de prijs: @@ -153,7 +144,7 @@ Laten we ons voorlopig alleen richten op één pompoensoort, de 'pie type', en k pie_pumpkins = new_pumpkins[new_pumpkins['Variety']=='PIE TYPE'] pie_pumpkins.plot.scatter('DayOfYear','Price') ``` -Spreidingsdiagram van Prijs vs. Dag van het Jaar +Spreidingsdiagram van Prijs vs. Dag van het Jaar Als we nu de correlatie tussen `Prijs` en `DagVanJaar` berekenen met behulp van de `corr`-functie, krijgen we iets als `-0.27` - wat betekent dat het trainen van een voorspellend model zinvol is. @@ -227,7 +218,7 @@ plt.scatter(X_test,y_test) plt.plot(X_test,pred) ``` -Lineaire regressie +Lineaire regressie ## Polynomiale Regressie @@ -256,7 +247,7 @@ Het gebruik van `PolynomialFeatures(2)` betekent dat we alle tweedegraads polyno Pipelines kunnen op dezelfde manier worden gebruikt als het oorspronkelijke `LinearRegression`-object, d.w.z. we kunnen de pipeline `fitten` en vervolgens `predict` gebruiken om de voorspelde resultaten te krijgen. Hier is de grafiek die testgegevens en de benaderingscurve toont: -Polynomiale regressie +Polynomiale regressie Met polynomiale regressie kunnen we een iets lagere MSE en hogere determinatie krijgen, maar niet significant. We moeten rekening houden met andere kenmerken! @@ -274,7 +265,7 @@ In een ideale wereld willen we prijzen voor verschillende pompoenvariëteiten ku Hier kun je zien hoe de gemiddelde prijs afhankelijk is van de variëteit: -Gemiddelde prijs per variëteit +Gemiddelde prijs per variëteit Om rekening te houden met variëteit, moeten we deze eerst omzetten naar numerieke vorm, of **coderen**. Er zijn verschillende manieren waarop we dit kunnen doen: diff --git a/translations/nl/2-Regression/3-Linear/assignment.md b/translations/nl/2-Regression/3-Linear/assignment.md index 9a722b36d..e9f121768 100644 --- a/translations/nl/2-Regression/3-Linear/assignment.md +++ b/translations/nl/2-Regression/3-Linear/assignment.md @@ -1,12 +1,3 @@ - # Maak een Regressiemodel ## Instructies diff --git a/translations/nl/2-Regression/3-Linear/solution/Julia/README.md b/translations/nl/2-Regression/3-Linear/solution/Julia/README.md index 55e6bf523..4452daa84 100644 --- a/translations/nl/2-Regression/3-Linear/solution/Julia/README.md +++ b/translations/nl/2-Regression/3-Linear/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/nl/2-Regression/4-Logistic/README.md b/translations/nl/2-Regression/4-Logistic/README.md index b43e46fc7..ccf5a0618 100644 --- a/translations/nl/2-Regression/4-Logistic/README.md +++ b/translations/nl/2-Regression/4-Logistic/README.md @@ -1,12 +1,3 @@ - # Logistische regressie om categorieën te voorspellen ![Logistische vs. lineaire regressie infographic](../../../../2-Regression/4-Logistic/images/linear-vs-logistic.png) diff --git a/translations/nl/2-Regression/4-Logistic/assignment.md b/translations/nl/2-Regression/4-Logistic/assignment.md index 259a9c51e..6f901db56 100644 --- a/translations/nl/2-Regression/4-Logistic/assignment.md +++ b/translations/nl/2-Regression/4-Logistic/assignment.md @@ -1,12 +1,3 @@ - # Opnieuw proberen met een regressie ## Instructies diff --git a/translations/nl/2-Regression/4-Logistic/solution/Julia/README.md b/translations/nl/2-Regression/4-Logistic/solution/Julia/README.md index 244562463..da981bf61 100644 --- a/translations/nl/2-Regression/4-Logistic/solution/Julia/README.md +++ b/translations/nl/2-Regression/4-Logistic/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/nl/2-Regression/README.md b/translations/nl/2-Regression/README.md index 341fc613b..e87437d7d 100644 --- a/translations/nl/2-Regression/README.md +++ b/translations/nl/2-Regression/README.md @@ -1,12 +1,3 @@ - # Regressiemodellen voor machine learning ## Regionaal onderwerp: Regressiemodellen voor pompoenprijzen in Noord-Amerika 🎃 diff --git a/translations/nl/3-Web-App/1-Web-App/README.md b/translations/nl/3-Web-App/1-Web-App/README.md index fefca1d42..22109668e 100644 --- a/translations/nl/3-Web-App/1-Web-App/README.md +++ b/translations/nl/3-Web-App/1-Web-App/README.md @@ -1,12 +1,3 @@ - # Bouw een webapp om een ML-model te gebruiken In deze les ga je een ML-model trainen op een dataset die letterlijk buitenaards is: _UFO-waarnemingen van de afgelopen eeuw_, afkomstig uit de NUFORC-database. diff --git a/translations/nl/3-Web-App/1-Web-App/assignment.md b/translations/nl/3-Web-App/1-Web-App/assignment.md index 3711dec3f..82cd98c95 100644 --- a/translations/nl/3-Web-App/1-Web-App/assignment.md +++ b/translations/nl/3-Web-App/1-Web-App/assignment.md @@ -1,12 +1,3 @@ - # Probeer een ander model ## Instructies diff --git a/translations/nl/3-Web-App/README.md b/translations/nl/3-Web-App/README.md index d103bd645..6034b7086 100644 --- a/translations/nl/3-Web-App/README.md +++ b/translations/nl/3-Web-App/README.md @@ -1,12 +1,3 @@ - # Bouw een webapp om je ML-model te gebruiken In dit deel van de cursus maak je kennis met een toegepast ML-onderwerp: hoe je je Scikit-learn model kunt opslaan als een bestand dat gebruikt kan worden om voorspellingen te doen binnen een webapplicatie. Zodra het model is opgeslagen, leer je hoe je het kunt gebruiken in een webapp gebouwd met Flask. Je maakt eerst een model met behulp van gegevens over UFO-waarnemingen! Vervolgens bouw je een webapp waarmee je een aantal seconden, een breedtegraad en een lengtegraad kunt invoeren om te voorspellen welk land een UFO heeft gemeld. diff --git a/translations/nl/4-Classification/1-Introduction/README.md b/translations/nl/4-Classification/1-Introduction/README.md index 96c8a8861..84cd84224 100644 --- a/translations/nl/4-Classification/1-Introduction/README.md +++ b/translations/nl/4-Classification/1-Introduction/README.md @@ -1,12 +1,3 @@ - # Introductie tot classificatie In deze vier lessen ga je een fundamenteel aspect van klassieke machine learning verkennen: _classificatie_. We zullen verschillende classificatie-algoritmen gebruiken met een dataset over de briljante keukens van Azië en India. Hopelijk heb je trek! diff --git a/translations/nl/4-Classification/1-Introduction/assignment.md b/translations/nl/4-Classification/1-Introduction/assignment.md index 7f7c8a507..68fc5b072 100644 --- a/translations/nl/4-Classification/1-Introduction/assignment.md +++ b/translations/nl/4-Classification/1-Introduction/assignment.md @@ -1,12 +1,3 @@ - # Verken classificatiemethoden ## Instructies diff --git a/translations/nl/4-Classification/1-Introduction/solution/Julia/README.md b/translations/nl/4-Classification/1-Introduction/solution/Julia/README.md index d94a58cc5..d24cff40e 100644 --- a/translations/nl/4-Classification/1-Introduction/solution/Julia/README.md +++ b/translations/nl/4-Classification/1-Introduction/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/nl/4-Classification/2-Classifiers-1/README.md b/translations/nl/4-Classification/2-Classifiers-1/README.md index 356d3f08c..f622c0334 100644 --- a/translations/nl/4-Classification/2-Classifiers-1/README.md +++ b/translations/nl/4-Classification/2-Classifiers-1/README.md @@ -1,12 +1,3 @@ - # Categorieën van keukens 1 In deze les gebruik je de dataset die je in de vorige les hebt opgeslagen, vol met gebalanceerde, schone gegevens over keukens. diff --git a/translations/nl/4-Classification/2-Classifiers-1/assignment.md b/translations/nl/4-Classification/2-Classifiers-1/assignment.md index 619e95df4..d78df276a 100644 --- a/translations/nl/4-Classification/2-Classifiers-1/assignment.md +++ b/translations/nl/4-Classification/2-Classifiers-1/assignment.md @@ -1,12 +1,3 @@ - # Bestudeer de oplossers ## Instructies diff --git a/translations/nl/4-Classification/2-Classifiers-1/solution/Julia/README.md b/translations/nl/4-Classification/2-Classifiers-1/solution/Julia/README.md index 53b5bbf00..d24cff40e 100644 --- a/translations/nl/4-Classification/2-Classifiers-1/solution/Julia/README.md +++ b/translations/nl/4-Classification/2-Classifiers-1/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/nl/4-Classification/3-Classifiers-2/README.md b/translations/nl/4-Classification/3-Classifiers-2/README.md index 0b6ae707f..0acd6d802 100644 --- a/translations/nl/4-Classification/3-Classifiers-2/README.md +++ b/translations/nl/4-Classification/3-Classifiers-2/README.md @@ -1,12 +1,3 @@ - # Culinair Classificators 2 In deze tweede les over classificatie ga je meer manieren verkennen om numerieke gegevens te classificeren. Je leert ook over de gevolgen van het kiezen van de ene classifier boven de andere. diff --git a/translations/nl/4-Classification/3-Classifiers-2/assignment.md b/translations/nl/4-Classification/3-Classifiers-2/assignment.md index 318e0e15a..9199ce4ee 100644 --- a/translations/nl/4-Classification/3-Classifiers-2/assignment.md +++ b/translations/nl/4-Classification/3-Classifiers-2/assignment.md @@ -1,12 +1,3 @@ - # Parameter Spel ## Instructies diff --git a/translations/nl/4-Classification/3-Classifiers-2/solution/Julia/README.md b/translations/nl/4-Classification/3-Classifiers-2/solution/Julia/README.md index 904d7ca03..a235eb554 100644 --- a/translations/nl/4-Classification/3-Classifiers-2/solution/Julia/README.md +++ b/translations/nl/4-Classification/3-Classifiers-2/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/nl/4-Classification/4-Applied/README.md b/translations/nl/4-Classification/4-Applied/README.md index fbe21169f..cb2bac0d1 100644 --- a/translations/nl/4-Classification/4-Applied/README.md +++ b/translations/nl/4-Classification/4-Applied/README.md @@ -1,12 +1,3 @@ - # Bouw een Webapplicatie voor Cuisines Aanbevelingen In deze les ga je een classificatiemodel bouwen met behulp van enkele technieken die je in eerdere lessen hebt geleerd, en met de dataset van heerlijke gerechten die in deze serie wordt gebruikt. Daarnaast bouw je een kleine webapplicatie om een opgeslagen model te gebruiken, waarbij je gebruik maakt van Onnx's web runtime. diff --git a/translations/nl/4-Classification/4-Applied/assignment.md b/translations/nl/4-Classification/4-Applied/assignment.md index 8029b67b7..83d2834e1 100644 --- a/translations/nl/4-Classification/4-Applied/assignment.md +++ b/translations/nl/4-Classification/4-Applied/assignment.md @@ -1,12 +1,3 @@ - # Bouw een aanbevelingssysteem ## Instructies diff --git a/translations/nl/4-Classification/README.md b/translations/nl/4-Classification/README.md index 1b8ccb7c4..9bdf4f461 100644 --- a/translations/nl/4-Classification/README.md +++ b/translations/nl/4-Classification/README.md @@ -1,12 +1,3 @@ - # Aan de slag met classificatie ## Regionaal onderwerp: Heerlijke Aziatische en Indiase gerechten 🍜 diff --git a/translations/nl/5-Clustering/1-Visualize/README.md b/translations/nl/5-Clustering/1-Visualize/README.md index 33ca36a6a..edc022acc 100644 --- a/translations/nl/5-Clustering/1-Visualize/README.md +++ b/translations/nl/5-Clustering/1-Visualize/README.md @@ -1,12 +1,3 @@ - # Introductie tot clustering Clustering is een type [Ongecontroleerd Leren](https://wikipedia.org/wiki/Unsupervised_learning) dat ervan uitgaat dat een dataset niet gelabeld is of dat de invoer niet gekoppeld is aan vooraf gedefinieerde uitkomsten. Het gebruikt verschillende algoritmen om door niet-gelabelde gegevens te sorteren en groepen te vormen op basis van patronen die het in de gegevens herkent. diff --git a/translations/nl/5-Clustering/1-Visualize/assignment.md b/translations/nl/5-Clustering/1-Visualize/assignment.md index 1235c74c6..a1b490538 100644 --- a/translations/nl/5-Clustering/1-Visualize/assignment.md +++ b/translations/nl/5-Clustering/1-Visualize/assignment.md @@ -1,12 +1,3 @@ - # Onderzoek andere visualisaties voor clustering ## Instructies diff --git a/translations/nl/5-Clustering/1-Visualize/solution/Julia/README.md b/translations/nl/5-Clustering/1-Visualize/solution/Julia/README.md index 427f36c7d..a235eb554 100644 --- a/translations/nl/5-Clustering/1-Visualize/solution/Julia/README.md +++ b/translations/nl/5-Clustering/1-Visualize/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/nl/5-Clustering/2-K-Means/README.md b/translations/nl/5-Clustering/2-K-Means/README.md index 865c334ac..e97175253 100644 --- a/translations/nl/5-Clustering/2-K-Means/README.md +++ b/translations/nl/5-Clustering/2-K-Means/README.md @@ -1,12 +1,3 @@ - # K-Means clustering ## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ml/) diff --git a/translations/nl/5-Clustering/2-K-Means/assignment.md b/translations/nl/5-Clustering/2-K-Means/assignment.md index b380003a0..ee1538fcd 100644 --- a/translations/nl/5-Clustering/2-K-Means/assignment.md +++ b/translations/nl/5-Clustering/2-K-Means/assignment.md @@ -1,12 +1,3 @@ - # Probeer verschillende clusteringmethoden ## Instructies diff --git a/translations/nl/5-Clustering/2-K-Means/solution/Julia/README.md b/translations/nl/5-Clustering/2-K-Means/solution/Julia/README.md index 54a9685d2..a235eb554 100644 --- a/translations/nl/5-Clustering/2-K-Means/solution/Julia/README.md +++ b/translations/nl/5-Clustering/2-K-Means/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/nl/5-Clustering/README.md b/translations/nl/5-Clustering/README.md index d4337fb58..e75185c26 100644 --- a/translations/nl/5-Clustering/README.md +++ b/translations/nl/5-Clustering/README.md @@ -1,12 +1,3 @@ - # Clustermodellen voor machine learning Clustering is een machine learning-taak waarbij wordt gezocht naar objecten die op elkaar lijken en deze worden gegroepeerd in groepen die clusters worden genoemd. Wat clustering onderscheidt van andere benaderingen in machine learning, is dat alles automatisch gebeurt. Sterker nog, het is eerlijk om te zeggen dat het het tegenovergestelde is van supervised learning. diff --git a/translations/nl/6-NLP/1-Introduction-to-NLP/README.md b/translations/nl/6-NLP/1-Introduction-to-NLP/README.md index cea231121..5bc219141 100644 --- a/translations/nl/6-NLP/1-Introduction-to-NLP/README.md +++ b/translations/nl/6-NLP/1-Introduction-to-NLP/README.md @@ -1,12 +1,3 @@ - # Introductie tot natuurlijke taalverwerking Deze les behandelt een korte geschiedenis en belangrijke concepten van *natuurlijke taalverwerking*, een subveld van *computational linguistics*. diff --git a/translations/nl/6-NLP/1-Introduction-to-NLP/assignment.md b/translations/nl/6-NLP/1-Introduction-to-NLP/assignment.md index f7710536e..d03742e0a 100644 --- a/translations/nl/6-NLP/1-Introduction-to-NLP/assignment.md +++ b/translations/nl/6-NLP/1-Introduction-to-NLP/assignment.md @@ -1,12 +1,3 @@ - # Zoek een bot ## Instructies diff --git a/translations/nl/6-NLP/2-Tasks/README.md b/translations/nl/6-NLP/2-Tasks/README.md index 5abc8971d..4c8afd25a 100644 --- a/translations/nl/6-NLP/2-Tasks/README.md +++ b/translations/nl/6-NLP/2-Tasks/README.md @@ -1,12 +1,3 @@ - # Veelvoorkomende taken en technieken in natuurlijke taalverwerking Voor de meeste *natuurlijke taalverwerking*-taken moet de te verwerken tekst worden opgesplitst, geanalyseerd en de resultaten worden opgeslagen of vergeleken met regels en datasets. Deze taken stellen de programmeur in staat om de _betekenis_ of _intentie_ of alleen de _frequentie_ van termen en woorden in een tekst af te leiden. diff --git a/translations/nl/6-NLP/2-Tasks/assignment.md b/translations/nl/6-NLP/2-Tasks/assignment.md index 981f073f3..315631431 100644 --- a/translations/nl/6-NLP/2-Tasks/assignment.md +++ b/translations/nl/6-NLP/2-Tasks/assignment.md @@ -1,12 +1,3 @@ - # Laat een Bot terugpraten ## Instructies diff --git a/translations/nl/6-NLP/3-Translation-Sentiment/README.md b/translations/nl/6-NLP/3-Translation-Sentiment/README.md index 103ac9b2b..b88ac3112 100644 --- a/translations/nl/6-NLP/3-Translation-Sentiment/README.md +++ b/translations/nl/6-NLP/3-Translation-Sentiment/README.md @@ -1,12 +1,3 @@ - # Vertaling en sentimentanalyse met ML In de vorige lessen heb je geleerd hoe je een eenvoudige bot kunt bouwen met `TextBlob`, een bibliotheek die machine learning achter de schermen gebruikt om basis NLP-taken uit te voeren, zoals het extraheren van zelfstandige naamwoordgroepen. Een andere belangrijke uitdaging in de computationele taalkunde is het nauwkeurig _vertalen_ van een zin van de ene gesproken of geschreven taal naar de andere. diff --git a/translations/nl/6-NLP/3-Translation-Sentiment/assignment.md b/translations/nl/6-NLP/3-Translation-Sentiment/assignment.md index 91e65e8e9..6710add9b 100644 --- a/translations/nl/6-NLP/3-Translation-Sentiment/assignment.md +++ b/translations/nl/6-NLP/3-Translation-Sentiment/assignment.md @@ -1,12 +1,3 @@ - # Dichterlijke vrijheid ## Instructies diff --git a/translations/nl/6-NLP/3-Translation-Sentiment/solution/Julia/README.md b/translations/nl/6-NLP/3-Translation-Sentiment/solution/Julia/README.md index 5ae681b23..cf1420e6f 100644 --- a/translations/nl/6-NLP/3-Translation-Sentiment/solution/Julia/README.md +++ b/translations/nl/6-NLP/3-Translation-Sentiment/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/nl/6-NLP/3-Translation-Sentiment/solution/R/README.md b/translations/nl/6-NLP/3-Translation-Sentiment/solution/R/README.md index 8a59361ad..cf1420e6f 100644 --- a/translations/nl/6-NLP/3-Translation-Sentiment/solution/R/README.md +++ b/translations/nl/6-NLP/3-Translation-Sentiment/solution/R/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/nl/6-NLP/4-Hotel-Reviews-1/README.md b/translations/nl/6-NLP/4-Hotel-Reviews-1/README.md index b608544ba..64053ac96 100644 --- a/translations/nl/6-NLP/4-Hotel-Reviews-1/README.md +++ b/translations/nl/6-NLP/4-Hotel-Reviews-1/README.md @@ -1,12 +1,3 @@ - # Sentimentanalyse met hotelbeoordelingen - gegevens verwerken In deze sectie gebruik je de technieken uit de vorige lessen om een verkennende gegevensanalyse uit te voeren op een grote dataset. Zodra je een goed begrip hebt van de bruikbaarheid van de verschillende kolommen, leer je: diff --git a/translations/nl/6-NLP/4-Hotel-Reviews-1/assignment.md b/translations/nl/6-NLP/4-Hotel-Reviews-1/assignment.md index c6606ee73..eaa3c8b19 100644 --- a/translations/nl/6-NLP/4-Hotel-Reviews-1/assignment.md +++ b/translations/nl/6-NLP/4-Hotel-Reviews-1/assignment.md @@ -1,12 +1,3 @@ - # NLTK ## Instructies diff --git a/translations/nl/6-NLP/4-Hotel-Reviews-1/solution/Julia/README.md b/translations/nl/6-NLP/4-Hotel-Reviews-1/solution/Julia/README.md index 2fd84de49..a235eb554 100644 --- a/translations/nl/6-NLP/4-Hotel-Reviews-1/solution/Julia/README.md +++ b/translations/nl/6-NLP/4-Hotel-Reviews-1/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/nl/6-NLP/4-Hotel-Reviews-1/solution/R/README.md b/translations/nl/6-NLP/4-Hotel-Reviews-1/solution/R/README.md index 2554f1e02..cf1420e6f 100644 --- a/translations/nl/6-NLP/4-Hotel-Reviews-1/solution/R/README.md +++ b/translations/nl/6-NLP/4-Hotel-Reviews-1/solution/R/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/nl/6-NLP/5-Hotel-Reviews-2/README.md b/translations/nl/6-NLP/5-Hotel-Reviews-2/README.md index 768d55767..fbd639ef6 100644 --- a/translations/nl/6-NLP/5-Hotel-Reviews-2/README.md +++ b/translations/nl/6-NLP/5-Hotel-Reviews-2/README.md @@ -1,12 +1,3 @@ - # Sentimentanalyse met hotelbeoordelingen Nu je de dataset in detail hebt verkend, is het tijd om de kolommen te filteren en vervolgens NLP-technieken toe te passen op de dataset om nieuwe inzichten over de hotels te verkrijgen. diff --git a/translations/nl/6-NLP/5-Hotel-Reviews-2/assignment.md b/translations/nl/6-NLP/5-Hotel-Reviews-2/assignment.md index c4c9c910d..559e8b0b3 100644 --- a/translations/nl/6-NLP/5-Hotel-Reviews-2/assignment.md +++ b/translations/nl/6-NLP/5-Hotel-Reviews-2/assignment.md @@ -1,12 +1,3 @@ - # Probeer een andere dataset ## Instructies diff --git a/translations/nl/6-NLP/5-Hotel-Reviews-2/solution/Julia/README.md b/translations/nl/6-NLP/5-Hotel-Reviews-2/solution/Julia/README.md index 2de454e5f..06871f000 100644 --- a/translations/nl/6-NLP/5-Hotel-Reviews-2/solution/Julia/README.md +++ b/translations/nl/6-NLP/5-Hotel-Reviews-2/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/nl/6-NLP/5-Hotel-Reviews-2/solution/R/README.md b/translations/nl/6-NLP/5-Hotel-Reviews-2/solution/R/README.md index 14e123a53..d24cff40e 100644 --- a/translations/nl/6-NLP/5-Hotel-Reviews-2/solution/R/README.md +++ b/translations/nl/6-NLP/5-Hotel-Reviews-2/solution/R/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/nl/6-NLP/README.md b/translations/nl/6-NLP/README.md index 1a1589142..39c986901 100644 --- a/translations/nl/6-NLP/README.md +++ b/translations/nl/6-NLP/README.md @@ -1,12 +1,3 @@ - # Aan de slag met natuurlijke taalverwerking Natuurlijke taalverwerking (NLP) is het vermogen van een computerprogramma om menselijke taal te begrijpen zoals deze wordt gesproken en geschreven – aangeduid als natuurlijke taal. Het is een onderdeel van kunstmatige intelligentie (AI). NLP bestaat al meer dan 50 jaar en heeft zijn oorsprong in de taalkunde. Het hele vakgebied is gericht op het helpen van machines om de menselijke taal te begrijpen en te verwerken. Dit kan vervolgens worden gebruikt om taken uit te voeren zoals spellingscontrole of machinale vertaling. Het heeft een breed scala aan toepassingen in de echte wereld in verschillende gebieden, waaronder medisch onderzoek, zoekmachines en bedrijfsinformatie. diff --git a/translations/nl/6-NLP/data/README.md b/translations/nl/6-NLP/data/README.md index c7f660e5d..0bfc85387 100644 --- a/translations/nl/6-NLP/data/README.md +++ b/translations/nl/6-NLP/data/README.md @@ -1,12 +1,3 @@ - Download de hotelbeoordelingsgegevens naar deze map. --- diff --git a/translations/nl/7-TimeSeries/1-Introduction/README.md b/translations/nl/7-TimeSeries/1-Introduction/README.md index e63a80515..66530e09f 100644 --- a/translations/nl/7-TimeSeries/1-Introduction/README.md +++ b/translations/nl/7-TimeSeries/1-Introduction/README.md @@ -1,12 +1,3 @@ - # Introductie tot tijdreeksvoorspelling ![Samenvatting van tijdreeksen in een sketchnote](../../../../sketchnotes/ml-timeseries.png) diff --git a/translations/nl/7-TimeSeries/1-Introduction/assignment.md b/translations/nl/7-TimeSeries/1-Introduction/assignment.md index 4c585bdf8..c56632405 100644 --- a/translations/nl/7-TimeSeries/1-Introduction/assignment.md +++ b/translations/nl/7-TimeSeries/1-Introduction/assignment.md @@ -1,12 +1,3 @@ - # Visualiseer meer tijdreeksen ## Instructies diff --git a/translations/nl/7-TimeSeries/1-Introduction/solution/Julia/README.md b/translations/nl/7-TimeSeries/1-Introduction/solution/Julia/README.md index 7b1da57cb..a235eb554 100644 --- a/translations/nl/7-TimeSeries/1-Introduction/solution/Julia/README.md +++ b/translations/nl/7-TimeSeries/1-Introduction/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/nl/7-TimeSeries/1-Introduction/solution/R/README.md b/translations/nl/7-TimeSeries/1-Introduction/solution/R/README.md index 215fd143a..d24cff40e 100644 --- a/translations/nl/7-TimeSeries/1-Introduction/solution/R/README.md +++ b/translations/nl/7-TimeSeries/1-Introduction/solution/R/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/nl/7-TimeSeries/2-ARIMA/README.md b/translations/nl/7-TimeSeries/2-ARIMA/README.md index f4b9b2c36..ae3075199 100644 --- a/translations/nl/7-TimeSeries/2-ARIMA/README.md +++ b/translations/nl/7-TimeSeries/2-ARIMA/README.md @@ -1,12 +1,3 @@ - # Tijdreeksvoorspelling met ARIMA In de vorige les heb je wat geleerd over tijdreeksvoorspelling en een dataset geladen die de fluctuaties van het elektriciteitsverbruik over een bepaalde periode laat zien. diff --git a/translations/nl/7-TimeSeries/2-ARIMA/assignment.md b/translations/nl/7-TimeSeries/2-ARIMA/assignment.md index c316933c5..ffbb4aacd 100644 --- a/translations/nl/7-TimeSeries/2-ARIMA/assignment.md +++ b/translations/nl/7-TimeSeries/2-ARIMA/assignment.md @@ -1,12 +1,3 @@ - # Een nieuw ARIMA-model ## Instructies diff --git a/translations/nl/7-TimeSeries/2-ARIMA/solution/Julia/README.md b/translations/nl/7-TimeSeries/2-ARIMA/solution/Julia/README.md index 9d9954fc1..a6d62fe55 100644 --- a/translations/nl/7-TimeSeries/2-ARIMA/solution/Julia/README.md +++ b/translations/nl/7-TimeSeries/2-ARIMA/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/nl/7-TimeSeries/2-ARIMA/solution/R/README.md b/translations/nl/7-TimeSeries/2-ARIMA/solution/R/README.md index 934abebfb..a6d62fe55 100644 --- a/translations/nl/7-TimeSeries/2-ARIMA/solution/R/README.md +++ b/translations/nl/7-TimeSeries/2-ARIMA/solution/R/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/nl/7-TimeSeries/3-SVR/README.md b/translations/nl/7-TimeSeries/3-SVR/README.md index 5f86f43da..c29738a13 100644 --- a/translations/nl/7-TimeSeries/3-SVR/README.md +++ b/translations/nl/7-TimeSeries/3-SVR/README.md @@ -1,12 +1,3 @@ - # Tijdreeksvoorspelling met Support Vector Regressor In de vorige les heb je geleerd hoe je het ARIMA-model kunt gebruiken om voorspellingen te maken voor tijdreeksen. Nu ga je kijken naar het Support Vector Regressor-model, een regressiemodel dat wordt gebruikt om continue gegevens te voorspellen. diff --git a/translations/nl/7-TimeSeries/3-SVR/assignment.md b/translations/nl/7-TimeSeries/3-SVR/assignment.md index 9d49ca619..14da10099 100644 --- a/translations/nl/7-TimeSeries/3-SVR/assignment.md +++ b/translations/nl/7-TimeSeries/3-SVR/assignment.md @@ -1,12 +1,3 @@ - # Een nieuw SVR-model ## Instructies [^1] diff --git a/translations/nl/7-TimeSeries/README.md b/translations/nl/7-TimeSeries/README.md index c937c1b9d..d727a18ea 100644 --- a/translations/nl/7-TimeSeries/README.md +++ b/translations/nl/7-TimeSeries/README.md @@ -1,12 +1,3 @@ - # Introductie tot tijdreeksvoorspelling Wat is tijdreeksvoorspelling? Het gaat om het voorspellen van toekomstige gebeurtenissen door trends uit het verleden te analyseren. diff --git a/translations/nl/8-Reinforcement/1-QLearning/README.md b/translations/nl/8-Reinforcement/1-QLearning/README.md index 7f0c5fc81..706089b4b 100644 --- a/translations/nl/8-Reinforcement/1-QLearning/README.md +++ b/translations/nl/8-Reinforcement/1-QLearning/README.md @@ -1,12 +1,3 @@ - # Introductie tot Reinforcement Learning en Q-Learning ![Samenvatting van reinforcement in machine learning in een sketchnote](../../../../sketchnotes/ml-reinforcement.png) diff --git a/translations/nl/8-Reinforcement/1-QLearning/assignment.md b/translations/nl/8-Reinforcement/1-QLearning/assignment.md index 8c46f71be..0c27b955f 100644 --- a/translations/nl/8-Reinforcement/1-QLearning/assignment.md +++ b/translations/nl/8-Reinforcement/1-QLearning/assignment.md @@ -1,12 +1,3 @@ - # Een Meer Realistische Wereld In onze situatie kon Peter zich bijna zonder moe te worden of honger te krijgen verplaatsen. In een meer realistische wereld moet hij af en toe gaan zitten om uit te rusten en zichzelf voeden. Laten we onze wereld realistischer maken door de volgende regels toe te passen: diff --git a/translations/nl/8-Reinforcement/1-QLearning/solution/Julia/README.md b/translations/nl/8-Reinforcement/1-QLearning/solution/Julia/README.md index abdde5616..00db9b4bf 100644 --- a/translations/nl/8-Reinforcement/1-QLearning/solution/Julia/README.md +++ b/translations/nl/8-Reinforcement/1-QLearning/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/nl/8-Reinforcement/1-QLearning/solution/R/README.md b/translations/nl/8-Reinforcement/1-QLearning/solution/R/README.md index a148236a6..d24cff40e 100644 --- a/translations/nl/8-Reinforcement/1-QLearning/solution/R/README.md +++ b/translations/nl/8-Reinforcement/1-QLearning/solution/R/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/nl/8-Reinforcement/2-Gym/README.md b/translations/nl/8-Reinforcement/2-Gym/README.md index 8c2aad280..a05062eeb 100644 --- a/translations/nl/8-Reinforcement/2-Gym/README.md +++ b/translations/nl/8-Reinforcement/2-Gym/README.md @@ -1,12 +1,3 @@ - # CartPole Schaatsen Het probleem dat we in de vorige les hebben opgelost, lijkt misschien een speelgoedprobleem, niet echt toepasbaar in echte scenario's. Dit is echter niet het geval, omdat veel echte problemen ook dit scenario delen - zoals het spelen van schaken of Go. Ze zijn vergelijkbaar omdat we ook een bord hebben met gegeven regels en een **discrete toestand**. diff --git a/translations/nl/8-Reinforcement/2-Gym/assignment.md b/translations/nl/8-Reinforcement/2-Gym/assignment.md index 0d5c2597e..3a911a656 100644 --- a/translations/nl/8-Reinforcement/2-Gym/assignment.md +++ b/translations/nl/8-Reinforcement/2-Gym/assignment.md @@ -1,12 +1,3 @@ - # Train Mountain Car [OpenAI Gym](http://gym.openai.com) is zo ontworpen dat alle omgevingen dezelfde API bieden - namelijk dezelfde methoden `reset`, `step` en `render`, en dezelfde abstracties van **actie ruimte** en **observatie ruimte**. Hierdoor zou het mogelijk moeten zijn om dezelfde reinforcement learning-algoritmes aan te passen aan verschillende omgevingen met minimale codewijzigingen. diff --git a/translations/nl/8-Reinforcement/2-Gym/solution/Julia/README.md b/translations/nl/8-Reinforcement/2-Gym/solution/Julia/README.md index 591ccc04f..a235eb554 100644 --- a/translations/nl/8-Reinforcement/2-Gym/solution/Julia/README.md +++ b/translations/nl/8-Reinforcement/2-Gym/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/nl/8-Reinforcement/2-Gym/solution/R/README.md b/translations/nl/8-Reinforcement/2-Gym/solution/R/README.md index cf7d1b2c9..a6d62fe55 100644 --- a/translations/nl/8-Reinforcement/2-Gym/solution/R/README.md +++ b/translations/nl/8-Reinforcement/2-Gym/solution/R/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/nl/8-Reinforcement/README.md b/translations/nl/8-Reinforcement/README.md index 05e7f66fc..0db01e768 100644 --- a/translations/nl/8-Reinforcement/README.md +++ b/translations/nl/8-Reinforcement/README.md @@ -1,12 +1,3 @@ - # Introductie tot reinforcement learning Reinforcement learning, RL, wordt gezien als een van de fundamentele machine learning paradigma's, naast supervised learning en unsupervised learning. RL draait om beslissingen: het nemen van de juiste beslissingen of er in ieder geval van leren. diff --git a/translations/nl/9-Real-World/1-Applications/README.md b/translations/nl/9-Real-World/1-Applications/README.md index 97bc47b24..1496266d9 100644 --- a/translations/nl/9-Real-World/1-Applications/README.md +++ b/translations/nl/9-Real-World/1-Applications/README.md @@ -1,12 +1,3 @@ - # Postscript: Machine learning in de echte wereld ![Samenvatting van machine learning in de echte wereld in een sketchnote](../../../../sketchnotes/ml-realworld.png) diff --git a/translations/nl/9-Real-World/1-Applications/assignment.md b/translations/nl/9-Real-World/1-Applications/assignment.md index de3c5d948..3e0ebf45b 100644 --- a/translations/nl/9-Real-World/1-Applications/assignment.md +++ b/translations/nl/9-Real-World/1-Applications/assignment.md @@ -1,12 +1,3 @@ - # Een ML Speurtocht ## Instructies diff --git a/translations/nl/9-Real-World/2-Debugging-ML-Models/README.md b/translations/nl/9-Real-World/2-Debugging-ML-Models/README.md index 2f0800a38..926fc80b4 100644 --- a/translations/nl/9-Real-World/2-Debugging-ML-Models/README.md +++ b/translations/nl/9-Real-World/2-Debugging-ML-Models/README.md @@ -1,12 +1,3 @@ - # Postscript: Model Debugging in Machine Learning met Responsible AI-dashboardcomponenten ## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ml/) diff --git a/translations/nl/9-Real-World/2-Debugging-ML-Models/assignment.md b/translations/nl/9-Real-World/2-Debugging-ML-Models/assignment.md index 04c2ef3be..38087222c 100644 --- a/translations/nl/9-Real-World/2-Debugging-ML-Models/assignment.md +++ b/translations/nl/9-Real-World/2-Debugging-ML-Models/assignment.md @@ -1,12 +1,3 @@ - # Verken het Responsible AI (RAI) dashboard ## Instructies diff --git a/translations/nl/9-Real-World/README.md b/translations/nl/9-Real-World/README.md index 15868c2af..5293d71df 100644 --- a/translations/nl/9-Real-World/README.md +++ b/translations/nl/9-Real-World/README.md @@ -1,12 +1,3 @@ - # Nawoord: Toepassingen van klassieke machine learning in de echte wereld In dit deel van de cursus maak je kennis met enkele toepassingen van klassieke ML in de echte wereld. We hebben het internet afgespeurd om whitepapers en artikelen te vinden over toepassingen die deze strategieën gebruiken, waarbij we neural networks, deep learning en AI zoveel mogelijk hebben vermeden. Ontdek hoe ML wordt gebruikt in bedrijfsprocessen, ecologische toepassingen, financiën, kunst en cultuur, en meer. diff --git a/translations/nl/AGENTS.md b/translations/nl/AGENTS.md index b0c1aa92f..f6013ed49 100644 --- a/translations/nl/AGENTS.md +++ b/translations/nl/AGENTS.md @@ -1,12 +1,3 @@ - # AGENTS.md ## Projectoverzicht diff --git a/translations/nl/CODE_OF_CONDUCT.md b/translations/nl/CODE_OF_CONDUCT.md index 11999ed75..118c94f93 100644 --- a/translations/nl/CODE_OF_CONDUCT.md +++ b/translations/nl/CODE_OF_CONDUCT.md @@ -1,12 +1,3 @@ - # Microsoft Open Source Gedragscode Dit project heeft de [Microsoft Open Source Gedragscode](https://opensource.microsoft.com/codeofconduct/) aangenomen. diff --git a/translations/nl/CONTRIBUTING.md b/translations/nl/CONTRIBUTING.md index 9db91831d..eb7090c28 100644 --- a/translations/nl/CONTRIBUTING.md +++ b/translations/nl/CONTRIBUTING.md @@ -1,12 +1,3 @@ - # Bijdragen Dit project verwelkomt bijdragen en suggesties. Voor de meeste bijdragen moet je akkoord gaan met een Contributor License Agreement (CLA), waarin je verklaart dat je het recht hebt om, en daadwerkelijk doet, ons de rechten te geven om jouw bijdrage te gebruiken. Voor meer informatie, bezoek https://cla.microsoft.com. diff --git a/translations/nl/README.md b/translations/nl/README.md index e4ddb790f..da19698f6 100644 --- a/translations/nl/README.md +++ b/translations/nl/README.md @@ -1,32 +1,23 @@ - -[![GitHub license](https://img.shields.io/github/license/microsoft/ML-For-Beginners.svg)](https://github.com/microsoft/ML-For-Beginners/blob/master/LICENSE) -[![GitHub contributors](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/) -[![GitHub issues](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/) -[![GitHub pull-requests](https://img.shields.io/github/issues-pr/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/pulls/) -[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com) - -[![GitHub watchers](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/) +[![GitHub-licentie](https://img.shields.io/github/license/microsoft/ML-For-Beginners.svg)](https://github.com/microsoft/ML-For-Beginners/blob/master/LICENSE) +[![GitHub bijdragers](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/) +[![GitHub problemen](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/) +[![GitHub pull-verzoeken](https://img.shields.io/github/issues-pr/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/pulls/) +[![PRs welkom](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com) + +[![GitHub volgers](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/) [![GitHub forks](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/) -[![GitHub stars](https://img.shields.io/github/stars/microsoft/ML-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/ML-For-Beginners/stargazers/) +[![GitHub sterren](https://img.shields.io/github/stars/microsoft/ML-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/ML-For-Beginners/stargazers/) -### 🌐 Ondersteuning voor meerdere talen +### 🌐 Meertalige Ondersteuning -#### Ondersteund via GitHub Actions (Geautomatiseerd & Altijd Actueel) +#### Ondersteund via GitHub Action (Geautomatiseerd & Altijd Up-to-Date) -[Arabisch](../ar/README.md) | [Bengaals](../bn/README.md) | [Bulgaars](../bg/README.md) | [Birmaans (Myanmar)](../my/README.md) | [Chinees (Vereenvoudigd)](../zh/README.md) | [Chinees (Traditioneel, Hong Kong)](../hk/README.md) | [Chinees (Traditioneel, Macau)](../mo/README.md) | [Chinees (Traditioneel, Taiwan)](../tw/README.md) | [Kroatisch](../hr/README.md) | [Tsjechisch](../cs/README.md) | [Deens](../da/README.md) | [Nederlands](./README.md) | [Ests](../et/README.md) | [Fins](../fi/README.md) | [Frans](../fr/README.md) | [Duits](../de/README.md) | [Grieks](../el/README.md) | [Hebreeuws](../he/README.md) | [Hindi](../hi/README.md) | [Hongaars](../hu/README.md) | [Indonesisch](../id/README.md) | [Italiaans](../it/README.md) | [Japans](../ja/README.md) | [Kannada](../kn/README.md) | [Koreaans](../ko/README.md) | [Litouws](../lt/README.md) | [Maleis](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepalees](../ne/README.md) | [Nigerian Pidgin](../pcm/README.md) | [Noors](../no/README.md) | [Perzisch (Farsi)](../fa/README.md) | [Pools](../pl/README.md) | [Portugees (Brazilië)](../br/README.md) | [Portugees (Portugal)](../pt/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Roemeens](../ro/README.md) | [Russisch](../ru/README.md) | [Servisch (Cyrillisch)](../sr/README.md) | [Slowaaks](../sk/README.md) | [Sloveens](../sl/README.md) | [Spaans](../es/README.md) | [Swahili](../sw/README.md) | [Zweeds](../sv/README.md) | [Tagalog (Filipijns)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thais](../th/README.md) | [Turks](../tr/README.md) | [Oekraïens](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamees](../vi/README.md) +[Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](../bg/README.md) | [Burmese (Myanmar)](../my/README.md) | [Chinees (Vereenvoudigd)](../zh-CN/README.md) | [Chinees (Traditioneel, Hong Kong)](../zh-HK/README.md) | [Chinees (Traditioneel, Macau)](../zh-MO/README.md) | [Chinees (Traditioneel, Taiwan)](../zh-TW/README.md) | [Kroatisch](../hr/README.md) | [Tsjechisch](../cs/README.md) | [Deens](../da/README.md) | [Nederlands](./README.md) | [Ests](../et/README.md) | [Fins](../fi/README.md) | [Frans](../fr/README.md) | [Duits](../de/README.md) | [Grieks](../el/README.md) | [Hebreeuws](../he/README.md) | [Hindi](../hi/README.md) | [Hongaars](../hu/README.md) | [Indonesisch](../id/README.md) | [Italiaans](../it/README.md) | [Japans](../ja/README.md) | [Kannada](../kn/README.md) | [Koreaans](../ko/README.md) | [Litouws](../lt/README.md) | [Maleis](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepalees](../ne/README.md) | [Nigeriaans Pidgin](../pcm/README.md) | [Noors](../no/README.md) | [Perzisch (Farsi)](../fa/README.md) | [Pools](../pl/README.md) | [Portugees (Brazilië)](../pt-BR/README.md) | [Portugees (Portugal)](../pt-PT/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Roemeens](../ro/README.md) | [Russisch](../ru/README.md) | [Servisch (Cyrillisch)](../sr/README.md) | [Slowaaks](../sk/README.md) | [Sloveens](../sl/README.md) | [Spaans](../es/README.md) | [Swahili](../sw/README.md) | [Zweeds](../sv/README.md) | [Tagalog (Filipijns)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thai](../th/README.md) | [Turks](../tr/README.md) | [Oekraïens](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamees](../vi/README.md) -> **Liever lokaal klonen?** +> **Lieber lokaal klonen?** -> Deze repository bevat meer dan 50 taalvertalingen wat de downloadgrootte aanzienlijk vergroot. Om zonder vertalingen te klonen, gebruik sparse checkout: +> Deze repository bevat vertalingen in meer dan 50 talen, wat de downloadgrootte aanzienlijk vergroot. Om zonder vertalingen te klonen, gebruik sparse checkout: > ```bash > git clone --filter=blob:none --sparse https://github.com/microsoft/ML-For-Beginners.git > cd ML-For-Beginners @@ -35,27 +26,27 @@ CO_OP_TRANSLATOR_METADATA: > Dit geeft je alles wat je nodig hebt om de cursus te voltooien met een veel snellere download. -#### Word lid van onze gemeenschap +#### Word Lid van Onze Community [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -We hebben een lopende Discord-serie 'learn with AI', leer meer en doe mee via [Learn with AI Series](https://aka.ms/learnwithai/discord) van 18 - 30 september 2025. Je krijgt tips en trucs over het gebruik van GitHub Copilot voor Data Science. +We hebben een doorlopende Discord 'learn with AI' serie, leer er meer over en doe mee op [Learn with AI Series](https://aka.ms/learnwithai/discord) van 18 - 30 september 2025. Je krijgt tips en trucs voor het gebruik van GitHub Copilot voor Data Science. -![Learn with AI series](../../../../translated_images/nl/3.9b58fd8d6c373c20.webp) +![Learn with AI series](../../translated_images/nl/3.9b58fd8d6c373c20.webp) # Machine Learning voor Beginners - Een Curriculum -> 🌍 Reis de wereld rond terwijl we Machine Learning verkennen aan de hand van wereldculturen 🌍 +> 🌍 Reis de wereld rond terwijl we Machine Learning verkennen via wereldculturen 🌍 -Cloud Advocates bij Microsoft bieden met plezier een 12-weken durend curriculum aan van 26 lessen dat volledig draait om **Machine Learning**. In dit curriculum leer je over wat soms wordt genoemd **klassieke machine learning**, waarbij voornamelijk gebruik wordt gemaakt van Scikit-learn als bibliotheek en deep learning wordt vermeden, wat behandeld wordt in ons [AI for Beginners' curriculum](https://aka.ms/ai4beginners). Combineer deze lessen ook met ons ['Data Science for Beginners' curriculum](https://aka.ms/ds4beginners)! +Cloud Advocates bij Microsoft bieden graag een 12-weeks, 26-lessen curriculum aan dat volledig over **Machine Learning** gaat. In dit curriculum leer je over wat soms **klassieke machine learning** wordt genoemd, waarbij voornamelijk Scikit-learn wordt gebruikt als bibliotheek en deep learning wordt vermeden, dat behandeld wordt in ons [AI voor Beginners' curriculum](https://aka.ms/ai4beginners). Koppel deze lessen ook aan ons ['Data Science voor Beginners' curriculum](https://aka.ms/ds4beginners)! -Reis met ons de wereld over terwijl we deze klassieke technieken toepassen op data uit verschillende delen van de wereld. Elke les bevat quizzen voor en na de les, geschreven instructies om de les te voltooien, een oplossing, een opdracht en meer. Onze projectgebaseerde onderwijsmethode laat je leren door te bouwen, een bewezen manier om nieuwe vaardigheden te laten beklijven. +Reis met ons mee over de wereld terwijl we deze klassieke technieken toepassen op data uit vele werelddelen. Elke les bevat pre- en post-les quizzes, geschreven instructies om de les te voltooien, een oplossing, een opdracht en meer. Onze projectgebaseerde pedagogiek stelt je in staat te leren terwijl je bouwt, een bewezen manier om nieuwe vaardigheden goed te beklijven. **✍️ Hartelijke dank aan onze auteurs** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, Ruth Yakubu en Amy Boyd -**🎨 Ook bedankt aan onze illustratoren** Tomomi Imura, Dasani Madipalli en Jen Looper +**🎨 Ook dank aan onze illustratoren** Tomomi Imura, Dasani Madipalli, en Jen Looper -**🙏 Speciale dank 🙏 aan onze Microsoft Student Ambassador auteurs, beoordelaars en inhoudsbijdragers**, met name Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila en Snigdha Agarwal +**🙏 Speciale dank 🙏 aan onze Microsoft Student Ambassador auteurs, reviewers en inhoudsbijdragers**, met name Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, en Snigdha Agarwal **🤩 Extra dank aan Microsoft Student Ambassadors Eric Wanjau, Jasleen Sondhi en Vidushi Gupta voor onze R-lessen!** @@ -65,114 +56,114 @@ Volg deze stappen: 1. **Fork de Repository**: Klik op de knop "Fork" rechtsboven op deze pagina. 2. **Clone de Repository**: `git clone https://github.com/microsoft/ML-For-Beginners.git` -> [vind alle aanvullende bronnen voor deze cursus in onze Microsoft Learn-collectie](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum) +> [vind alle aanvullende bronnen voor deze cursus in onze Microsoft Learn collectie](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum) -> 🔧 **Hulp nodig?** Bekijk onze [Probleemoplossingshandleiding](TROUBLESHOOTING.md) voor oplossingen van veelvoorkomende problemen met installatie, setup en het uitvoeren van lessen. +> 🔧 **Heb je hulp nodig?** Raadpleeg onze [Probleemoplossingsgids](TROUBLESHOOTING.md) voor oplossingen van veelvoorkomende installatie-, setup- en lesuitvoeringsproblemen. -**[Studenten](https://aka.ms/student-page)**, om dit curriculum te gebruiken, forkt u de volledige repo naar uw eigen GitHub-account en voltooit de oefeningen zelfstandig of in een groep: +**[Studenten](https://aka.ms/student-page)**, om dit curriculum te gebruiken, fork de hele repo naar je eigen GitHub-account en voltooi de oefeningen alleen of met een groep: -- Begin met een quiz voor de les. -- Lees de les en voltooi de activiteiten, pauzeer en reflecteer bij elke kennisklik. -- Probeer de projecten te maken door de lessen te begrijpen in plaats van de oplossing te draaien; die code is echter beschikbaar in de `/solution`-mappen van elke projectgerichte les. -- Maak de quiz na de les. -- Voltooi de uitdaging. -- Maak de opdracht af. -- Na het voltooien van een lessenreeks, bezoek het [Discussieforum](https://github.com/microsoft/ML-For-Beginners/discussions) en "leer hardop" door de juiste PAT-rubric in te vullen. Een 'PAT' is een Voortgangsbeoordelingsinstrument, een rubric die je invult om je leren te bevorderen. Je kunt ook reageren op andere PATs zodat we samen kunnen leren. +- Begin met een pre-college quiz. +- Lees de les en voltooi de activiteiten, pauzeer en reflecteer bij elke kenniscontrole. +- Probeer de projecten te maken door de lessen te begrijpen in plaats van simpelweg de oplossing te draaien; die code is echter beschikbaar in de `/solution` mappen bij elke projectgerichte les. +- Maak de post-college quiz. +- Voltooi de challenge. +- Voltooi de opdracht. +- Na het voltooien van een lesgroep, bezoek het [Discussiebord](https://github.com/microsoft/ML-For-Beginners/discussions) en "leer hardop" door het invullen van de bijbehorende PAT-rubric. Een 'PAT' is een Progress Assessment Tool, een rubric die je invult om je leren verder te brengen. Je kunt ook reageren op andere PAT's zodat we samen kunnen leren. -> Voor verdere studie bevelen we deze [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott) modules en leerroutes aan. +> Voor verdere studie raden we aan deze [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott) modules en leerpaden te volgen. -**Docenten**, we hebben [enkele suggesties opgenomen](for-teachers.md) over hoe dit curriculum te gebruiken. +**Docenten**, we hebben [enkele suggesties opgenomen](for-teachers.md) over hoe je dit curriculum kunt gebruiken. --- ## Video walkthroughs -Sommige lessen zijn beschikbaar als korte video's. Je vindt ze ingebed in de lessen, of op de [ML for Beginners afspeellijst op het Microsoft Developer YouTube-kanaal](https://aka.ms/ml-beginners-videos) door op de onderstaande afbeelding te klikken. +Sommige lessen zijn beschikbaar als korte video's. Je vindt deze inline in de lessen, of op de [ML voor Beginners afspeellijst op het Microsoft Developer YouTube-kanaal](https://aka.ms/ml-beginners-videos) door op de afbeelding hieronder te klikken. -[![ML for beginners banner](../../../../translated_images/nl/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos) +[![ML voor beginners banner](../../translated_images/nl/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos) --- -## Ontmoet het team +## Maak kennis met het Team [![Promo video](../../images/ml.gif)](https://youtu.be/Tj1XWrDSYJU) **Gif door** [Mohit Jaisal](https://linkedin.com/in/mohitjaisal) -> 🎥 Klik op de afbeelding hierboven voor een video over het project en de mensen die het hebben gemaakt! +> 🎥 Klik op de afbeelding hierboven voor een video over het project en de mensen die het gemaakt hebben! --- ## Pedagogiek -We hebben twee didactische principes gekozen tijdens het bouwen van dit curriculum: zorgen dat het hands-on **projectgebaseerd** is en dat het **regelmatige quizzen** bevat. Daarnaast heeft dit curriculum een gemeenschappelijk **thema** om samenhang te creëren. +We hebben gekozen voor twee pedagogische principes bij het bouwen van dit curriculum: zorgen dat het hands-on **project-gebaseerd** is en dat het **frequente quizzes** bevat. Daarnaast heeft dit curriculum een gemeenschappelijk **thema** voor samenhang. -Door ervoor te zorgen dat de inhoud aansluit bij projecten, wordt het proces boeiender voor studenten en zal het vasthouden van concepten worden versterkt. Bovendien stelt een quiz zonder hoge inzet vóór een les de intentie van de student om een onderwerp te leren, terwijl een tweede quiz na de les verdere retentie verzekert. Dit curriculum is ontworpen om flexibel en leuk te zijn en kan geheel of gedeeltelijk worden gevolgd. De projecten beginnen klein en worden steeds complexer tegen het einde van de 12 weken durende cyclus. Dit curriculum bevat ook een nasecretie over toepassingen van ML in de echte wereld, die kan worden gebruikt als extra krediet of als basis voor discussie. +Door ervoor te zorgen dat de inhoud aansluit op projecten, wordt het proces boeiender voor studenten en wordt het vasthouden van concepten versterkt. Daarnaast stelt een quiz met lage inzet vóór de les de intentie van de student om een onderwerp te leren, terwijl een tweede quiz na de les verdere retentie waarborgt. Dit curriculum is ontworpen om flexibel en leuk te zijn en kan geheel of gedeeltelijk worden gevolgd. De projecten beginnen klein en worden steeds complexer aan het eind van de 12 weken cyclus. Dit curriculum bevat ook een nageschrift over toepassingen van ML in de echte wereld, dat kan worden gebruikt als extra opdracht of als basis voor discussie. -> Vind ons [Gedragscode](CODE_OF_CONDUCT.md), [Bijdragen](CONTRIBUTING.md), [Vertalingen](TRANSLATIONS.md) en [Probleemoplossing](TROUBLESHOOTING.md) richtlijnen. We verwelkomen je constructieve feedback! +> Vind onze [Gedragscode](CODE_OF_CONDUCT.md), [Bijdragen](CONTRIBUTING.md), [Vertalingen](TRANSLATIONS.md) en [Probleemoplossing](TROUBLESHOOTING.md) richtlijnen. We verwelkomen je opbouwende feedback! ## Elke les bevat -- optionele sketchnote +- optioneel sketchnote - optionele aanvullende video -- video walkthrough (sommige lessen alleen) -- [voor-de-les warming-up quiz](https://ff-quizzes.netlify.app/en/ml/) +- video walkthrough (sommige lessen) +- [pre-les warm-up quiz](https://ff-quizzes.netlify.app/en/ml/) - geschreven les -- voor projectgerichte lessen, stapsgewijze gidsen om het project te bouwen -- kennischecks +- voor project-gebaseerde lessen, stapsgewijze gidsen over hoe je het project opzet +- kenniscontroles - een uitdaging -- aanvullende lectuur +- aanvullende literatuur - opdracht -- [na-de-les quiz](https://ff-quizzes.netlify.app/en/ml/) - -> **Een opmerking over talen**: Deze lessen zijn voornamelijk geschreven in Python, maar velen zijn ook beschikbaar in R. Om een R-les te voltooien, ga naar de `/solution` map en zoek naar R-lessen. Deze bevatten een .rmd extensie die staat voor een **R Markdown** bestand dat eenvoudig gedefinieerd kan worden als een insluiting van `codeblokken` (van R of andere talen) en een `YAML-header` (die begeleidt hoe outputformaten zoals PDF worden opgemaakt) in een `Markdown document`. Als zodanig dient het als een voorbeeldige auteurframework voor datawetenschap omdat je je code, de output en je gedachten kunt combineren door ze in Markdown op te schrijven. Bovendien kunnen R Markdown documenten worden gerenderd naar outputformaten zoals PDF, HTML of Word. -> **Een opmerking over quizzen**: Alle quizzen bevinden zich in de [Quiz App map](../../quiz-app), in totaal 52 quizzen met elk drie vragen. Ze worden vanuit de lessen gelinkt, maar de quiz-app kan lokaal worden uitgevoerd; volg de instructies in de `quiz-app` map om lokaal te hosten of te implementeren naar Azure. - -| Lesnummer | Onderwerp | Lesgroepering | Leerdoelen | Gekoppelde les | Auteur | -| :-------: | :------------------------------------------------------------: | :-----------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------: | -| 01 | Introductie tot machine learning | [Introductie](1-Introduction/README.md) | Leer de basisconcepten achter machine learning | [Les](1-Introduction/1-intro-to-ML/README.md) | Muhammad | -| 02 | De geschiedenis van machine learning | [Introductie](1-Introduction/README.md) | Leer de geschiedenis achter dit vakgebied | [Les](1-Introduction/2-history-of-ML/README.md) | Jen en Amy | -| 03 | Rechtvaardigheid en machine learning | [Introductie](1-Introduction/README.md) | Wat zijn de belangrijke filosofische kwesties rond rechtvaardigheid die studenten moeten overwegen bij het bouwen en toepassen van ML modellen? | [Les](1-Introduction/3-fairness/README.md) | Tomomi | -| 04 | Technieken voor machine learning | [Introductie](1-Introduction/README.md) | Welke technieken gebruiken ML-onderzoekers om ML-modellen te bouwen? | [Les](1-Introduction/4-techniques-of-ML/README.md) | Chris en Jen | -| 05 | Introductie tot regressie | [Regressie](2-Regression/README.md) | Beginnen met Python en Scikit-learn voor regressiemodellen | [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html) | Jen • Eric Wanjau | -| 06 | Noord-Amerikaanse pompoenprijzen 🎃 | [Regressie](2-Regression/README.md) | Visualiseer en reinig data ter voorbereiding van ML | [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html) | Jen • Eric Wanjau | -| 07 | Noord-Amerikaanse pompoenprijzen 🎃 | [Regressie](2-Regression/README.md) | Bouw lineaire en polynomiale regressiemodellen | [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html) | Jen en Dmitry • Eric Wanjau | -| 08 | Noord-Amerikaanse pompoenprijzen 🎃 | [Regressie](2-Regression/README.md) | Bouw een logistiek regressiemodel | [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html) | Jen • Eric Wanjau | -| 09 | Een Web App 🔌 | [Web App](3-Web-App/README.md) | Bouw een webapp om je getrainde model te gebruiken | [Python](3-Web-App/1-Web-App/README.md) | Jen | -| 10 | Introductie tot classificatie | [Classificatie](4-Classification/README.md) | Reinig, bereid voor en visualiseer je data; introductie tot classificatie | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html) | Jen en Cassie • Eric Wanjau | -| 11 | Heerlijke Aziatische en Indiase keukens 🍜 | [Classificatie](4-Classification/README.md) | Introductie tot classifiers | [Python](4-Classification/2-Classifiers-1/README.md) • [R](../../4-Classification/2-Classifiers-1/solution/R/lesson_11.html) | Jen en Cassie • Eric Wanjau | -| 12 | Heerlijke Aziatische en Indiase keukens 🍜 | [Classificatie](4-Classification/README.md) | Meer classifiers | [Python](4-Classification/3-Classifiers-2/README.md) • [R](../../4-Classification/3-Classifiers-2/solution/R/lesson_12.html) | Jen en Cassie • Eric Wanjau | -| 13 | Heerlijke Aziatische en Indiase keukens 🍜 | [Classificatie](4-Classification/README.md) | Bouw een aanbevelings-webapp met behulp van je model | [Python](4-Classification/4-Applied/README.md) | Jen | -| 14 | Introductie tot clustering | [Clustering](5-Clustering/README.md) | Reinig, bereid voor en visualiseer je data; introductie tot clustering | [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html) | Jen • Eric Wanjau | -| 15 | Verkennen van Nigeriaanse muzieksmaken 🎧 | [Clustering](5-Clustering/README.md) | Verken de K-Means clusteringmethode | [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html) | Jen • Eric Wanjau | -| 16 | Introductie tot natuurlijke taalverwerking ☕️ | [Natuurlijke taalverwerking](6-NLP/README.md) | Leer de basis van NLP door een eenvoudige bot te bouwen | [Python](6-NLP/1-Introduction-to-NLP/README.md) | Stephen | -| 17 | Veelvoorkomende NLP-taken ☕️ | [Natuurlijke taalverwerking](6-NLP/README.md) | Verdiep je NLP-kennis door de veelvoorkomende taken te begrijpen die nodig zijn bij het werken met taalstructuren | [Python](6-NLP/2-Tasks/README.md) | Stephen | -| 18 | Vertaling en sentimentanalyse ♥️ | [Natuurlijke taalverwerking](6-NLP/README.md) | Vertaling en sentimentanalyse met Jane Austen | [Python](6-NLP/3-Translation-Sentiment/README.md) | Stephen | -| 19 | Romantische hotels in Europa ♥️ | [Natuurlijke taalverwerking](6-NLP/README.md) | Sentimentanalyse met hotelbeoordelingen 1 | [Python](6-NLP/4-Hotel-Reviews-1/README.md) | Stephen | -| 20 | Romantische hotels in Europa ♥️ | [Natuurlijke taalverwerking](6-NLP/README.md) | Sentimentanalyse met hotelbeoordelingen 2 | [Python](6-NLP/5-Hotel-Reviews-2/README.md) | Stephen | -| 21 | Introductie tot tijdreeksvoorspellingen | [Tijdreeks](7-TimeSeries/README.md) | Introductie tot tijdreeksvoorspellingen | [Python](7-TimeSeries/1-Introduction/README.md) | Francesca | -| 22 | ⚡️ Wereldenergieverbruik ⚡️ - tijdreeksvoorspelling met ARIMA | [Tijdreeks](7-TimeSeries/README.md) | Tijdreeksvoorspelling met ARIMA | [Python](7-TimeSeries/2-ARIMA/README.md) | Francesca | -| 23 | ⚡️ Wereldenergieverbruik ⚡️ - tijdreeksvoorspelling met SVR | [Tijdreeks](7-TimeSeries/README.md) | Tijdreeksvoorspelling met Support Vector Regressor | [Python](7-TimeSeries/3-SVR/README.md) | Anirban | -| 24 | Introductie tot reinforcement learning | [Reinforcement learning](8-Reinforcement/README.md) | Introductie tot reinforcement learning met Q-Learning | [Python](8-Reinforcement/1-QLearning/README.md) | Dmitry | -| 25 | Help Peter de wolf te vermijden! 🐺 | [Reinforcement learning](8-Reinforcement/README.md) | Reinforcement learning Gym | [Python](8-Reinforcement/2-Gym/README.md) | Dmitry | -| Nawoord | Realistische ML scenario's en toepassingen | [ML in het Wild](9-Real-World/README.md) | Interessante en onthullende toepassingen van klassieke ML in de echte wereld | [Les](9-Real-World/1-Applications/README.md) | Team | -| Nawoord | Modeldebugging in ML met de RAI-dashboard | [ML in het Wild](9-Real-World/README.md) | Modeldebugging in Machine Learning met behulp van Responsible AI dashboardcomponenten | [Les](9-Real-World/2-Debugging-ML-Models/README.md) | Ruth Yakubu | - -> [vind alle aanvullende bronnen voor deze cursus in onze Microsoft Learn-collectie](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum) +- [post-les quiz](https://ff-quizzes.netlify.app/en/ml/) + +> **Een opmerking over talen**: Deze lessen zijn hoofdzakelijk geschreven in Python, maar vele zijn ook beschikbaar in R. Om een R-les te voltooien, ga naar de `/solution` map en zoek bij R-lessen. Ze bevatten een .rmd extensie, wat staat voor een **R Markdown** bestand dat simpelweg kan worden gedefinieerd als een combinatie van `codeblokken` (van R of andere talen) en een `YAML-kop` (die aanstuurt hoe outputformaten, zoals PDF, opgemaakt worden) in een `Markdown-document`. Als zodanig dient het als een uitstekend auteur-framework voor datawetenschap omdat je zo je code, de output en je gedachten kunt combineren door ze in Markdown op te schrijven. Bovendien kunnen R Markdown documenten worden gerenderd naar outputformaten zoals PDF, HTML of Word. +> **Een opmerking over quizzes**: Alle quizzes staan in de [Quiz App map](../../quiz-app), in totaal 52 quizzes van elk drie vragen. Ze worden vanuit de lessen gekoppeld, maar de quizapp kan lokaal worden uitgevoerd; volg de instructies in de `quiz-app` map om lokaal te hosten of te implementeren naar Azure. + +| Lesnummer | Onderwerp | Les Groepering | Leerdoelen | Gekoppelde Les | Auteur | +| :-------: | :--------------------------------------------------------------: | :----------------------------------------------: | --------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------: | +| 01 | Introductie tot machine learning | [Introductie](1-Introduction/README.md) | Leer de basisconcepten achter machine learning | [Les](1-Introduction/1-intro-to-ML/README.md) | Muhammad | +| 02 | De geschiedenis van machine learning | [Introductie](1-Introduction/README.md) | Leer de geschiedenis achter dit vakgebied | [Les](1-Introduction/2-history-of-ML/README.md) | Jen en Amy | +| 03 | Rechtvaardigheid en machine learning | [Introductie](1-Introduction/README.md) | Wat zijn belangrijke filosofische kwesties rondom rechtvaardigheid die studenten moeten overwegen bij het bouwen en toepassen van ML-modellen? | [Les](1-Introduction/3-fairness/README.md) | Tomomi | +| 04 | Technieken voor machine learning | [Introductie](1-Introduction/README.md) | Welke technieken gebruiken ML-onderzoekers om ML-modellen te bouwen? | [Les](1-Introduction/4-techniques-of-ML/README.md) | Chris en Jen | +| 05 | Introductie tot regressie | [Regressie](2-Regression/README.md) | Begin met Python en Scikit-learn voor regressiemodellen | [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html) | Jen • Eric Wanjau | +| 06 | Noord-Amerikaanse pompoenprijzen 🎃 | [Regressie](2-Regression/README.md) | Visualiseer en reinig data ter voorbereiding op ML | [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html) | Jen • Eric Wanjau | +| 07 | Noord-Amerikaanse pompoenprijzen 🎃 | [Regressie](2-Regression/README.md) | Bouw lineaire en polynomiale regressiemodellen | [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html) | Jen en Dmitry • Eric Wanjau | +| 08 | Noord-Amerikaanse pompoenprijzen 🎃 | [Regressie](2-Regression/README.md) | Bouw een logistisch regressiemodel | [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html) | Jen • Eric Wanjau | +| 09 | Een Web App 🔌 | [Web App](3-Web-App/README.md) | Bouw een webapp om je getrainde model te gebruiken | [Python](3-Web-App/1-Web-App/README.md) | Jen | +| 10 | Introductie tot classificatie | [Classificatie](4-Classification/README.md) | Reinig, bereid voor en visualiseer je data; introductie tot classificatie | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html) | Jen en Cassie • Eric Wanjau | +| 11 | Heerlijke Aziatische en Indiase keukens 🍜 | [Classificatie](4-Classification/README.md) | Introductie tot classifiers | [Python](4-Classification/2-Classifiers-1/README.md) • [R](../../4-Classification/2-Classifiers-1/solution/R/lesson_11.html) | Jen en Cassie • Eric Wanjau | +| 12 | Heerlijke Aziatische en Indiase keukens 🍜 | [Classificatie](4-Classification/README.md) | Meer classifiers | [Python](4-Classification/3-Classifiers-2/README.md) • [R](../../4-Classification/3-Classifiers-2/solution/R/lesson_12.html) | Jen en Cassie • Eric Wanjau | +| 13 | Heerlijke Aziatische en Indiase keukens 🍜 | [Classificatie](4-Classification/README.md) | Bouw een aanbevelings-webapp met je model | [Python](4-Classification/4-Applied/README.md) | Jen | +| 14 | Introductie tot clustering | [Clustering](5-Clustering/README.md) | Reinig, bereid voor en visualiseer je data; introductie tot clustering | [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html) | Jen • Eric Wanjau | +| 15 | Verkennen van Nigeriaanse muzikale smaken 🎧 | [Clustering](5-Clustering/README.md) | Verken de K-Means clusteringmethode | [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html) | Jen • Eric Wanjau | +| 16 | Introductie tot natuurlijke taalverwerking ☕️ | [Natuurlijke taalverwerking](6-NLP/README.md) | Leer de basis van NLP door een eenvoudige bot te bouwen | [Python](6-NLP/1-Introduction-to-NLP/README.md) | Stephen | +| 17 | Veelvoorkomende NLP taken ☕️ | [Natuurlijke taalverwerking](6-NLP/README.md) | Verdiep je NLP-kennis door veelvoorkomende taken te leren die bij taalstructuren horen | [Python](6-NLP/2-Tasks/README.md) | Stephen | +| 18 | Vertaling en sentimentanalyse ♥️ | [Natuurlijke taalverwerking](6-NLP/README.md) | Vertaling en sentimentanalyse met Jane Austen | [Python](6-NLP/3-Translation-Sentiment/README.md) | Stephen | +| 19 | Romantische hotels in Europa ♥️ | [Natuurlijke taalverwerking](6-NLP/README.md) | Sentimentanalyse met hotelreviews 1 | [Python](6-NLP/4-Hotel-Reviews-1/README.md) | Stephen | +| 20 | Romantische hotels in Europa ♥️ | [Natuurlijke taalverwerking](6-NLP/README.md) | Sentimentanalyse met hotelreviews 2 | [Python](6-NLP/5-Hotel-Reviews-2/README.md) | Stephen | +| 21 | Introductie tot tijdreeksvoorspelling | [Tijdreeks](7-TimeSeries/README.md) | Introductie tot tijdreeksvoorspelling | [Python](7-TimeSeries/1-Introduction/README.md) | Francesca | +| 22 | ⚡️ Wereld Energieverbruik ⚡️ - tijdreeksvoorspelling met ARIMA | [Tijdreeks](7-TimeSeries/README.md) | Tijdreeksvoorspelling met ARIMA | [Python](7-TimeSeries/2-ARIMA/README.md) | Francesca | +| 23 | ⚡️ Wereld Energieverbruik ⚡️ - tijdreeksvoorspelling met SVR | [Tijdreeks](7-TimeSeries/README.md) | Tijdreeksvoorspelling met Support Vector Regressor | [Python](7-TimeSeries/3-SVR/README.md) | Anirban | +| 24 | Introductie tot reinforcement learning | [Reinforcement learning](8-Reinforcement/README.md) | Introductie tot reinforcement learning met Q-Learning | [Python](8-Reinforcement/1-QLearning/README.md) | Dmitry | +| 25 | Help Peter de wolf vermijden! 🐺 | [Reinforcement learning](8-Reinforcement/README.md) | Reinforcement learning Gym | [Python](8-Reinforcement/2-Gym/README.md) | Dmitry | +| Postscript | Echte ML scenario’s en toepassingen | [ML in het Wild](9-Real-World/README.md) | Interessante en onthullende toepassingen van klassieke ML in de praktijk | [Les](9-Real-World/1-Applications/README.md) | Team | +| Postscript | Model Debugging in ML met RAI-dashboard | [ML in het Wild](9-Real-World/README.md) | Model-debugging in machine learning met Responsible AI-dashboardcomponenten | [Les](9-Real-World/2-Debugging-ML-Models/README.md) | Ruth Yakubu | + +> [vind alle aanvullende bronnen voor deze cursus in onze Microsoft Learn collectie](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum) ## Offline toegang -Je kunt deze documentatie offline gebruiken door gebruik te maken van [Docsify](https://docsify.js.org/#/). Fork deze repo, [installeer Docsify](https://docsify.js.org/#/quickstart) op je lokale machine en typ dan in de hoofdmap van deze repo `docsify serve`. De website wordt geserveerd op poort 3000 op je localhost: `localhost:3000`. +Je kunt deze documentatie offline gebruiken met [Docsify](https://docsify.js.org/#/). Fork deze repo, [installeer Docsify](https://docsify.js.org/#/quickstart) op je eigen machine, en typ dan in de hoofdmap van deze repo `docsify serve`. De website wordt dan bediend op poort 3000 op je localhost: `localhost:3000`. -## PDF's +## PDF’s Vind een pdf van het curriculum met links [hier](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf). -## 🎒 Andere cursussen +## 🎒 Andere Cursussen -Ons team produceert ook andere cursussen! Bekijk: +Ons team maakt ook andere cursussen! Bekijk: ### LangChain @@ -190,10 +181,10 @@ Ons team produceert ook andere cursussen! Bekijk: --- ### Generative AI Series -[![Generative AI voor Beginners](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst) -[![Generative AI (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst) -[![Generative AI (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst) -[![Generative AI (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst) +[![Generatieve AI voor Beginners](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst) +[![Generatieve AI (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst) +[![Generatieve AI (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst) +[![Generatieve AI (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst) --- @@ -209,24 +200,24 @@ Ons team produceert ook andere cursussen! Bekijk: --- ### Copilot Serie -[![Copilot voor AI Gepaard Programmeren](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) +[![Copilot voor AI Gepaarde Programmering](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) [![Copilot voor C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst) [![Copilot Avontuur](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst) ## Hulp Krijgen -Als je vastloopt of vragen hebt over het bouwen van AI-apps. Sluit je aan bij mede-leerlingen en ervaren ontwikkelaars in discussies over MCP. Het is een ondersteunende gemeenschap waar vragen welkom zijn en kennis vrijelijk wordt gedeeld. +Als je vastloopt of vragen hebt over het bouwen van AI-apps. Doe mee met medeleerlingen en ervaren ontwikkelaars in discussies over MCP. Het is een ondersteunende gemeenschap waar vragen welkom zijn en kennis vrij wordt gedeeld. [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -Als je productfeedback of fouten hebt tijdens het bouwen, bezoek dan: +Als je productfeedback hebt of fouten tegenkomt tijdens het bouwen bezoek: -[![Microsoft Foundry Ontwikkelaarsforum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) +[![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) --- **Disclaimer**: -Dit document is vertaald met behulp van de AI-vertalingsdienst [Co-op Translator](https://github.com/Azure/co-op-translator). Hoewel we streven naar nauwkeurigheid, kan het zijn dat geautomatiseerde vertalingen fouten of onnauwkeurigheden bevatten. Het originele document in de oorspronkelijke taal geldt als de gezaghebbende bron. Voor belangrijke informatie wordt een professionele menselijke vertaling aanbevolen. Wij zijn niet aansprakelijk voor misverstanden of verkeerde interpretaties die voortvloeien uit het gebruik van deze vertaling. +Dit document is vertaald met behulp van de AI-vertalingsservice [Co-op Translator](https://github.com/Azure/co-op-translator). Hoewel we streven naar nauwkeurigheid, kan automatische vertaling fouten of onnauwkeurigheden bevatten. Het oorspronkelijke document in de oorspronkelijke taal dient als gezaghebbende bron te worden beschouwd. Voor cruciale informatie wordt professionele menselijke vertaling aanbevolen. Wij zijn niet aansprakelijk voor enige misverstanden of verkeerde interpretaties die voortvloeien uit het gebruik van deze vertaling. \ No newline at end of file diff --git a/translations/nl/SECURITY.md b/translations/nl/SECURITY.md index e15cd8060..4b2127eb8 100644 --- a/translations/nl/SECURITY.md +++ b/translations/nl/SECURITY.md @@ -1,12 +1,3 @@ - ## Beveiliging Microsoft neemt de beveiliging van onze softwareproducten en -diensten serieus, waaronder alle broncode-repositories die worden beheerd via onze GitHub-organisaties, waaronder [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet), [Xamarin](https://github.com/xamarin) en [onze GitHub-organisaties](https://opensource.microsoft.com/). diff --git a/translations/nl/SUPPORT.md b/translations/nl/SUPPORT.md index f7bf1619d..9ad3c3bd7 100644 --- a/translations/nl/SUPPORT.md +++ b/translations/nl/SUPPORT.md @@ -1,12 +1,3 @@ - # Ondersteuning ## Hoe problemen te melden en hulp te krijgen diff --git a/translations/nl/TROUBLESHOOTING.md b/translations/nl/TROUBLESHOOTING.md index c46e000ca..d39399ce2 100644 --- a/translations/nl/TROUBLESHOOTING.md +++ b/translations/nl/TROUBLESHOOTING.md @@ -1,12 +1,3 @@ - # Probleemoplossingsgids Deze gids helpt je bij het oplossen van veelvoorkomende problemen bij het werken met het curriculum Machine Learning voor Beginners. Als je hier geen oplossing vindt, kijk dan op onze [Discord Discussies](https://aka.ms/foundry/discord) of [open een issue](https://github.com/microsoft/ML-For-Beginners/issues). diff --git a/translations/nl/docs/_sidebar.md b/translations/nl/docs/_sidebar.md index b9987ddec..f6fd6ce51 100644 --- a/translations/nl/docs/_sidebar.md +++ b/translations/nl/docs/_sidebar.md @@ -1,12 +1,3 @@ - - Introductie - [Introductie tot Machine Learning](../1-Introduction/1-intro-to-ML/README.md) - [Geschiedenis van Machine Learning](../1-Introduction/2-history-of-ML/README.md) diff --git a/translations/nl/for-teachers.md b/translations/nl/for-teachers.md index de22caf56..831a74631 100644 --- a/translations/nl/for-teachers.md +++ b/translations/nl/for-teachers.md @@ -1,12 +1,3 @@ - ## Voor Docenten Wilt u deze lesstof in uw klas gebruiken? Voel u vrij! diff --git a/translations/nl/quiz-app/README.md b/translations/nl/quiz-app/README.md index 8334ed3df..f45ae588a 100644 --- a/translations/nl/quiz-app/README.md +++ b/translations/nl/quiz-app/README.md @@ -1,12 +1,3 @@ - # Quizzen Deze quizzen zijn de pre- en post-lezing quizzen voor het ML-curriculum op https://aka.ms/ml-beginners diff --git a/translations/nl/sketchnotes/LICENSE.md b/translations/nl/sketchnotes/LICENSE.md index 4498ad202..f4a67d11b 100644 --- a/translations/nl/sketchnotes/LICENSE.md +++ b/translations/nl/sketchnotes/LICENSE.md @@ -1,12 +1,3 @@ - Attribution-ShareAlike 4.0 Internationaal ======================================================================= diff --git a/translations/nl/sketchnotes/README.md b/translations/nl/sketchnotes/README.md index e47f8b926..d873a4b79 100644 --- a/translations/nl/sketchnotes/README.md +++ b/translations/nl/sketchnotes/README.md @@ -1,12 +1,3 @@ - Alle sketchnotes van het curriculum kunnen hier worden gedownload. 🖨 Voor afdrukken in hoge resolutie zijn de TIFF-versies beschikbaar op [deze repo](https://github.com/girliemac/a-picture-is-worth-a-1000-words/tree/main/ml/tiff). diff --git a/translations/vi/.co-op-translator.json b/translations/vi/.co-op-translator.json new file mode 100644 index 000000000..f93d974b5 --- /dev/null +++ b/translations/vi/.co-op-translator.json @@ -0,0 +1,596 @@ +{ + "1-Introduction/1-intro-to-ML/README.md": { + "original_hash": "69389392fa6346e0dfa30f664b7b6fec", + "translation_date": "2025-09-05T19:39:02+00:00", + "source_file": "1-Introduction/1-intro-to-ML/README.md", + "language_code": "vi" + }, + "1-Introduction/1-intro-to-ML/assignment.md": { + "original_hash": "4c4698044bb8af52cfb6388a4ee0e53b", + "translation_date": "2025-09-05T19:40:34+00:00", + "source_file": "1-Introduction/1-intro-to-ML/assignment.md", + "language_code": "vi" + }, + "1-Introduction/2-history-of-ML/README.md": { + "original_hash": "6a05fec147e734c3e6bfa54505648e2b", + "translation_date": 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--git a/translations/vi/1-Introduction/1-intro-to-ML/README.md b/translations/vi/1-Introduction/1-intro-to-ML/README.md index dc7d8ba76..6fe7ef5bf 100644 --- a/translations/vi/1-Introduction/1-intro-to-ML/README.md +++ b/translations/vi/1-Introduction/1-intro-to-ML/README.md @@ -1,12 +1,3 @@ - # Giới thiệu về học máy ## [Câu hỏi trước bài giảng](https://ff-quizzes.netlify.app/en/ml/) diff --git a/translations/vi/1-Introduction/1-intro-to-ML/assignment.md b/translations/vi/1-Introduction/1-intro-to-ML/assignment.md index 60769ffc4..81eb4ca21 100644 --- a/translations/vi/1-Introduction/1-intro-to-ML/assignment.md +++ b/translations/vi/1-Introduction/1-intro-to-ML/assignment.md @@ -1,12 +1,3 @@ - # Bắt đầu và hoạt động ## Hướng dẫn diff --git a/translations/vi/1-Introduction/2-history-of-ML/README.md b/translations/vi/1-Introduction/2-history-of-ML/README.md index d6b604e57..cc2e10f5d 100644 --- a/translations/vi/1-Introduction/2-history-of-ML/README.md +++ b/translations/vi/1-Introduction/2-history-of-ML/README.md @@ -1,12 +1,3 @@ - # Lịch sử học máy ![Tóm tắt lịch sử học máy trong một bản vẽ](../../../../sketchnotes/ml-history.png) diff --git a/translations/vi/1-Introduction/2-history-of-ML/assignment.md b/translations/vi/1-Introduction/2-history-of-ML/assignment.md index 0408aecc6..6505390b7 100644 --- a/translations/vi/1-Introduction/2-history-of-ML/assignment.md +++ b/translations/vi/1-Introduction/2-history-of-ML/assignment.md @@ -1,12 +1,3 @@ - # Tạo dòng thời gian ## Hướng dẫn diff --git a/translations/vi/1-Introduction/3-fairness/README.md b/translations/vi/1-Introduction/3-fairness/README.md index 5fc1e6fc2..bde68a7fc 100644 --- a/translations/vi/1-Introduction/3-fairness/README.md +++ b/translations/vi/1-Introduction/3-fairness/README.md @@ -1,12 +1,3 @@ - # Xây dựng giải pháp Machine Learning với AI có trách nhiệm ![Tóm tắt về AI có trách nhiệm trong Machine Learning qua sketchnote](../../../../sketchnotes/ml-fairness.png) diff --git a/translations/vi/1-Introduction/3-fairness/assignment.md b/translations/vi/1-Introduction/3-fairness/assignment.md index 5672bed53..5de4ae4e9 100644 --- a/translations/vi/1-Introduction/3-fairness/assignment.md +++ b/translations/vi/1-Introduction/3-fairness/assignment.md @@ -1,12 +1,3 @@ - # Khám phá Bộ công cụ AI có trách nhiệm ## Hướng dẫn diff --git a/translations/vi/1-Introduction/4-techniques-of-ML/README.md b/translations/vi/1-Introduction/4-techniques-of-ML/README.md index 378413698..c6d518d26 100644 --- a/translations/vi/1-Introduction/4-techniques-of-ML/README.md +++ b/translations/vi/1-Introduction/4-techniques-of-ML/README.md @@ -1,12 +1,3 @@ - # Kỹ thuật Học Máy Quy trình xây dựng, sử dụng và duy trì các mô hình học máy cùng dữ liệu mà chúng sử dụng là một quy trình rất khác biệt so với nhiều quy trình phát triển khác. Trong bài học này, chúng ta sẽ làm rõ quy trình này và phác thảo các kỹ thuật chính mà bạn cần biết. Bạn sẽ: diff --git a/translations/vi/1-Introduction/4-techniques-of-ML/assignment.md b/translations/vi/1-Introduction/4-techniques-of-ML/assignment.md index eff72b525..b865b143d 100644 --- a/translations/vi/1-Introduction/4-techniques-of-ML/assignment.md +++ b/translations/vi/1-Introduction/4-techniques-of-ML/assignment.md @@ -1,12 +1,3 @@ - # Phỏng vấn một nhà khoa học dữ liệu ## Hướng dẫn diff --git a/translations/vi/1-Introduction/README.md b/translations/vi/1-Introduction/README.md index ebf307448..b65ce45a6 100644 --- a/translations/vi/1-Introduction/README.md +++ b/translations/vi/1-Introduction/README.md @@ -1,12 +1,3 @@ - # Giới thiệu về học máy Trong phần này của chương trình học, bạn sẽ được giới thiệu về các khái niệm cơ bản trong lĩnh vực học máy, học máy là gì, cũng như tìm hiểu về lịch sử và các kỹ thuật mà các nhà nghiên cứu sử dụng để làm việc với nó. Hãy cùng khám phá thế giới mới của học máy (ML) nhé! diff --git a/translations/vi/2-Regression/1-Tools/README.md b/translations/vi/2-Regression/1-Tools/README.md index d90db3560..920f9910f 100644 --- a/translations/vi/2-Regression/1-Tools/README.md +++ b/translations/vi/2-Regression/1-Tools/README.md @@ -1,12 +1,3 @@ - # Bắt đầu với Python và Scikit-learn cho các mô hình hồi quy ![Tóm tắt về hồi quy trong một sketchnote](../../../../sketchnotes/ml-regression.png) diff --git a/translations/vi/2-Regression/1-Tools/assignment.md b/translations/vi/2-Regression/1-Tools/assignment.md index 8cb335394..fb4d35cba 100644 --- a/translations/vi/2-Regression/1-Tools/assignment.md +++ b/translations/vi/2-Regression/1-Tools/assignment.md @@ -1,12 +1,3 @@ - # Hồi quy với Scikit-learn ## Hướng dẫn diff --git a/translations/vi/2-Regression/1-Tools/solution/Julia/README.md b/translations/vi/2-Regression/1-Tools/solution/Julia/README.md index adf100fcc..61484c254 100644 --- a/translations/vi/2-Regression/1-Tools/solution/Julia/README.md +++ b/translations/vi/2-Regression/1-Tools/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/vi/2-Regression/2-Data/README.md b/translations/vi/2-Regression/2-Data/README.md index 0cd7afcd9..740aba761 100644 --- a/translations/vi/2-Regression/2-Data/README.md +++ b/translations/vi/2-Regression/2-Data/README.md @@ -1,12 +1,3 @@ - # Xây dựng mô hình hồi quy sử dụng Scikit-learn: chuẩn bị và trực quan hóa dữ liệu ![Infographic trực quan hóa dữ liệu](../../../../2-Regression/2-Data/images/data-visualization.png) diff --git a/translations/vi/2-Regression/2-Data/assignment.md b/translations/vi/2-Regression/2-Data/assignment.md index b3bc84e26..675066dad 100644 --- a/translations/vi/2-Regression/2-Data/assignment.md +++ b/translations/vi/2-Regression/2-Data/assignment.md @@ -1,12 +1,3 @@ - # Khám phá trực quan hóa dữ liệu Có nhiều thư viện khác nhau có sẵn để trực quan hóa dữ liệu. Tạo một số biểu đồ trực quan sử dụng dữ liệu Pumpkin trong bài học này với matplotlib và seaborn trong một notebook mẫu. Những thư viện nào dễ sử dụng hơn? diff --git a/translations/vi/2-Regression/2-Data/solution/Julia/README.md b/translations/vi/2-Regression/2-Data/solution/Julia/README.md index 900970f0e..61484c254 100644 --- a/translations/vi/2-Regression/2-Data/solution/Julia/README.md +++ b/translations/vi/2-Regression/2-Data/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/vi/2-Regression/3-Linear/README.md b/translations/vi/2-Regression/3-Linear/README.md index e322f5ea6..94fafcb56 100644 --- a/translations/vi/2-Regression/3-Linear/README.md +++ b/translations/vi/2-Regression/3-Linear/README.md @@ -1,12 +1,3 @@ - # Xây dựng mô hình hồi quy sử dụng Scikit-learn: hồi quy theo bốn cách ![Đồ họa thông tin hồi quy tuyến tính và đa thức](../../../../2-Regression/3-Linear/images/linear-polynomial.png) @@ -114,11 +105,11 @@ Bây giờ bạn đã hiểu toán học đằng sau hồi quy tuyến tính, h Từ bài học trước, bạn có thể đã thấy rằng giá trung bình cho các tháng khác nhau trông như thế này: -Giá trung bình theo tháng +Giá trung bình theo tháng Điều này gợi ý rằng có thể có một số tương quan, và chúng ta có thể thử huấn luyện mô hình hồi quy tuyến tính để dự đoán mối quan hệ giữa `Tháng` và `Giá`, hoặc giữa `NgàyTrongNăm` và `Giá`. Đây là biểu đồ phân tán cho thấy mối quan hệ sau: -Biểu đồ phân tán Giá vs. Ngày trong Năm +Biểu đồ phân tán Giá vs. Ngày trong Năm Hãy xem liệu có tương quan nào không bằng cách sử dụng hàm `corr`: @@ -137,7 +128,7 @@ for i,var in enumerate(new_pumpkins['Variety'].unique()): ax = df.plot.scatter('DayOfYear','Price',ax=ax,c=colors[i],label=var) ``` -Biểu đồ phân tán Giá vs. Ngày trong Năm +Biểu đồ phân tán Giá vs. Ngày trong Năm Cuộc điều tra của chúng ta gợi ý rằng loại bí ngô có ảnh hưởng lớn hơn đến giá tổng thể so với ngày bán thực tế. Chúng ta có thể thấy điều này với biểu đồ cột: @@ -145,7 +136,7 @@ Cuộc điều tra của chúng ta gợi ý rằng loại bí ngô có ảnh hư new_pumpkins.groupby('Variety')['Price'].mean().plot(kind='bar') ``` -Biểu đồ cột giá vs loại bí ngô +Biểu đồ cột giá vs loại bí ngô Hãy tập trung vào một loại bí ngô, loại 'pie type', và xem ngày bán có ảnh hưởng gì đến giá: @@ -153,7 +144,7 @@ Hãy tập trung vào một loại bí ngô, loại 'pie type', và xem ngày b pie_pumpkins = new_pumpkins[new_pumpkins['Variety']=='PIE TYPE'] pie_pumpkins.plot.scatter('DayOfYear','Price') ``` -Biểu đồ phân tán Giá vs. Ngày trong Năm +Biểu đồ phân tán Giá vs. Ngày trong Năm Nếu bây giờ chúng ta tính toán tương quan giữa `Giá` và `NgàyTrongNăm` bằng cách sử dụng hàm `corr`, chúng ta sẽ nhận được giá trị khoảng `-0.27` - điều này có nghĩa là việc huấn luyện một mô hình dự đoán là hợp lý. @@ -227,7 +218,7 @@ plt.scatter(X_test,y_test) plt.plot(X_test,pred) ``` -Hồi quy tuyến tính +Hồi quy tuyến tính ## Hồi quy đa thức @@ -256,7 +247,7 @@ Sử dụng `PolynomialFeatures(2)` có nghĩa là chúng ta sẽ bao gồm tấ Pipeline có thể được sử dụng theo cách tương tự như đối tượng `LinearRegression` ban đầu, tức là chúng ta có thể `fit` pipeline, sau đó sử dụng `predict` để nhận kết quả dự đoán. Đây là biểu đồ hiển thị dữ liệu kiểm tra và đường cong xấp xỉ: -Hồi quy đa thức +Hồi quy đa thức Sử dụng hồi quy đa thức, chúng ta có thể đạt được MSE thấp hơn một chút và hệ số xác định cao hơn, nhưng không đáng kể. Chúng ta cần xem xét các đặc trưng khác! @@ -274,7 +265,7 @@ Trong thế giới lý tưởng, chúng ta muốn có thể dự đoán giá cho Dưới đây là cách giá trung bình phụ thuộc vào loại bí ngô: -Giá trung bình theo loại +Giá trung bình theo loại Để đưa loại bí ngô vào mô hình, trước tiên chúng ta cần chuyển đổi nó sang dạng số, hoặc **mã hóa**. Có một số cách để thực hiện: diff --git a/translations/vi/2-Regression/3-Linear/assignment.md b/translations/vi/2-Regression/3-Linear/assignment.md index b658de445..dac5bb92c 100644 --- a/translations/vi/2-Regression/3-Linear/assignment.md +++ b/translations/vi/2-Regression/3-Linear/assignment.md @@ -1,12 +1,3 @@ - # Tạo Mô Hình Hồi Quy ## Hướng dẫn diff --git a/translations/vi/2-Regression/3-Linear/solution/Julia/README.md b/translations/vi/2-Regression/3-Linear/solution/Julia/README.md index 5922ef9fe..61484c254 100644 --- a/translations/vi/2-Regression/3-Linear/solution/Julia/README.md +++ b/translations/vi/2-Regression/3-Linear/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/vi/2-Regression/4-Logistic/README.md b/translations/vi/2-Regression/4-Logistic/README.md index 9dbea3ccb..ceafaccd0 100644 --- a/translations/vi/2-Regression/4-Logistic/README.md +++ b/translations/vi/2-Regression/4-Logistic/README.md @@ -1,12 +1,3 @@ - # Hồi quy Logistic để dự đoán danh mục ![Infographic về hồi quy Logistic và hồi quy tuyến tính](../../../../2-Regression/4-Logistic/images/linear-vs-logistic.png) diff --git a/translations/vi/2-Regression/4-Logistic/assignment.md b/translations/vi/2-Regression/4-Logistic/assignment.md index 2764f71d3..eb78cc41a 100644 --- a/translations/vi/2-Regression/4-Logistic/assignment.md +++ b/translations/vi/2-Regression/4-Logistic/assignment.md @@ -1,12 +1,3 @@ - # Thử lại một số hồi quy ## Hướng dẫn diff --git a/translations/vi/2-Regression/4-Logistic/solution/Julia/README.md b/translations/vi/2-Regression/4-Logistic/solution/Julia/README.md index 1c4726b61..61484c254 100644 --- a/translations/vi/2-Regression/4-Logistic/solution/Julia/README.md +++ b/translations/vi/2-Regression/4-Logistic/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/vi/2-Regression/README.md b/translations/vi/2-Regression/README.md index 6968791f2..d478ea0ae 100644 --- a/translations/vi/2-Regression/README.md +++ b/translations/vi/2-Regression/README.md @@ -1,12 +1,3 @@ - # Các mô hình hồi quy trong học máy ## Chủ đề khu vực: Các mô hình hồi quy cho giá bí ngô ở Bắc Mỹ 🎃 diff --git a/translations/vi/3-Web-App/1-Web-App/README.md b/translations/vi/3-Web-App/1-Web-App/README.md index a5c3ba03f..e61ac776f 100644 --- a/translations/vi/3-Web-App/1-Web-App/README.md +++ b/translations/vi/3-Web-App/1-Web-App/README.md @@ -1,12 +1,3 @@ - # Xây dựng ứng dụng web sử dụng mô hình ML Trong bài học này, bạn sẽ huấn luyện một mô hình ML trên một tập dữ liệu đặc biệt: _Các lần nhìn thấy UFO trong thế kỷ qua_, được lấy từ cơ sở dữ liệu của NUFORC. diff --git a/translations/vi/3-Web-App/1-Web-App/assignment.md b/translations/vi/3-Web-App/1-Web-App/assignment.md index e6f03f1e4..d08d95ed0 100644 --- a/translations/vi/3-Web-App/1-Web-App/assignment.md +++ b/translations/vi/3-Web-App/1-Web-App/assignment.md @@ -1,12 +1,3 @@ - # Thử một mô hình khác ## Hướng dẫn diff --git a/translations/vi/3-Web-App/README.md b/translations/vi/3-Web-App/README.md index b68e08c39..375299a83 100644 --- a/translations/vi/3-Web-App/README.md +++ b/translations/vi/3-Web-App/README.md @@ -1,12 +1,3 @@ - # Xây dựng ứng dụng web để sử dụng mô hình ML của bạn Trong phần này của chương trình học, bạn sẽ được giới thiệu về một chủ đề ML ứng dụng: cách lưu mô hình Scikit-learn của bạn dưới dạng tệp để có thể sử dụng để dự đoán trong một ứng dụng web. Sau khi mô hình được lưu, bạn sẽ học cách sử dụng nó trong một ứng dụng web được xây dựng bằng Flask. Đầu tiên, bạn sẽ tạo một mô hình sử dụng một số dữ liệu liên quan đến các lần nhìn thấy UFO! Sau đó, bạn sẽ xây dựng một ứng dụng web cho phép bạn nhập số giây cùng với giá trị vĩ độ và kinh độ để dự đoán quốc gia nào đã báo cáo nhìn thấy UFO. diff --git a/translations/vi/4-Classification/1-Introduction/README.md b/translations/vi/4-Classification/1-Introduction/README.md index f19d433ba..21e36f344 100644 --- a/translations/vi/4-Classification/1-Introduction/README.md +++ b/translations/vi/4-Classification/1-Introduction/README.md @@ -1,12 +1,3 @@ - # Giới thiệu về phân loại Trong bốn bài học này, bạn sẽ khám phá một trọng tâm cơ bản của học máy cổ điển - _phân loại_. Chúng ta sẽ cùng tìm hiểu cách sử dụng các thuật toán phân loại khác nhau với một tập dữ liệu về các món ăn tuyệt vời của châu Á và Ấn Độ. Hy vọng bạn đã sẵn sàng để thưởng thức! diff --git a/translations/vi/4-Classification/1-Introduction/assignment.md b/translations/vi/4-Classification/1-Introduction/assignment.md index daaf08ed4..c9cef8514 100644 --- a/translations/vi/4-Classification/1-Introduction/assignment.md +++ b/translations/vi/4-Classification/1-Introduction/assignment.md @@ -1,12 +1,3 @@ - # Khám phá các phương pháp phân loại ## Hướng dẫn diff --git a/translations/vi/4-Classification/1-Introduction/solution/Julia/README.md b/translations/vi/4-Classification/1-Introduction/solution/Julia/README.md index 6642af3cd..61484c254 100644 --- a/translations/vi/4-Classification/1-Introduction/solution/Julia/README.md +++ b/translations/vi/4-Classification/1-Introduction/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/vi/4-Classification/2-Classifiers-1/README.md b/translations/vi/4-Classification/2-Classifiers-1/README.md index 8960b806d..730cfb291 100644 --- a/translations/vi/4-Classification/2-Classifiers-1/README.md +++ b/translations/vi/4-Classification/2-Classifiers-1/README.md @@ -1,12 +1,3 @@ - # Bộ phân loại ẩm thực 1 Trong bài học này, bạn sẽ sử dụng tập dữ liệu mà bạn đã lưu từ bài học trước, chứa đầy dữ liệu cân bằng và sạch về các nền ẩm thực. diff --git a/translations/vi/4-Classification/2-Classifiers-1/assignment.md b/translations/vi/4-Classification/2-Classifiers-1/assignment.md index 94511b687..9c1698095 100644 --- a/translations/vi/4-Classification/2-Classifiers-1/assignment.md +++ b/translations/vi/4-Classification/2-Classifiers-1/assignment.md @@ -1,12 +1,3 @@ - # Nghiên cứu các bộ giải ## Hướng dẫn diff --git a/translations/vi/4-Classification/2-Classifiers-1/solution/Julia/README.md b/translations/vi/4-Classification/2-Classifiers-1/solution/Julia/README.md index 86c4d3ae8..61484c254 100644 --- a/translations/vi/4-Classification/2-Classifiers-1/solution/Julia/README.md +++ b/translations/vi/4-Classification/2-Classifiers-1/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/vi/4-Classification/3-Classifiers-2/README.md b/translations/vi/4-Classification/3-Classifiers-2/README.md index ced4deb9a..6cc3ddd80 100644 --- a/translations/vi/4-Classification/3-Classifiers-2/README.md +++ b/translations/vi/4-Classification/3-Classifiers-2/README.md @@ -1,12 +1,3 @@ - # Bộ phân loại ẩm thực 2 Trong bài học phân loại thứ hai này, bạn sẽ khám phá thêm các cách để phân loại dữ liệu số. Bạn cũng sẽ tìm hiểu về hậu quả của việc chọn một bộ phân loại này thay vì bộ phân loại khác. diff --git a/translations/vi/4-Classification/3-Classifiers-2/assignment.md b/translations/vi/4-Classification/3-Classifiers-2/assignment.md index c5e031650..1e66116cb 100644 --- a/translations/vi/4-Classification/3-Classifiers-2/assignment.md +++ b/translations/vi/4-Classification/3-Classifiers-2/assignment.md @@ -1,12 +1,3 @@ - # Chơi với Tham số ## Hướng dẫn diff --git a/translations/vi/4-Classification/3-Classifiers-2/solution/Julia/README.md b/translations/vi/4-Classification/3-Classifiers-2/solution/Julia/README.md index 4916dcc81..61484c254 100644 --- a/translations/vi/4-Classification/3-Classifiers-2/solution/Julia/README.md +++ b/translations/vi/4-Classification/3-Classifiers-2/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/vi/4-Classification/4-Applied/README.md b/translations/vi/4-Classification/4-Applied/README.md index 28b39e1a7..fa9c05019 100644 --- a/translations/vi/4-Classification/4-Applied/README.md +++ b/translations/vi/4-Classification/4-Applied/README.md @@ -1,12 +1,3 @@ - # Xây dựng ứng dụng web gợi ý món ăn Trong bài học này, bạn sẽ xây dựng một mô hình phân loại bằng cách sử dụng một số kỹ thuật đã học trong các bài trước và với bộ dữ liệu món ăn ngon được sử dụng xuyên suốt loạt bài này. Ngoài ra, bạn sẽ xây dựng một ứng dụng web nhỏ để sử dụng mô hình đã lưu, tận dụng runtime web của Onnx. diff --git a/translations/vi/4-Classification/4-Applied/assignment.md b/translations/vi/4-Classification/4-Applied/assignment.md index 0b30c8fa9..4f2e75293 100644 --- a/translations/vi/4-Classification/4-Applied/assignment.md +++ b/translations/vi/4-Classification/4-Applied/assignment.md @@ -1,12 +1,3 @@ - # Xây dựng hệ thống gợi ý ## Hướng dẫn diff --git a/translations/vi/4-Classification/README.md b/translations/vi/4-Classification/README.md index 5cfd8940e..eb0993a71 100644 --- a/translations/vi/4-Classification/README.md +++ b/translations/vi/4-Classification/README.md @@ -1,12 +1,3 @@ - # Bắt đầu với phân loại ## Chủ đề khu vực: Các món ăn ngon của châu Á và Ấn Độ 🍜 diff --git a/translations/vi/5-Clustering/1-Visualize/README.md b/translations/vi/5-Clustering/1-Visualize/README.md index 7f6318b96..2f46c7700 100644 --- a/translations/vi/5-Clustering/1-Visualize/README.md +++ b/translations/vi/5-Clustering/1-Visualize/README.md @@ -1,12 +1,3 @@ - # Giới thiệu về phân cụm Phân cụm là một loại [Học không giám sát](https://wikipedia.org/wiki/Unsupervised_learning) giả định rằng một tập dữ liệu không được gắn nhãn hoặc các đầu vào của nó không được liên kết với các đầu ra được định nghĩa trước. Nó sử dụng các thuật toán khác nhau để phân loại dữ liệu không gắn nhãn và cung cấp các nhóm dựa trên các mẫu mà nó nhận ra trong dữ liệu. diff --git a/translations/vi/5-Clustering/1-Visualize/assignment.md b/translations/vi/5-Clustering/1-Visualize/assignment.md index 62c05a647..c8967a9b5 100644 --- a/translations/vi/5-Clustering/1-Visualize/assignment.md +++ b/translations/vi/5-Clustering/1-Visualize/assignment.md @@ -1,12 +1,3 @@ - # Nghiên cứu các cách trực quan hóa khác cho phân cụm ## Hướng dẫn diff --git a/translations/vi/5-Clustering/1-Visualize/solution/Julia/README.md b/translations/vi/5-Clustering/1-Visualize/solution/Julia/README.md index c2e4fc8b8..61484c254 100644 --- a/translations/vi/5-Clustering/1-Visualize/solution/Julia/README.md +++ b/translations/vi/5-Clustering/1-Visualize/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/vi/5-Clustering/2-K-Means/README.md b/translations/vi/5-Clustering/2-K-Means/README.md index d298663a0..82dd17785 100644 --- a/translations/vi/5-Clustering/2-K-Means/README.md +++ b/translations/vi/5-Clustering/2-K-Means/README.md @@ -1,12 +1,3 @@ - # Phân cụm K-Means ## [Câu hỏi trước bài học](https://ff-quizzes.netlify.app/en/ml/) diff --git a/translations/vi/5-Clustering/2-K-Means/assignment.md b/translations/vi/5-Clustering/2-K-Means/assignment.md index 20ca9cbe4..f6f166550 100644 --- a/translations/vi/5-Clustering/2-K-Means/assignment.md +++ b/translations/vi/5-Clustering/2-K-Means/assignment.md @@ -1,12 +1,3 @@ - # Thử các phương pháp phân cụm khác ## Hướng dẫn diff --git a/translations/vi/5-Clustering/2-K-Means/solution/Julia/README.md b/translations/vi/5-Clustering/2-K-Means/solution/Julia/README.md index 7c88a2f5c..61484c254 100644 --- a/translations/vi/5-Clustering/2-K-Means/solution/Julia/README.md +++ b/translations/vi/5-Clustering/2-K-Means/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/vi/5-Clustering/README.md b/translations/vi/5-Clustering/README.md index 4b46dd7be..6da7c3f5a 100644 --- a/translations/vi/5-Clustering/README.md +++ b/translations/vi/5-Clustering/README.md @@ -1,12 +1,3 @@ - # Các mô hình phân cụm trong học máy Phân cụm là một nhiệm vụ trong học máy, nơi nó tìm cách xác định các đối tượng giống nhau và nhóm chúng lại thành các nhóm gọi là cụm. Điều làm phân cụm khác biệt so với các phương pháp khác trong học máy là mọi thứ diễn ra tự động, thực tế có thể nói rằng nó hoàn toàn trái ngược với học có giám sát. diff --git a/translations/vi/6-NLP/1-Introduction-to-NLP/README.md b/translations/vi/6-NLP/1-Introduction-to-NLP/README.md index d3ec866ea..2eee4c8c7 100644 --- a/translations/vi/6-NLP/1-Introduction-to-NLP/README.md +++ b/translations/vi/6-NLP/1-Introduction-to-NLP/README.md @@ -1,12 +1,3 @@ - # Giới thiệu về xử lý ngôn ngữ tự nhiên Bài học này bao gồm lịch sử ngắn gọn và các khái niệm quan trọng của *xử lý ngôn ngữ tự nhiên*, một lĩnh vực con của *ngôn ngữ học tính toán*. diff --git a/translations/vi/6-NLP/1-Introduction-to-NLP/assignment.md b/translations/vi/6-NLP/1-Introduction-to-NLP/assignment.md index 782b294db..e747e8f0a 100644 --- a/translations/vi/6-NLP/1-Introduction-to-NLP/assignment.md +++ b/translations/vi/6-NLP/1-Introduction-to-NLP/assignment.md @@ -1,12 +1,3 @@ - # Tìm kiếm một bot ## Hướng dẫn diff --git a/translations/vi/6-NLP/2-Tasks/README.md b/translations/vi/6-NLP/2-Tasks/README.md index ba5e0f0e4..bf677b65b 100644 --- a/translations/vi/6-NLP/2-Tasks/README.md +++ b/translations/vi/6-NLP/2-Tasks/README.md @@ -1,12 +1,3 @@ - # Các nhiệm vụ và kỹ thuật phổ biến trong xử lý ngôn ngữ tự nhiên Đối với hầu hết các nhiệm vụ *xử lý ngôn ngữ tự nhiên*, văn bản cần được xử lý phải được phân tích, kiểm tra, và kết quả được lưu trữ hoặc đối chiếu với các quy tắc và tập dữ liệu. Những nhiệm vụ này cho phép lập trình viên xác định _ý nghĩa_, _mục đích_, hoặc chỉ đơn giản là _tần suất_ của các thuật ngữ và từ trong văn bản. diff --git a/translations/vi/6-NLP/2-Tasks/assignment.md b/translations/vi/6-NLP/2-Tasks/assignment.md index 716c14909..2a48f620e 100644 --- a/translations/vi/6-NLP/2-Tasks/assignment.md +++ b/translations/vi/6-NLP/2-Tasks/assignment.md @@ -1,12 +1,3 @@ - # Làm cho Bot phản hồi ## Hướng dẫn diff --git a/translations/vi/6-NLP/3-Translation-Sentiment/README.md b/translations/vi/6-NLP/3-Translation-Sentiment/README.md index a467b2727..b7bf8e4ee 100644 --- a/translations/vi/6-NLP/3-Translation-Sentiment/README.md +++ b/translations/vi/6-NLP/3-Translation-Sentiment/README.md @@ -1,12 +1,3 @@ - # Dịch thuật và phân tích cảm xúc với ML Trong các bài học trước, bạn đã học cách xây dựng một bot cơ bản sử dụng `TextBlob`, một thư viện tích hợp ML phía sau để thực hiện các nhiệm vụ NLP cơ bản như trích xuất cụm danh từ. Một thách thức quan trọng khác trong ngôn ngữ học máy tính là việc dịch _chính xác_ một câu từ một ngôn ngữ nói hoặc viết sang một ngôn ngữ khác. diff --git a/translations/vi/6-NLP/3-Translation-Sentiment/assignment.md b/translations/vi/6-NLP/3-Translation-Sentiment/assignment.md index acde3369c..d732086c9 100644 --- a/translations/vi/6-NLP/3-Translation-Sentiment/assignment.md +++ b/translations/vi/6-NLP/3-Translation-Sentiment/assignment.md @@ -1,12 +1,3 @@ - # Giấy phép sáng tạo ## Hướng dẫn diff --git a/translations/vi/6-NLP/3-Translation-Sentiment/solution/Julia/README.md b/translations/vi/6-NLP/3-Translation-Sentiment/solution/Julia/README.md index 416a5169d..796bd4032 100644 --- a/translations/vi/6-NLP/3-Translation-Sentiment/solution/Julia/README.md +++ b/translations/vi/6-NLP/3-Translation-Sentiment/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/vi/6-NLP/3-Translation-Sentiment/solution/R/README.md b/translations/vi/6-NLP/3-Translation-Sentiment/solution/R/README.md index f6e03c95a..61484c254 100644 --- a/translations/vi/6-NLP/3-Translation-Sentiment/solution/R/README.md +++ b/translations/vi/6-NLP/3-Translation-Sentiment/solution/R/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/vi/6-NLP/4-Hotel-Reviews-1/README.md b/translations/vi/6-NLP/4-Hotel-Reviews-1/README.md index 6aec89eea..20e64a1f2 100644 --- a/translations/vi/6-NLP/4-Hotel-Reviews-1/README.md +++ b/translations/vi/6-NLP/4-Hotel-Reviews-1/README.md @@ -1,12 +1,3 @@ - # Phân tích cảm xúc với đánh giá khách sạn - xử lý dữ liệu Trong phần này, bạn sẽ sử dụng các kỹ thuật đã học ở các bài trước để thực hiện phân tích dữ liệu khám phá trên một tập dữ liệu lớn. Sau khi hiểu rõ về tính hữu ích của các cột khác nhau, bạn sẽ học: diff --git a/translations/vi/6-NLP/4-Hotel-Reviews-1/assignment.md b/translations/vi/6-NLP/4-Hotel-Reviews-1/assignment.md index cf4946177..f0a6e1085 100644 --- a/translations/vi/6-NLP/4-Hotel-Reviews-1/assignment.md +++ b/translations/vi/6-NLP/4-Hotel-Reviews-1/assignment.md @@ -1,12 +1,3 @@ - # NLTK ## Hướng dẫn diff --git a/translations/vi/6-NLP/4-Hotel-Reviews-1/solution/Julia/README.md b/translations/vi/6-NLP/4-Hotel-Reviews-1/solution/Julia/README.md index 1b35da686..5ad455fc3 100644 --- a/translations/vi/6-NLP/4-Hotel-Reviews-1/solution/Julia/README.md +++ b/translations/vi/6-NLP/4-Hotel-Reviews-1/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/vi/6-NLP/4-Hotel-Reviews-1/solution/R/README.md b/translations/vi/6-NLP/4-Hotel-Reviews-1/solution/R/README.md index 2fbf3e312..796bd4032 100644 --- a/translations/vi/6-NLP/4-Hotel-Reviews-1/solution/R/README.md +++ b/translations/vi/6-NLP/4-Hotel-Reviews-1/solution/R/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/vi/6-NLP/5-Hotel-Reviews-2/README.md b/translations/vi/6-NLP/5-Hotel-Reviews-2/README.md index 21fb2e1d8..02f04ad7a 100644 --- a/translations/vi/6-NLP/5-Hotel-Reviews-2/README.md +++ b/translations/vi/6-NLP/5-Hotel-Reviews-2/README.md @@ -1,12 +1,3 @@ - # Phân tích cảm xúc với đánh giá khách sạn Bây giờ bạn đã khám phá chi tiết bộ dữ liệu, đã đến lúc lọc các cột và sử dụng các kỹ thuật NLP trên bộ dữ liệu để thu thập những thông tin mới về các khách sạn. diff --git a/translations/vi/6-NLP/5-Hotel-Reviews-2/assignment.md b/translations/vi/6-NLP/5-Hotel-Reviews-2/assignment.md index 6dbf644d3..0b2ceb4a1 100644 --- a/translations/vi/6-NLP/5-Hotel-Reviews-2/assignment.md +++ b/translations/vi/6-NLP/5-Hotel-Reviews-2/assignment.md @@ -1,12 +1,3 @@ - # Thử một bộ dữ liệu khác ## Hướng dẫn diff --git a/translations/vi/6-NLP/5-Hotel-Reviews-2/solution/Julia/README.md b/translations/vi/6-NLP/5-Hotel-Reviews-2/solution/Julia/README.md index 3dbee5a19..796bd4032 100644 --- a/translations/vi/6-NLP/5-Hotel-Reviews-2/solution/Julia/README.md +++ b/translations/vi/6-NLP/5-Hotel-Reviews-2/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/vi/6-NLP/5-Hotel-Reviews-2/solution/R/README.md b/translations/vi/6-NLP/5-Hotel-Reviews-2/solution/R/README.md index 4d89340ae..61484c254 100644 --- a/translations/vi/6-NLP/5-Hotel-Reviews-2/solution/R/README.md +++ b/translations/vi/6-NLP/5-Hotel-Reviews-2/solution/R/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/vi/6-NLP/README.md b/translations/vi/6-NLP/README.md index 8345887fc..78870579a 100644 --- a/translations/vi/6-NLP/README.md +++ b/translations/vi/6-NLP/README.md @@ -1,12 +1,3 @@ - # Bắt đầu với xử lý ngôn ngữ tự nhiên Xử lý ngôn ngữ tự nhiên (NLP) là khả năng của một chương trình máy tính để hiểu ngôn ngữ con người khi nó được nói và viết - được gọi là ngôn ngữ tự nhiên. Đây là một thành phần của trí tuệ nhân tạo (AI). NLP đã tồn tại hơn 50 năm và có nguồn gốc từ lĩnh vực ngôn ngữ học. Toàn bộ lĩnh vực này hướng đến việc giúp máy móc hiểu và xử lý ngôn ngữ con người. Điều này sau đó có thể được sử dụng để thực hiện các nhiệm vụ như kiểm tra chính tả hoặc dịch máy. NLP có nhiều ứng dụng thực tế trong nhiều lĩnh vực, bao gồm nghiên cứu y học, công cụ tìm kiếm và trí tuệ kinh doanh. diff --git a/translations/vi/6-NLP/data/README.md b/translations/vi/6-NLP/data/README.md index 755891c09..52a7ea244 100644 --- a/translations/vi/6-NLP/data/README.md +++ b/translations/vi/6-NLP/data/README.md @@ -1,12 +1,3 @@ - Tải dữ liệu đánh giá khách sạn về thư mục này. --- diff --git a/translations/vi/7-TimeSeries/1-Introduction/README.md b/translations/vi/7-TimeSeries/1-Introduction/README.md index 57467f69e..7ce420844 100644 --- a/translations/vi/7-TimeSeries/1-Introduction/README.md +++ b/translations/vi/7-TimeSeries/1-Introduction/README.md @@ -1,12 +1,3 @@ - # Giới thiệu về dự đoán chuỗi thời gian ![Tóm tắt về chuỗi thời gian trong một bản vẽ phác thảo](../../../../sketchnotes/ml-timeseries.png) diff --git a/translations/vi/7-TimeSeries/1-Introduction/assignment.md b/translations/vi/7-TimeSeries/1-Introduction/assignment.md index 6dd51d30c..56177cb0e 100644 --- a/translations/vi/7-TimeSeries/1-Introduction/assignment.md +++ b/translations/vi/7-TimeSeries/1-Introduction/assignment.md @@ -1,12 +1,3 @@ - # Hình dung thêm một số chuỗi thời gian ## Hướng dẫn diff --git a/translations/vi/7-TimeSeries/1-Introduction/solution/Julia/README.md b/translations/vi/7-TimeSeries/1-Introduction/solution/Julia/README.md index 348950146..b1958eb0f 100644 --- a/translations/vi/7-TimeSeries/1-Introduction/solution/Julia/README.md +++ b/translations/vi/7-TimeSeries/1-Introduction/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/vi/7-TimeSeries/1-Introduction/solution/R/README.md b/translations/vi/7-TimeSeries/1-Introduction/solution/R/README.md index 5334ef5bd..61484c254 100644 --- a/translations/vi/7-TimeSeries/1-Introduction/solution/R/README.md +++ b/translations/vi/7-TimeSeries/1-Introduction/solution/R/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/vi/7-TimeSeries/2-ARIMA/README.md b/translations/vi/7-TimeSeries/2-ARIMA/README.md index 27b32131d..77e27a334 100644 --- a/translations/vi/7-TimeSeries/2-ARIMA/README.md +++ b/translations/vi/7-TimeSeries/2-ARIMA/README.md @@ -1,12 +1,3 @@ - # Dự đoán chuỗi thời gian với ARIMA Trong bài học trước, bạn đã tìm hiểu một chút về dự đoán chuỗi thời gian và tải một tập dữ liệu cho thấy sự biến động của tải điện qua một khoảng thời gian. diff --git a/translations/vi/7-TimeSeries/2-ARIMA/assignment.md b/translations/vi/7-TimeSeries/2-ARIMA/assignment.md index d9d67d769..d521df40f 100644 --- a/translations/vi/7-TimeSeries/2-ARIMA/assignment.md +++ b/translations/vi/7-TimeSeries/2-ARIMA/assignment.md @@ -1,12 +1,3 @@ - # Một mô hình ARIMA mới ## Hướng dẫn diff --git a/translations/vi/7-TimeSeries/2-ARIMA/solution/Julia/README.md b/translations/vi/7-TimeSeries/2-ARIMA/solution/Julia/README.md index ba0497e47..61484c254 100644 --- a/translations/vi/7-TimeSeries/2-ARIMA/solution/Julia/README.md +++ b/translations/vi/7-TimeSeries/2-ARIMA/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/vi/7-TimeSeries/2-ARIMA/solution/R/README.md b/translations/vi/7-TimeSeries/2-ARIMA/solution/R/README.md index 362b54678..61484c254 100644 --- a/translations/vi/7-TimeSeries/2-ARIMA/solution/R/README.md +++ b/translations/vi/7-TimeSeries/2-ARIMA/solution/R/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/vi/7-TimeSeries/3-SVR/README.md b/translations/vi/7-TimeSeries/3-SVR/README.md index 8a437bcf5..a1cbbb561 100644 --- a/translations/vi/7-TimeSeries/3-SVR/README.md +++ b/translations/vi/7-TimeSeries/3-SVR/README.md @@ -1,12 +1,3 @@ - # Dự đoán chuỗi thời gian với Support Vector Regressor Trong bài học trước, bạn đã học cách sử dụng mô hình ARIMA để dự đoán chuỗi thời gian. Bây giờ, bạn sẽ tìm hiểu về mô hình Support Vector Regressor, một mô hình hồi quy được sử dụng để dự đoán dữ liệu liên tục. diff --git a/translations/vi/7-TimeSeries/3-SVR/assignment.md b/translations/vi/7-TimeSeries/3-SVR/assignment.md index fed3b66c5..c43b54193 100644 --- a/translations/vi/7-TimeSeries/3-SVR/assignment.md +++ b/translations/vi/7-TimeSeries/3-SVR/assignment.md @@ -1,12 +1,3 @@ - # Một mô hình SVR mới ## Hướng dẫn [^1] diff --git a/translations/vi/7-TimeSeries/README.md b/translations/vi/7-TimeSeries/README.md index 7212413a3..92f9715d6 100644 --- a/translations/vi/7-TimeSeries/README.md +++ b/translations/vi/7-TimeSeries/README.md @@ -1,12 +1,3 @@ - # Giới thiệu về dự đoán chuỗi thời gian Dự đoán chuỗi thời gian là gì? Đó là việc dự đoán các sự kiện trong tương lai bằng cách phân tích xu hướng trong quá khứ. diff --git a/translations/vi/8-Reinforcement/1-QLearning/README.md b/translations/vi/8-Reinforcement/1-QLearning/README.md index 899cb5dda..6960e9b2e 100644 --- a/translations/vi/8-Reinforcement/1-QLearning/README.md +++ b/translations/vi/8-Reinforcement/1-QLearning/README.md @@ -1,12 +1,3 @@ - # Giới thiệu về Học Tăng Cường và Q-Learning ![Tóm tắt về học tăng cường trong học máy qua sketchnote](../../../../sketchnotes/ml-reinforcement.png) diff --git a/translations/vi/8-Reinforcement/1-QLearning/assignment.md b/translations/vi/8-Reinforcement/1-QLearning/assignment.md index 933dd26c7..ea7183b2c 100644 --- a/translations/vi/8-Reinforcement/1-QLearning/assignment.md +++ b/translations/vi/8-Reinforcement/1-QLearning/assignment.md @@ -1,12 +1,3 @@ - # Một Thế Giới Thực Tế Hơn Trong tình huống của chúng ta, Peter có thể di chuyển gần như không cảm thấy mệt mỏi hay đói. Trong một thế giới thực tế hơn, anh ấy cần phải ngồi xuống nghỉ ngơi thỉnh thoảng, và cũng cần ăn uống để duy trì sức khỏe. Hãy làm cho thế giới của chúng ta thực tế hơn bằng cách áp dụng các quy tắc sau: diff --git a/translations/vi/8-Reinforcement/1-QLearning/solution/Julia/README.md b/translations/vi/8-Reinforcement/1-QLearning/solution/Julia/README.md index 83be41b65..5ad455fc3 100644 --- a/translations/vi/8-Reinforcement/1-QLearning/solution/Julia/README.md +++ b/translations/vi/8-Reinforcement/1-QLearning/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/vi/8-Reinforcement/1-QLearning/solution/R/README.md b/translations/vi/8-Reinforcement/1-QLearning/solution/R/README.md index 38951fb48..61484c254 100644 --- a/translations/vi/8-Reinforcement/1-QLearning/solution/R/README.md +++ b/translations/vi/8-Reinforcement/1-QLearning/solution/R/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/vi/8-Reinforcement/2-Gym/README.md b/translations/vi/8-Reinforcement/2-Gym/README.md index e57e2dc3e..22497d223 100644 --- a/translations/vi/8-Reinforcement/2-Gym/README.md +++ b/translations/vi/8-Reinforcement/2-Gym/README.md @@ -1,12 +1,3 @@ - # Trượt ván CartPole Bài toán mà chúng ta đã giải trong bài học trước có vẻ như là một vấn đề đơn giản, không thực sự áp dụng được vào các tình huống thực tế. Nhưng thực tế không phải vậy, vì nhiều vấn đề trong thế giới thực cũng có kịch bản tương tự - bao gồm chơi cờ vua hoặc cờ vây. Chúng tương tự nhau vì chúng ta cũng có một bàn cờ với các quy tắc nhất định và một **trạng thái rời rạc**. diff --git a/translations/vi/8-Reinforcement/2-Gym/assignment.md b/translations/vi/8-Reinforcement/2-Gym/assignment.md index 61c772d32..904d6680d 100644 --- a/translations/vi/8-Reinforcement/2-Gym/assignment.md +++ b/translations/vi/8-Reinforcement/2-Gym/assignment.md @@ -1,12 +1,3 @@ - # Huấn luyện Mountain Car [OpenAI Gym](http://gym.openai.com) được thiết kế sao cho tất cả các môi trường đều cung cấp cùng một API - tức là các phương thức `reset`, `step` và `render`, cùng các khái niệm về **action space** và **observation space**. Do đó, có thể áp dụng cùng một thuật toán học tăng cường cho các môi trường khác nhau với ít thay đổi mã nguồn. diff --git a/translations/vi/8-Reinforcement/2-Gym/solution/Julia/README.md b/translations/vi/8-Reinforcement/2-Gym/solution/Julia/README.md index f2e66465a..61484c254 100644 --- a/translations/vi/8-Reinforcement/2-Gym/solution/Julia/README.md +++ b/translations/vi/8-Reinforcement/2-Gym/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/vi/8-Reinforcement/2-Gym/solution/R/README.md b/translations/vi/8-Reinforcement/2-Gym/solution/R/README.md index 6ba6178a2..61484c254 100644 --- a/translations/vi/8-Reinforcement/2-Gym/solution/R/README.md +++ b/translations/vi/8-Reinforcement/2-Gym/solution/R/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/vi/8-Reinforcement/README.md b/translations/vi/8-Reinforcement/README.md index 58dd9b343..908a98ca3 100644 --- a/translations/vi/8-Reinforcement/README.md +++ b/translations/vi/8-Reinforcement/README.md @@ -1,12 +1,3 @@ - # Giới thiệu về học tăng cường Học tăng cường, hay RL, được xem là một trong những mô hình học máy cơ bản, bên cạnh học có giám sát và học không giám sát. RL xoay quanh việc đưa ra quyết định: đưa ra quyết định đúng hoặc ít nhất là học hỏi từ những quyết định đã đưa ra. diff --git a/translations/vi/9-Real-World/1-Applications/README.md b/translations/vi/9-Real-World/1-Applications/README.md index 8b9c7c82f..380152867 100644 --- a/translations/vi/9-Real-World/1-Applications/README.md +++ b/translations/vi/9-Real-World/1-Applications/README.md @@ -1,12 +1,3 @@ - # Tái bút: Học máy trong thế giới thực ![Tóm tắt về học máy trong thế giới thực qua sketchnote](../../../../sketchnotes/ml-realworld.png) diff --git a/translations/vi/9-Real-World/1-Applications/assignment.md b/translations/vi/9-Real-World/1-Applications/assignment.md index c5e596f08..96c93f4df 100644 --- a/translations/vi/9-Real-World/1-Applications/assignment.md +++ b/translations/vi/9-Real-World/1-Applications/assignment.md @@ -1,12 +1,3 @@ - # Một Cuộc Săn Lùng ML ## Hướng dẫn diff --git a/translations/vi/9-Real-World/2-Debugging-ML-Models/README.md b/translations/vi/9-Real-World/2-Debugging-ML-Models/README.md index 6cae729c9..8f697326e 100644 --- a/translations/vi/9-Real-World/2-Debugging-ML-Models/README.md +++ b/translations/vi/9-Real-World/2-Debugging-ML-Models/README.md @@ -1,12 +1,3 @@ - # Tái bút: Gỡ lỗi mô hình trong Machine Learning bằng các thành phần của bảng điều khiển AI có trách nhiệm ## [Câu hỏi trước bài giảng](https://ff-quizzes.netlify.app/en/ml/) diff --git a/translations/vi/9-Real-World/2-Debugging-ML-Models/assignment.md b/translations/vi/9-Real-World/2-Debugging-ML-Models/assignment.md index 671b6c173..9e1903867 100644 --- a/translations/vi/9-Real-World/2-Debugging-ML-Models/assignment.md +++ b/translations/vi/9-Real-World/2-Debugging-ML-Models/assignment.md @@ -1,12 +1,3 @@ - # Khám phá bảng điều khiển AI có trách nhiệm (RAI) ## Hướng dẫn diff --git a/translations/vi/9-Real-World/README.md b/translations/vi/9-Real-World/README.md index 7961061df..d4269db66 100644 --- a/translations/vi/9-Real-World/README.md +++ b/translations/vi/9-Real-World/README.md @@ -1,12 +1,3 @@ - # Phụ lục: Ứng dụng thực tế của học máy cổ điển Trong phần này của chương trình học, bạn sẽ được giới thiệu về một số ứng dụng thực tế của học máy cổ điển. Chúng tôi đã tìm kiếm trên internet để thu thập các bài báo và tài liệu về những ứng dụng đã sử dụng các chiến lược này, tránh sử dụng mạng nơ-ron, học sâu và trí tuệ nhân tạo càng nhiều càng tốt. Tìm hiểu cách học máy được sử dụng trong hệ thống kinh doanh, ứng dụng sinh thái, tài chính, nghệ thuật và văn hóa, và nhiều lĩnh vực khác. diff --git a/translations/vi/AGENTS.md b/translations/vi/AGENTS.md index 9008e2e24..6a5d118e3 100644 --- a/translations/vi/AGENTS.md +++ b/translations/vi/AGENTS.md @@ -1,12 +1,3 @@ - # AGENTS.md ## Tổng quan dự án diff --git a/translations/vi/CODE_OF_CONDUCT.md b/translations/vi/CODE_OF_CONDUCT.md index 1c210f79c..91eaeff3a 100644 --- a/translations/vi/CODE_OF_CONDUCT.md +++ b/translations/vi/CODE_OF_CONDUCT.md @@ -1,12 +1,3 @@ - # Quy tắc ứng xử mã nguồn mở của Microsoft Dự án này đã áp dụng [Quy tắc ứng xử mã nguồn mở của Microsoft](https://opensource.microsoft.com/codeofconduct/). diff --git a/translations/vi/CONTRIBUTING.md b/translations/vi/CONTRIBUTING.md index ce8adaaed..9bce6a1fb 100644 --- a/translations/vi/CONTRIBUTING.md +++ b/translations/vi/CONTRIBUTING.md @@ -1,12 +1,3 @@ - # Đóng góp Dự án này hoan nghênh các đóng góp và gợi ý. Hầu hết các đóng góp yêu cầu bạn đồng ý với Thỏa thuận Giấy phép Người đóng góp (CLA), xác nhận rằng bạn có quyền và thực sự cấp cho chúng tôi quyền sử dụng đóng góp của bạn. Để biết thêm chi tiết, hãy truy cập https://cla.microsoft.com. diff --git a/translations/vi/README.md b/translations/vi/README.md index 5d2433235..c43110caa 100644 --- a/translations/vi/README.md +++ b/translations/vi/README.md @@ -1,232 +1,223 @@ - -[![GitHub license](https://img.shields.io/github/license/microsoft/ML-For-Beginners.svg)](https://github.com/microsoft/ML-For-Beginners/blob/master/LICENSE) -[![GitHub contributors](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/) -[![GitHub issues](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/) -[![GitHub pull-requests](https://img.shields.io/github/issues-pr/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/pulls/) -[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com) - -[![GitHub watchers](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/) -[![GitHub forks](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/) -[![GitHub stars](https://img.shields.io/github/stars/microsoft/ML-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/ML-For-Beginners/stargazers/) - -### 🌐 Hỗ trợ Đa Ngôn Ngữ - -#### Được hỗ trợ qua GitHub Action (Tự động & Luôn cập nhật) +[![Giấy phép GitHub](https://img.shields.io/github/license/microsoft/ML-For-Beginners.svg)](https://github.com/microsoft/ML-For-Beginners/blob/master/LICENSE) +[![Người đóng góp GitHub](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/) +[![Vấn đề GitHub](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/) +[![Yêu cầu kéo GitHub](https://img.shields.io/github/issues-pr/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/pulls/) +[![Chào đón PR](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com) + +[![Người theo dõi GitHub](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/) +[![Forks GitHub](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/) +[![Ngôi sao GitHub](https://img.shields.io/github/stars/microsoft/ML-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/ML-For-Beginners/stargazers/) + +### 🌐 Hỗ trợ đa ngôn ngữ + +#### Hỗ trợ qua GitHub Action (Tự động & Luôn cập nhật) -[Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](../bg/README.md) | [Burmese (Myanmar)](../my/README.md) | [Chinese (Simplified)](../zh/README.md) | [Chinese (Traditional, Hong Kong)](../hk/README.md) | [Chinese (Traditional, Macau)](../mo/README.md) | [Chinese (Traditional, Taiwan)](../tw/README.md) | [Croatian](../hr/README.md) | [Czech](../cs/README.md) | [Danish](../da/README.md) | [Dutch](../nl/README.md) | [Estonian](../et/README.md) | [Finnish](../fi/README.md) | [French](../fr/README.md) | [German](../de/README.md) | [Greek](../el/README.md) | [Hebrew](../he/README.md) | [Hindi](../hi/README.md) | [Hungarian](../hu/README.md) | [Indonesian](../id/README.md) | [Italian](../it/README.md) | [Japanese](../ja/README.md) | [Kannada](../kn/README.md) | [Korean](../ko/README.md) | [Lithuanian](../lt/README.md) | [Malay](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Nigerian Pidgin](../pcm/README.md) | [Norwegian](../no/README.md) | [Persian (Farsi)](../fa/README.md) | [Polish](../pl/README.md) | [Portuguese (Brazil)](../br/README.md) | [Portuguese (Portugal)](../pt/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romanian](../ro/README.md) | [Russian](../ru/README.md) | [Serbian (Cyrillic)](../sr/README.md) | [Slovak](../sk/README.md) | [Slovenian](../sl/README.md) | [Spanish](../es/README.md) | [Swahili](../sw/README.md) | [Swedish](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thai](../th/README.md) | [Turkish](../tr/README.md) | [Ukrainian](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamese](./README.md) +[Tiếng Ả rập](../ar/README.md) | [Tiếng Bengal](../bn/README.md) | [Tiếng Bungari](../bg/README.md) | [Tiếng Miến Điện (Myanmar)](../my/README.md) | [Tiếng Trung (Giản thể)](../zh-CN/README.md) | [Tiếng Trung (Phồn thể, Hồng Kông)](../zh-HK/README.md) | [Tiếng Trung (Phồn thể, Macau)](../zh-MO/README.md) | [Tiếng Trung (Phồn thể, Đài Loan)](../zh-TW/README.md) | [Tiếng Croatia](../hr/README.md) | [Tiếng Séc](../cs/README.md) | [Tiếng Đan Mạch](../da/README.md) | [Tiếng Hà Lan](../nl/README.md) | [Tiếng Estonia](../et/README.md) | [Tiếng Phần Lan](../fi/README.md) | [Tiếng Pháp](../fr/README.md) | [Tiếng Đức](../de/README.md) | [Tiếng Hy Lạp](../el/README.md) | [Tiếng Hebrew](../he/README.md) | [Tiếng Hindi](../hi/README.md) | [Tiếng Hungary](../hu/README.md) | [Tiếng Indonesia](../id/README.md) | [Tiếng Ý](../it/README.md) | [Tiếng Nhật](../ja/README.md) | [Tiếng Kannada](../kn/README.md) | [Tiếng Hàn](../ko/README.md) | [Tiếng Litva](../lt/README.md) | [Tiếng Mã Lai](../ms/README.md) | [Tiếng Malayalam](../ml/README.md) | [Tiếng Marathi](../mr/README.md) | [Tiếng Nepal](../ne/README.md) | [Tiếng Pidgin Nigeria](../pcm/README.md) | [Tiếng Na Uy](../no/README.md) | [Tiếng Ba Tư (Farsi)](../fa/README.md) | [Tiếng Ba Lan](../pl/README.md) | [Tiếng Bồ Đào Nha (Brazil)](../pt-BR/README.md) | [Tiếng Bồ Đào Nha (Bồ Đào Nha)](../pt-PT/README.md) | [Tiếng Punjabi (Gurmukhi)](../pa/README.md) | [Tiếng Romania](../ro/README.md) | [Tiếng Nga](../ru/README.md) | [Tiếng Serbian (Chữ Kirin)](../sr/README.md) | [Tiếng Slovakia](../sk/README.md) | [Tiếng Slovenia](../sl/README.md) | [Tiếng Tây Ban Nha](../es/README.md) | [Tiếng Swahili](../sw/README.md) | [Tiếng Thụy Điển](../sv/README.md) | [Tiếng Tagalog (Filipino)](../tl/README.md) | [Tiếng Tamil](../ta/README.md) | [Tiếng Telugu](../te/README.md) | [Tiếng Thái](../th/README.md) | [Tiếng Thổ Nhĩ Kỳ](../tr/README.md) | [Tiếng Ukraina](../uk/README.md) | [Tiếng Urdu](../ur/README.md) | [Tiếng Việt](./README.md) > **Ưa thích sao chép về máy?** -> Kho lưu trữ này bao gồm hơn 50 bản dịch ngôn ngữ, điều này làm tăng đáng kể kích thước tải xuống. Để sao chép mà không có bản dịch, hãy dùng sparse checkout: +> Kho lưu trữ này bao gồm hơn 50 bản dịch ngôn ngữ điều này làm tăng đáng kể kích thước tải xuống. Để sao chép mà không tải bản dịch, hãy sử dụng sparse checkout: > ```bash > git clone --filter=blob:none --sparse https://github.com/microsoft/ML-For-Beginners.git > cd ML-For-Beginners > git sparse-checkout set --no-cone '/*' '!translations' '!translated_images' > ``` -> Điều này cung cấp cho bạn mọi thứ bạn cần để hoàn thành khóa học với tốc độ tải xuống nhanh hơn nhiều. +> Điều này cung cấp cho bạn mọi thứ cần để hoàn thành khóa học với tốc độ tải nhanh hơn nhiều. -#### Tham gia Cộng đồng của Chúng tôi +#### Tham gia cộng đồng của chúng tôi [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -Chúng tôi có một chuỗi học tập trên Discord về AI đang diễn ra, tìm hiểu thêm và tham gia cùng chúng tôi tại [Learn with AI Series](https://aka.ms/learnwithai/discord) từ ngày 18 đến 30 tháng 9 năm 2025. Bạn sẽ nhận được mẹo và thủ thuật sử dụng GitHub Copilot cho Khoa học Dữ liệu. +Chúng tôi có một chuỗi học với AI đang diễn ra trên Discord, tìm hiểu thêm và tham gia cùng chúng tôi tại [Chuỗi học với AI](https://aka.ms/learnwithai/discord) từ ngày 18 - 30 tháng 9 năm 2025. Bạn sẽ nhận được các mẹo và thủ thuật sử dụng GitHub Copilot cho Khoa học dữ liệu. -![Chuỗi học cùng AI](../../../../translated_images/vi/3.9b58fd8d6c373c20.webp) +![Chuỗi học với AI](../../translated_images/vi/3.9b58fd8d6c373c20.webp) -# Máy Học cho Người Mới Bắt Đầu - Một Chương Trình Học +# Học Máy cho Người Mới Bắt Đầu - Một Chương Trình học -> 🌍 Du lịch khắp thế giới khi chúng ta khám phá Máy Học thông qua các nền văn hóa trên thế giới 🌍 +> 🌍 Du lịch vòng quanh thế giới khi chúng ta khám phá Học Máy thông qua các nền văn hóa thế giới 🌍 -Nhóm Cloud Advocates tại Microsoft vui mừng giới thiệu một chương trình học 12 tuần, gồm 26 bài học tất cả về **Máy Học**. Trong chương trình này, bạn sẽ tìm hiểu về cái mà đôi khi gọi là **máy học cổ điển**, chủ yếu sử dụng thư viện Scikit-learn và tránh học sâu, phần này được bao gồm trong chương trình [AI cho Người Mới Bắt Đầu](https://aka.ms/ai4beginners) của chúng tôi. Hãy kết hợp các bài học này với chương trình ['Khoa học Dữ liệu cho Người Mới Bắt Đầu'](https://aka.ms/ds4beginners) của chúng tôi nữa nhé! +Các Cloud Advocates tại Microsoft vui mừng cung cấp một chương trình học kéo dài 12 tuần, 26 bài học toàn diện về **Học Máy**. Trong chương trình này, bạn sẽ tìm hiểu về cái gọi là **học máy cổ điển**, chủ yếu sử dụng thư viện Scikit-learn và tránh học sâu, được đề cập trong chương trình học [AI cho Người Mới Bắt Đầu](https://aka.ms/ai4beginners) của chúng tôi. Kết hợp các bài học này với chương trình ['Khoa học Dữ liệu cho Người Mới Bắt Đầu'](https://aka.ms/ds4beginners) của chúng tôi nữa nhé! -Hãy cùng chúng tôi du lịch khắp thế giới khi áp dụng các kỹ thuật cổ điển này vào dữ liệu từ nhiều khu vực trên thế giới. Mỗi bài học bao gồm các bài kiểm tra trước và sau bài học, hướng dẫn viết để hoàn thành bài học, một giải pháp, một bài tập và nhiều hơn thế nữa. Phương pháp giảng dạy dựa trên dự án của chúng tôi cho phép bạn học trong khi xây dựng, một cách đã được chứng minh giúp kỹ năng mới "bám lâu". +Hãy đi du lịch cùng chúng tôi vòng quanh thế giới khi áp dụng các kỹ thuật cổ điển này vào dữ liệu từ nhiều vùng khác nhau trên thế giới. Mỗi bài học bao gồm bài kiểm tra trước và sau bài học, các hướng dẫn viết để hoàn thành bài học, một giải pháp, một bài tập, và nhiều hơn thế nữa. Phương pháp giảng dạy dựa trên dự án cho phép bạn học trong khi xây dựng, một cách đã được chứng minh để các kỹ năng mới 'bám sâu'. -**✍️ Lời cảm ơn sâu sắc đến các tác giả của chúng tôi** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, Ruth Yakubu và Amy Boyd +**✍️ Xin chân thành cảm ơn các tác giả của chúng tôi** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, Ruth Yakubu và Amy Boyd -**🎨 Cảm ơn cả các họa sĩ minh họa** Tomomi Imura, Dasani Madipalli, và Jen Looper +**🎨 Cảm ơn cũng gửi đến các họa sĩ minh họa** Tomomi Imura, Dasani Madipalli, và Jen Looper -**🙏 Lời cảm ơn đặc biệt 🙏 đến các đại sứ sinh viên Microsoft là tác giả, người đánh giá và đóng góp nội dung**, đặc biệt là Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila và Snigdha Agarwal +**🙏 Cảm ơn đặc biệt 🙏 đến các Microsoft Student Ambassador là tác giả, người đánh giá và đóng góp nội dung**, đặc biệt là Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, và Snigdha Agarwal -**🤩 Thêm lời biết ơn đến các đại sứ sinh viên Microsoft Eric Wanjau, Jasleen Sondhi, và Vidushi Gupta cho các bài học R của chúng tôi!** +**🤩 Thêm lòng biết ơn đến các Microsoft Student Ambassadors Eric Wanjau, Jasleen Sondhi, và Vidushi Gupta cho các bài học R của chúng tôi!** # Bắt đầu Thực hiện các bước sau: -1. **Fork Kho lưu trữ**: Nhấn nút "Fork" ở góc trên bên phải trang này. -2. **Clone Kho lưu trữ**: `git clone https://github.com/microsoft/ML-For-Beginners.git` +1. **Nhánh (Fork) kho lưu trữ**: Nhấn vào nút "Fork" ở góc trên bên phải của trang này. +2. **Sao chép kho lưu trữ**: `git clone https://github.com/microsoft/ML-For-Beginners.git` > [tìm tất cả tài nguyên bổ sung cho khóa học này trong bộ sưu tập Microsoft Learn của chúng tôi](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum) -> 🔧 **Cần giúp đỡ?** Hãy xem [Hướng dẫn Khắc phục Sự cố](TROUBLESHOOTING.md) của chúng tôi để có các giải pháp cho các vấn đề thường gặp về cài đặt, thiết lập và chạy bài học. +> 🔧 **Cần giúp đỡ?** Xem [Hướng dẫn khắc phục sự cố](TROUBLESHOOTING.md) của chúng tôi để tìm giải pháp cho các vấn đề phổ biến về cài đặt, thiết lập và chạy bài học. -**[Học sinh](https://aka.ms/student-page)**, để sử dụng chương trình này, hãy fork toàn bộ repo về tài khoản GitHub của bạn và hoàn thành các bài tập một mình hoặc theo nhóm: +**[Sinh viên](https://aka.ms/student-page)**, để sử dụng chương trình học này, hãy fork toàn bộ repo về tài khoản GitHub của bạn và hoàn thành các bài tập một mình hoặc theo nhóm: -- Bắt đầu với bài kiểm tra làm quen trước bài giảng. -- Đọc bài giảng và hoàn thành các hoạt động, dừng lại và suy nghĩ tại mỗi phần kiểm tra kiến thức. -- Cố gắng tạo các dự án bằng cách hiểu bài học hơn là chỉ chạy mã giải pháp; tuy nhiên mã đó có sẵn trong thư mục `/solution` của mỗi bài học theo dự án. +- Bắt đầu với một bài kiểm tra làm nóng trước bài giảng. +- Đọc bài giảng và hoàn thành các hoạt động, dừng lại và suy ngẫm ở mỗi phần kiểm tra kiến thức. +- Cố gắng tự tạo dự án bằng cách hiểu bài học thay vì chỉ chạy mã giải pháp; tuy nhiên mã giải pháp có sẵn trong thư mục `/solution` ở mỗi bài học dự án. - Làm bài kiểm tra sau bài giảng. - Hoàn thành thử thách. - Hoàn thành bài tập. -- Sau khi hoàn thành một nhóm bài học, hãy truy cập [Diễn đàn Thảo luận](https://github.com/microsoft/ML-For-Beginners/discussions) và "học tập công khai" bằng cách điền vào bảng đánh giá PAT thích hợp. 'PAT' là Công cụ Đánh Giá Tiến Trình, một bảng đánh giá bạn điền để nâng cao việc học của mình. Bạn cũng có thể phản hồi các PAT khác để chúng ta cùng học tập. +- Sau khi hoàn thành một nhóm bài học, hãy truy cập [Bảng Thảo luận](https://github.com/microsoft/ML-For-Beginners/discussions) và "thể hiện việc học" bằng cách điền vào biểu mẫu PAT phù hợp. 'PAT' là Công cụ Đánh giá Tiến độ mà bạn điền để thúc đẩy việc học của mình. Bạn cũng có thể phản ứng với các PAT khác để chúng ta cùng học hỏi. -> Để học nâng cao hơn, chúng tôi khuyên bạn làm theo các mô-đun và lộ trình học trên [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott). +> Để học sâu hơn, chúng tôi khuyên bạn nên theo dõi các mô-đun và lộ trình học trên [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott). -**Giáo viên**, chúng tôi có [bao gồm một số gợi ý](for-teachers.md) về cách sử dụng chương trình này. +**Giáo viên**, chúng tôi đã [bao gồm một số gợi ý](for-teachers.md) về cách sử dụng chương trình học này. --- ## Video hướng dẫn -Một số bài học có sẵn dưới dạng video ngắn. Bạn có thể tìm thấy tất cả trong bài học hoặc trên [playlists ML for Beginners trên kênh Microsoft Developer YouTube](https://aka.ms/ml-beginners-videos) bằng cách nhấp vào hình bên dưới. +Một số bài học có sẵn dưới dạng video ngắn. Bạn có thể tìm tất cả những video này trong bài học hoặc trên [Danh sách phát ML for Beginners trên kênh YouTube Microsoft Developer](https://aka.ms/ml-beginners-videos) bằng cách nhấp vào hình ảnh dưới đây. -[![ML for beginners banner](../../../../translated_images/vi/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos) +[![Biểu ngữ ML cho người mới bắt đầu](../../translated_images/vi/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos) --- -## Gặp gỡ Đội ngũ +## Gặp gỡ đội ngũ -[![Promo video](../../images/ml.gif)](https://youtu.be/Tj1XWrDSYJU) +[![Video quảng bá](../../images/ml.gif)](https://youtu.be/Tj1XWrDSYJU) **Gif bởi** [Mohit Jaisal](https://linkedin.com/in/mohitjaisal) -> 🎥 Nhấn vào hình trên để xem video về dự án và những người tạo ra nó! +> 🎥 Nhấp vào hình ảnh trên để xem video về dự án và những người đã tạo ra nó! --- ## Phương pháp giảng dạy -Chúng tôi đã chọn hai nguyên tắc sư phạm trong khi xây dựng chương trình này: đảm bảo rằng nó là **dự án thực hành** và bao gồm **các bài kiểm tra thường xuyên**. Thêm vào đó, chương trình có một **chủ đề chung** để tạo sự gắn kết. +Chúng tôi đã chọn hai nguyên tắc giáo dục khi xây dựng chương trình này: đảm bảo nó là dựa trên **dự án thực hành** và bao gồm **các bài kiểm tra thường xuyên**. Bên cạnh đó, chương trình có một **chủ đề chung** để tạo sự liên kết. -Bằng cách đảm bảo nội dung phù hợp với các dự án, quá trình học trở nên hấp dẫn hơn cho học sinh và khả năng ghi nhớ các khái niệm sẽ được tăng cường. Thêm nữa, bài kiểm tra mức độ thấp trước lớp đặt mục tiêu học tập cho học sinh về một chủ đề, trong khi bài kiểm tra thứ hai sau lớp đảm bảo ghi nhớ cao hơn. Chương trình này được thiết kế để linh hoạt và vui nhộn, có thể học toàn bộ hoặc từng phần. Các dự án bắt đầu nhỏ và trở nên phức tạp hơn dần cho đến cuối chu kỳ 12 tuần. Chương trình còn bao gồm phần mở rộng về ứng dụng thực tế của ML, có thể dùng như điểm thưởng hoặc làm cơ sở thảo luận. +Bằng cách đảm bảo nội dung phù hợp với các dự án, quá trình học tập trở nên hấp dẫn hơn đối với sinh viên và việc ghi nhớ các khái niệm sẽ được tăng cường. Thêm vào đó, một bài kiểm tra nhẹ trước lớp đặt ý định học tập cho sinh viên, trong khi bài kiểm tra thứ hai sau lớp đảm bảo việc ghi nhớ lâu dài hơn. Chương trình này được thiết kế linh hoạt và vui nhộn và có thể học toàn bộ hoặc một phần. Các dự án bắt đầu nhỏ và trở nên phức tạp hơn dần theo chu kỳ 12 tuần. Chương trình cũng bao gồm phần phụ lục về các ứng dụng thực tế của ML, có thể được sử dụng như tín chỉ bổ sung hoặc làm cơ sở cho thảo luận. -> Tìm các quy định về [Ứng xử](CODE_OF_CONDUCT.md), [Đóng góp](CONTRIBUTING.md), [Dịch thuật](TRANSLATIONS.md), và [Khắc phục sự cố](TROUBLESHOOTING.md) của chúng tôi. Chúng tôi hoan nghênh phản hồi mang tính xây dựng của bạn! +> Xem [Bộ Quy tắc Ứng xử](CODE_OF_CONDUCT.md), [Đóng góp](CONTRIBUTING.md), [Dịch thuật](TRANSLATIONS.md), và [Khắc phục sự cố](TROUBLESHOOTING.md) của chúng tôi. Chúng tôi hoan nghênh phản hồi mang tính xây dựng của bạn! ## Mỗi bài học bao gồm -- ghi chú sơ đồ tùy chọn -- video bổ trợ tùy chọn -- video hướng dẫn (một số bài học) -- [bài kiểm tra làm quen trước bài giảng](https://ff-quizzes.netlify.app/en/ml/) +- ghi chú phác thảo tùy chọn +- video bổ sung tùy chọn +- video hướng dẫn (chỉ một số bài) +- [bài kiểm tra làm nóng trước bài giảng](https://ff-quizzes.netlify.app/en/ml/) - bài học viết -- đối với bài học dự án, hướng dẫn từng bước cách xây dựng dự án +- đối với bài học dựa trên dự án, hướng dẫn từng bước về cách xây dựng dự án - kiểm tra kiến thức - thử thách -- đọc thêm bổ trợ +- đọc bổ sung - bài tập - [bài kiểm tra sau bài giảng](https://ff-quizzes.netlify.app/en/ml/) -> **Ghi chú về ngôn ngữ**: Các bài học chủ yếu được viết bằng Python, nhưng nhiều bài cũng có sẵn bằng R. Để hoàn thành bài học R, hãy vào thư mục `/solution` và tìm các bài học R. Chúng bao gồm phần mở rộng .rmd, là tệp **R Markdown** có thể hiểu đơn giản là nhúng `đoạn mã` (bằng R hoặc các ngôn ngữ khác) và `đầu đề YAML` (hướng dẫn cách định dạng đầu ra như PDF) trong một tài liệu `Markdown`. Do đó, nó là một khung tác giả mẫu mực cho khoa học dữ liệu vì cho phép bạn kết hợp mã, kết quả và suy nghĩ của mình bằng cách viết chúng trực tiếp trong Markdown. Hơn nữa, tài liệu R Markdown có thể được xuất ra định dạng PDF, HTML hoặc Word. -> **Lưu ý về các bài kiểm tra**: Tất cả các bài kiểm tra đều nằm trong [Thư mục Ứng dụng Quiz](../../quiz-app), với tổng cộng 52 bài kiểm tra, mỗi bài có ba câu hỏi. Chúng được liên kết trong các bài học nhưng ứng dụng quiz có thể chạy cục bộ; hãy làm theo hướng dẫn trong thư mục `quiz-app` để lưu trữ cục bộ hoặc triển khai lên Azure. - -| Số Bài Học | Chủ Đề | Nhóm Bài Học | Mục Tiêu Học Tập | Bài Học Liên Kết | Tác Giả | -| :---------: | :-------------------------------------------------------: | :---------------------------------------: | ----------------------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------: | -| 01 | Giới thiệu về học máy | [Giới thiệu](1-Introduction/README.md) | Hiểu các khái niệm cơ bản đằng sau học máy | [Bài học](1-Introduction/1-intro-to-ML/README.md) | Muhammad | -| 02 | Lịch sử của học máy | [Giới thiệu](1-Introduction/README.md) | Tìm hiểu lịch sử nền tảng của lĩnh vực này | [Bài học](1-Introduction/2-history-of-ML/README.md) | Jen và Amy | -| 03 | Công bằng và học máy | [Giới thiệu](1-Introduction/README.md) | Những vấn đề triết học quan trọng về sự công bằng mà sinh viên nên cân nhắc khi xây dựng và áp dụng mô hình ML là gì? | [Bài học](1-Introduction/3-fairness/README.md) | Tomomi | -| 04 | Các kỹ thuật học máy | [Giới thiệu](1-Introduction/README.md) | Các nhà nghiên cứu ML sử dụng những kỹ thuật nào để xây dựng mô hình ML? | [Bài học](1-Introduction/4-techniques-of-ML/README.md) | Chris và Jen | -| 05 | Giới thiệu về hồi quy | [Hồi quy](2-Regression/README.md) | Bắt đầu với Python và Scikit-learn cho các mô hình hồi quy | [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html) | Jen • Eric Wanjau | -| 06 | Giá bí ngô Bắc Mỹ 🎃 | [Hồi quy](2-Regression/README.md) | Trực quan hóa và làm sạch dữ liệu để chuẩn bị cho ML | [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html) | Jen • Eric Wanjau | -| 07 | Giá bí ngô Bắc Mỹ 🎃 | [Hồi quy](2-Regression/README.md) | Xây dựng các mô hình hồi quy tuyến tính và đa thức | [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html) | Jen và Dmitry • Eric Wanjau | -| 08 | Giá bí ngô Bắc Mỹ 🎃 | [Hồi quy](2-Regression/README.md) | Xây dựng mô hình hồi quy logistic | [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html) | Jen • Eric Wanjau | -| 09 | Ứng dụng Web 🔌 | [Ứng dụng Web](3-Web-App/README.md) | Xây dựng ứng dụng web để sử dụng mô hình đã huấn luyện | [Python](3-Web-App/1-Web-App/README.md) | Jen | -| 10 | Giới thiệu về phân loại | [Phân loại](4-Classification/README.md) | Làm sạch, chuẩn bị và trực quan hóa dữ liệu; giới thiệu về phân loại | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html) | Jen và Cassie • Eric Wanjau | -| 11 | Các món ăn ngon châu Á và Ấn Độ 🍜 | [Phân loại](4-Classification/README.md) | Giới thiệu về các bộ phân loại | [Python](4-Classification/2-Classifiers-1/README.md) • [R](../../4-Classification/2-Classifiers-1/solution/R/lesson_11.html) | Jen và Cassie • Eric Wanjau | -| 12 | Các món ăn ngon châu Á và Ấn Độ 🍜 | [Phân loại](4-Classification/README.md) | Thêm nhiều bộ phân loại hơn | [Python](4-Classification/3-Classifiers-2/README.md) • [R](../../4-Classification/3-Classifiers-2/solution/R/lesson_12.html) | Jen và Cassie • Eric Wanjau | -| 13 | Các món ăn ngon châu Á và Ấn Độ 🍜 | [Phân loại](4-Classification/README.md) | Xây dựng một ứng dụng gợi ý dựa trên mô hình của bạn | [Python](4-Classification/4-Applied/README.md) | Jen | -| 14 | Giới thiệu về phân cụm | [Phân cụm](5-Clustering/README.md) | Làm sạch, chuẩn bị và trực quan hóa dữ liệu; giới thiệu về phân cụm | [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html) | Jen • Eric Wanjau | -| 15 | Khám phá sở thích âm nhạc ở Nigeria 🎧 | [Phân cụm](5-Clustering/README.md) | Khám phá phương pháp phân cụm K-Means | [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html) | Jen • Eric Wanjau | -| 16 | Giới thiệu về xử lý ngôn ngữ tự nhiên ☕️ | [Xử lý ngôn ngữ tự nhiên](6-NLP/README.md) | Học những kiến thức cơ bản về NLP qua việc xây dựng một bot đơn giản | [Python](6-NLP/1-Introduction-to-NLP/README.md) | Stephen | -| 17 | Các tác vụ NLP phổ biến ☕️ | [Xử lý ngôn ngữ tự nhiên](6-NLP/README.md) | Nâng cao kiến thức NLP qua việc hiểu các tác vụ phổ biến khi làm việc với cấu trúc ngôn ngữ | [Python](6-NLP/2-Tasks/README.md) | Stephen | -| 18 | Dịch thuật và phân tích cảm xúc ♥️ | [Xử lý ngôn ngữ tự nhiên](6-NLP/README.md) | Dịch thuật và phân tích cảm xúc với Jane Austen | [Python](6-NLP/3-Translation-Sentiment/README.md) | Stephen | -| 19 | Khách sạn lãng mạn ở châu Âu ♥️ | [Xử lý ngôn ngữ tự nhiên](6-NLP/README.md) | Phân tích cảm xúc với nhận xét khách sạn 1 | [Python](6-NLP/4-Hotel-Reviews-1/README.md) | Stephen | -| 20 | Khách sạn lãng mạn ở châu Âu ♥️ | [Xử lý ngôn ngữ tự nhiên](6-NLP/README.md) | Phân tích cảm xúc với nhận xét khách sạn 2 | [Python](6-NLP/5-Hotel-Reviews-2/README.md) | Stephen | -| 21 | Giới thiệu dự báo chuỗi thời gian | [Chuỗi thời gian](7-TimeSeries/README.md) | Giới thiệu về dự báo chuỗi thời gian | [Python](7-TimeSeries/1-Introduction/README.md) | Francesca | -| 22 | ⚡️ Sử dụng điện năng thế giới ⚡️ - dự báo chuỗi thời gian ARIMA | [Chuỗi thời gian](7-TimeSeries/README.md) | Dự báo chuỗi thời gian với ARIMA | [Python](7-TimeSeries/2-ARIMA/README.md) | Francesca | -| 23 | ⚡️ Sử dụng điện năng thế giới ⚡️ - dự báo chuỗi thời gian SVR | [Chuỗi thời gian](7-TimeSeries/README.md) | Dự báo chuỗi thời gian với Hồi quy Vector Hỗ trợ | [Python](7-TimeSeries/3-SVR/README.md) | Anirban | -| 24 | Giới thiệu học tăng cường | [Học tăng cường](8-Reinforcement/README.md) | Giới thiệu về học tăng cường với Q-Learning | [Python](8-Reinforcement/1-QLearning/README.md) | Dmitry | -| 25 | Giúp Peter tránh con sói! 🐺 | [Học tăng cường](8-Reinforcement/README.md) | Học tăng cường Gym | [Python](8-Reinforcement/2-Gym/README.md) | Dmitry | -| Hậu ký | Các kịch bản và ứng dụng ML trong thực tế | [ML ngoài đời](9-Real-World/README.md) | Các ứng dụng thú vị và minh họa thực tế của ML cổ điển | [Bài học](9-Real-World/1-Applications/README.md) | Nhóm | -| Hậu ký | Gỡ lỗi mô hình trong ML qua dashboard RAI | [ML ngoài đời](9-Real-World/README.md) | Gỡ lỗi mô hình trong học máy sử dụng các thành phần dashboard Responsible AI | [Bài học](9-Real-World/2-Debugging-ML-Models/README.md) | Ruth Yakubu | - -> [tìm tất cả tài nguyên bổ sung cho khóa học này trong bộ sưu tập Microsoft Learn của chúng tôi](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum) +> **Lưu ý về ngôn ngữ**: Các bài học này chủ yếu viết bằng Python, nhưng nhiều bài cũng có sẵn bằng R. Để hoàn thành bài học R, hãy đi đến thư mục `/solution` và tìm các bài học R. Chúng bao gồm phần mở rộng .rmd là một tệp **R Markdown** có thể được hiểu đơn giản là sự kết hợp của các `đoạn mã` (R hoặc các ngôn ngữ khác) và `tiêu đề YAML` (hướng dẫn cách định dạng đầu ra như PDF) trong một `tài liệu Markdown`. Do đó, nó đóng vai trò như một khuôn khổ tác giả mẫu mực cho khoa học dữ liệu vì cho phép bạn kết hợp mã của mình, kết quả đầu ra, và suy nghĩ của bạn bằng cách viết chúng xuống dưới dạng Markdown. Hơn nữa, tài liệu R Markdown có thể được xuất ra các định dạng như PDF, HTML hoặc Word. +> **Một lưu ý về các bài kiểm tra**: Tất cả các bài kiểm tra đều nằm trong [thư mục Ứng dụng Quiz](../../quiz-app), tổng cộng 52 bài kiểm tra với mỗi bài gồm ba câu hỏi. Chúng được liên kết trong các bài học nhưng ứng dụng quiz có thể được chạy cục bộ; hãy làm theo hướng dẫn trong thư mục `quiz-app` để lưu trữ cục bộ hoặc triển khai lên Azure. + +| Lesson Number | Topic | Lesson Grouping | Learning Objectives | Linked Lesson | Author | +| :-----------: | :------------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------: | +| 01 | Giới thiệu về học máy (machine learning) | [Introduction](1-Introduction/README.md) | Học các khái niệm cơ bản đằng sau học máy | [Lesson](1-Introduction/1-intro-to-ML/README.md) | Muhammad | +| 02 | Lịch sử học máy | [Introduction](1-Introduction/README.md) | Tìm hiểu lịch sử của lĩnh vực này | [Lesson](1-Introduction/2-history-of-ML/README.md) | Jen và Amy | +| 03 | Công bằng và học máy | [Introduction](1-Introduction/README.md) | Những vấn đề triết học quan trọng về công bằng mà học viên nên cân nhắc khi xây dựng và áp dụng các mô hình ML? | [Lesson](1-Introduction/3-fairness/README.md) | Tomomi | +| 04 | Các kỹ thuật trong học máy | [Introduction](1-Introduction/README.md) | Các kỹ thuật mà các nhà nghiên cứu ML sử dụng để xây dựng mô hình ML là gì? | [Lesson](1-Introduction/4-techniques-of-ML/README.md) | Chris và Jen | +| 05 | Giới thiệu về hồi quy | [Regression](2-Regression/README.md) | Bắt đầu với Python và Scikit-learn cho các mô hình hồi quy | [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html) | Jen • Eric Wanjau | +| 06 | Giá bí ngô Bắc Mỹ 🎃 | [Regression](2-Regression/README.md) | Trực quan hóa và làm sạch dữ liệu chuẩn bị cho học máy | [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html) | Jen • Eric Wanjau | +| 07 | Giá bí ngô Bắc Mỹ 🎃 | [Regression](2-Regression/README.md) | Xây dựng các mô hình hồi quy tuyến tính và đa thức | [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html) | Jen và Dmitry • Eric Wanjau | +| 08 | Giá bí ngô Bắc Mỹ 🎃 | [Regression](2-Regression/README.md) | Xây dựng một mô hình hồi quy logistic | [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html) | Jen • Eric Wanjau | +| 09 | Ứng dụng Web 🔌 | [Web App](3-Web-App/README.md) | Xây dựng một ứng dụng web để sử dụng mô hình đã đào tạo | [Python](3-Web-App/1-Web-App/README.md) | Jen | +| 10 | Giới thiệu về phân loại | [Classification](4-Classification/README.md) | Làm sạch, chuẩn bị và trực quan hóa dữ liệu; giới thiệu về phân loại | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html) | Jen và Cassie • Eric Wanjau | +| 11 | Các món ăn ngon của châu Á và Ấn Độ 🍜 | [Classification](4-Classification/README.md) | Giới thiệu về các bộ phân loại | [Python](4-Classification/2-Classifiers-1/README.md) • [R](../../4-Classification/2-Classifiers-1/solution/R/lesson_11.html) | Jen và Cassie • Eric Wanjau | +| 12 | Các món ăn ngon của châu Á và Ấn Độ 🍜 | [Classification](4-Classification/README.md) | Thêm các bộ phân loại | [Python](4-Classification/3-Classifiers-2/README.md) • [R](../../4-Classification/3-Classifiers-2/solution/R/lesson_12.html) | Jen và Cassie • Eric Wanjau | +| 13 | Các món ăn ngon của châu Á và Ấn Độ 🍜 | [Classification](4-Classification/README.md) | Xây dựng một ứng dụng web đề xuất sử dụng mô hình của bạn | [Python](4-Classification/4-Applied/README.md) | Jen | +| 14 | Giới thiệu về phân cụm | [Clustering](5-Clustering/README.md) | Làm sạch, chuẩn bị và trực quan hóa dữ liệu; Giới thiệu về phân cụm | [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html) | Jen • Eric Wanjau | +| 15 | Khám phá gu âm nhạc Nigeria 🎧 | [Clustering](5-Clustering/README.md) | Khám phá phương pháp phân cụm K-Means | [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html) | Jen • Eric Wanjau | +| 16 | Giới thiệu về xử lý ngôn ngữ tự nhiên ☕️ | [Natural language processing](6-NLP/README.md) | Học các kiến thức cơ bản về NLP bằng cách xây dựng một bot đơn giản | [Python](6-NLP/1-Introduction-to-NLP/README.md) | Stephen | +| 17 | Các nhiệm vụ phổ biến trong NLP ☕️ | [Natural language processing](6-NLP/README.md) | Tăng cường kiến thức NLP bằng cách hiểu các nhiệm vụ phổ biến khi xử lý cấu trúc ngôn ngữ | [Python](6-NLP/2-Tasks/README.md) | Stephen | +| 18 | Dịch và phân tích cảm xúc ♥️ | [Natural language processing](6-NLP/README.md) | Dịch và phân tích cảm xúc với Jane Austen | [Python](6-NLP/3-Translation-Sentiment/README.md) | Stephen | +| 19 | Khách sạn lãng mạn ở châu Âu ♥️ | [Natural language processing](6-NLP/README.md) | Phân tích cảm xúc với đánh giá khách sạn 1 | [Python](6-NLP/4-Hotel-Reviews-1/README.md) | Stephen | +| 20 | Khách sạn lãng mạn ở châu Âu ♥️ | [Natural language processing](6-NLP/README.md) | Phân tích cảm xúc với đánh giá khách sạn 2 | [Python](6-NLP/5-Hotel-Reviews-2/README.md) | Stephen | +| 21 | Giới thiệu về dự báo chuỗi thời gian | [Time series](7-TimeSeries/README.md) | Giới thiệu về dự báo chuỗi thời gian | [Python](7-TimeSeries/1-Introduction/README.md) | Francesca | +| 22 | ⚡️ Mức sử dụng điện thế giới ⚡️ - dự báo chuỗi thời gian với ARIMA | [Time series](7-TimeSeries/README.md) | Dự báo chuỗi thời gian với ARIMA | [Python](7-TimeSeries/2-ARIMA/README.md) | Francesca | +| 23 | ⚡️ Mức sử dụng điện thế giới ⚡️ - dự báo chuỗi thời gian với SVR | [Time series](7-TimeSeries/README.md) | Dự báo chuỗi thời gian với Hồi quy vector hỗ trợ (Support Vector Regressor) | [Python](7-TimeSeries/3-SVR/README.md) | Anirban | +| 24 | Giới thiệu về học tăng cường | [Reinforcement learning](8-Reinforcement/README.md) | Giới thiệu về học tăng cường với Q-Learning | [Python](8-Reinforcement/1-QLearning/README.md) | Dmitry | +| 25 | Giúp Peter tránh con sói! 🐺 | [Reinforcement learning](8-Reinforcement/README.md) | Học tăng cường Gym | [Python](8-Reinforcement/2-Gym/README.md) | Dmitry | +| Postscript | Các kịch bản và ứng dụng ML trong thế giới thực | [ML in the Wild](9-Real-World/README.md) | Các ứng dụng ML cổ điển thú vị và mang tính khám phá trong thực tế | [Lesson](9-Real-World/1-Applications/README.md) | Team | +| Postscript | Gỡ lỗi mô hình trong ML sử dụng bảng điều khiển RAI | [ML in the Wild](9-Real-World/README.md) | Gỡ lỗi mô hình trong học máy sử dụng các thành phần bảng điều khiển Responsible AI | [Lesson](9-Real-World/2-Debugging-ML-Models/README.md) | Ruth Yakubu | + +> [tìm tất cả các tài nguyên bổ sung cho khóa học này trong bộ sưu tập Microsoft Learn của chúng tôi](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum) ## Truy cập ngoại tuyến -Bạn có thể chạy tài liệu này ngoại tuyến bằng cách sử dụng [Docsify](https://docsify.js.org/#/). Hãy fork repo này, [cài đặt Docsify](https://docsify.js.org/#/quickstart) trên máy của bạn, sau đó tại thư mục gốc của repo này, gõ `docsify serve`. Website sẽ được phục vụ trên cổng 3000 trên localhost của bạn: `localhost:3000`. +Bạn có thể chạy tài liệu này ngoại tuyến bằng cách sử dụng [Docsify](https://docsify.js.org/#/). Tạo nhánh (fork) repo này, [cài đặt Docsify](https://docsify.js.org/#/quickstart) trên máy của bạn, rồi trong thư mục gốc của repo này, gõ `docsify serve`. Website sẽ được phục vụ trên cổng 3000 tại localhost của bạn: `localhost:3000`. ## PDF -Tìm một file pdf của chương trình học kèm liên kết [tại đây](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf). +Tìm một file pdf của chương trình học có liên kết [tại đây](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf). -## 🎒 Các Khóa Học Khác +## 🎒 Các khóa học khác Nhóm của chúng tôi còn sản xuất các khóa học khác! Hãy xem: ### LangChain -[![LangChain4j cho Người mới bắt đầu](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners) -[![LangChain.js cho Người mới bắt đầu](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin) +[![LangChain4j for Beginners](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners) +[![LangChain.js for Beginners](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin) --- ### Azure / Edge / MCP / Agents -[![AZD cho Người mới bắt đầu](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst) -[![Edge AI cho Người mới bắt đầu](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst) -[![MCP cho Người mới bắt đầu](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst) -[![AI Agents cho Người mới bắt đầu](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst) +[![AZD for Beginners](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst) +[![Edge AI for Beginners](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst) +[![MCP for Beginners](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst) +[![AI Agents for Beginners](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst) --- -### Chuỗi bài AI Tạo Sinh -[![Trí tuệ nhân tạo tạo sinh cho Người mới bắt đầu](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst) -[![Trí tuệ nhân tạo tạo sinh (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst) -[![Trí tuệ nhân tạo tạo sinh (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst) -[![Trí tuệ nhân tạo tạo sinh (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst) +### Generative AI Series +[![Generative AI for Beginners](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst) +[![Generative AI (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst) +[![Generative AI (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst) +[![Generative AI (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst) --- - + ### Học Tập Cốt Lõi -[![Học máy cho Người mới bắt đầu](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst) -[![Khoa học Dữ liệu cho Người mới bắt đầu](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst) -[![Trí tuệ Nhân tạo cho Người mới bắt đầu](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst) -[![An ninh mạng cho Người mới bắt đầu](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung) -[![Phát triển Web cho Người mới bắt đầu](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst) -[![IoT cho Người mới bắt đầu](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst) -[![Phát triển XR cho Người mới bắt đầu](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst) +[![ML for Beginners](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst) +[![Data Science for Beginners](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst) +[![AI for Beginners](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst) +[![Cybersecurity for Beginners](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung) +[![Web Dev for Beginners](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst) +[![IoT for Beginners](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst) +[![XR Development for Beginners](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst) --- - -### Chuỗi Copilot -[![Copilot cho Lập trình Ghép cặp AI](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) -[![Copilot cho C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst) -[![Chuyến phiêu lưu Copilot](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst) + +### Series Copilot +[![Copilot for AI Paired Programming](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) +[![Copilot for C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst) +[![Copilot Adventure](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst) ## Nhận Trợ Giúp -Nếu bạn gặp khó khăn hoặc có bất kỳ câu hỏi nào về việc xây dựng ứng dụng AI. Hãy tham gia cùng những người học và các nhà phát triển có kinh nghiệm trong các cuộc thảo luận về MCP. Đây là một cộng đồng hỗ trợ, nơi các câu hỏi được hoan nghênh và kiến thức được chia sẻ tự do. +Nếu bạn bị mắc kẹt hoặc có bất kỳ câu hỏi nào về việc xây dựng các ứng dụng AI. Tham gia cùng những người học khác và các nhà phát triển giàu kinh nghiệm trong các cuộc thảo luận về MCP. Đây là một cộng đồng hỗ trợ, nơi các câu hỏi được hoan nghênh và kiến thức được chia sẻ tự do. [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -Nếu bạn có phản hồi về sản phẩm hoặc gặp lỗi trong quá trình xây dựng, vui lòng truy cập: +Nếu bạn có phản hồi về sản phẩm hoặc lỗi trong quá trình xây dựng, vui lòng truy cập: -[![Diễn đàn Nhà phát triển Microsoft Foundry](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) +[![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) --- -**Tuyên bố miễn trách nhiệm**: -Tài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn thông tin chính xác và đáng tin cậy. Đối với các thông tin quan trọng, nên sử dụng dịch vụ dịch thuật chuyên nghiệp do con người thực hiện. Chúng tôi không chịu trách nhiệm về bất kỳ sự hiểu lầm hoặc giải thích sai lệch nào phát sinh từ việc sử dụng bản dịch này. +**Tuyên bố miễn trừ trách nhiệm**: +Tài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi nỗ lực để đảm bảo độ chính xác, xin lưu ý rằng bản dịch tự động có thể chứa lỗi hoặc sai sót. Tài liệu gốc bằng ngôn ngữ gốc nên được xem là nguồn chính xác và đáng tin cậy. Đối với những thông tin quan trọng, nên sử dụng dịch vụ dịch thuật chuyên nghiệp do con người thực hiện. Chúng tôi không chịu trách nhiệm cho bất kỳ hiểu lầm hoặc giải thích sai nào phát sinh từ việc sử dụng bản dịch này. \ No newline at end of file diff --git a/translations/vi/SECURITY.md b/translations/vi/SECURITY.md index 40aaf0ce5..65b7c8c4a 100644 --- a/translations/vi/SECURITY.md +++ b/translations/vi/SECURITY.md @@ -1,12 +1,3 @@ - ## Bảo mật Microsoft coi trọng vấn đề bảo mật của các sản phẩm và dịch vụ phần mềm của mình, bao gồm tất cả các kho mã nguồn được quản lý thông qua các tổ chức GitHub của chúng tôi, bao gồm [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet), [Xamarin](https://github.com/xamarin), và [các tổ chức GitHub của chúng tôi](https://opensource.microsoft.com/). diff --git a/translations/vi/SUPPORT.md b/translations/vi/SUPPORT.md index 3f718dc3a..9ded6be05 100644 --- a/translations/vi/SUPPORT.md +++ b/translations/vi/SUPPORT.md @@ -1,12 +1,3 @@ - # Hỗ trợ ## Cách gửi vấn đề và nhận trợ giúp diff --git a/translations/vi/TROUBLESHOOTING.md b/translations/vi/TROUBLESHOOTING.md index 60c297b99..f78a5976a 100644 --- a/translations/vi/TROUBLESHOOTING.md +++ b/translations/vi/TROUBLESHOOTING.md @@ -1,12 +1,3 @@ - # Hướng Dẫn Khắc Phục Sự Cố Hướng dẫn này giúp bạn giải quyết các vấn đề thường gặp khi làm việc với chương trình học Machine Learning for Beginners. Nếu bạn không tìm thấy giải pháp ở đây, hãy kiểm tra [Thảo luận trên Discord](https://aka.ms/foundry/discord) hoặc [mở một vấn đề mới](https://github.com/microsoft/ML-For-Beginners/issues). diff --git a/translations/vi/docs/_sidebar.md b/translations/vi/docs/_sidebar.md index 4cf984c9e..92e16cf82 100644 --- a/translations/vi/docs/_sidebar.md +++ b/translations/vi/docs/_sidebar.md @@ -1,12 +1,3 @@ - - Giới thiệu - [Giới thiệu về Machine Learning](../1-Introduction/1-intro-to-ML/README.md) - [Lịch sử của Machine Learning](../1-Introduction/2-history-of-ML/README.md) diff --git a/translations/vi/for-teachers.md b/translations/vi/for-teachers.md index e5dddfda6..34ecddda3 100644 --- a/translations/vi/for-teachers.md +++ b/translations/vi/for-teachers.md @@ -1,12 +1,3 @@ - ## Dành cho giáo viên Bạn có muốn sử dụng chương trình học này trong lớp học của mình không? Hãy thoải mái sử dụng nhé! diff --git a/translations/vi/quiz-app/README.md b/translations/vi/quiz-app/README.md index 65d267b9c..495d9c775 100644 --- a/translations/vi/quiz-app/README.md +++ b/translations/vi/quiz-app/README.md @@ -1,12 +1,3 @@ - # Câu hỏi trắc nghiệm Các câu hỏi trắc nghiệm này là bài kiểm tra trước và sau bài giảng trong chương trình học ML tại https://aka.ms/ml-beginners diff --git a/translations/vi/sketchnotes/LICENSE.md b/translations/vi/sketchnotes/LICENSE.md index b9e4c0e33..df97552e5 100644 --- a/translations/vi/sketchnotes/LICENSE.md +++ b/translations/vi/sketchnotes/LICENSE.md @@ -1,12 +1,3 @@ - Quyền Chia Sẻ Tương Tự 4.0 Quốc Tế ======================================================================= diff --git a/translations/vi/sketchnotes/README.md b/translations/vi/sketchnotes/README.md index cbbf55c5f..bae1cfe6d 100644 --- a/translations/vi/sketchnotes/README.md +++ b/translations/vi/sketchnotes/README.md @@ -1,12 +1,3 @@ - Tất cả các bản vẽ minh họa của chương trình học có thể được tải xuống tại đây. 🖨 Để in với độ phân giải cao, các phiên bản TIFF có sẵn tại [repo này](https://github.com/girliemac/a-picture-is-worth-a-1000-words/tree/main/ml/tiff).