diff --git a/1-Introduction/1-intro-to-ML/README.md b/1-Introduction/1-intro-to-ML/README.md index a6821ee4..21864e94 100644 --- a/1-Introduction/1-intro-to-ML/README.md +++ b/1-Introduction/1-intro-to-ML/README.md @@ -19,4 +19,6 @@ Before embarking on this curriculum, you need to have your computer set up and r ## Review & Self Study -**Assignment**: [Get Up and Running](assignment.md) +## Assignment + +[Get Up and Running](assignment.md) diff --git a/1-Introduction/2-history-of-ML/README.md b/1-Introduction/2-history-of-ML/README.md index f36a4525..6bd91ada 100644 --- a/1-Introduction/2-history-of-ML/README.md +++ b/1-Introduction/2-history-of-ML/README.md @@ -93,4 +93,6 @@ Here are items to watch and listen to: [![The history of AI by Amy Boyd](https://img.youtube.com/vi/EJt3_bFYKss/0.jpg)](https://www.youtube.com/watch?v=EJt3_bFYKss "The history of AI by Amy Boyd") -**Assignment**: [Create a timeline](assignment.md) +## Assignment + +[Create a timeline](assignment.md) diff --git a/1-Introduction/3-fairness/README.md b/1-Introduction/3-fairness/README.md index f7beccf8..88b2b81f 100644 --- a/1-Introduction/3-fairness/README.md +++ b/1-Introduction/3-fairness/README.md @@ -192,4 +192,6 @@ Read about Azure Machine Learning's tools to ensure fairness - [Azure Machine Learning](https://docs.microsoft.com/en-us/azure/machine-learning/concept-fairness-ml?WT.mc_id=academic-15963-cxa) -## [Explore Fairlearn](assignment.md) +## Assignment + +[Explore Fairlearn](assignment.md) diff --git a/2-Regression/1-Tools/README.md b/2-Regression/1-Tools/README.md index be2fd391..be8d299e 100644 --- a/2-Regression/1-Tools/README.md +++ b/2-Regression/1-Tools/README.md @@ -187,4 +187,6 @@ In this tutorial, you worked with simple linear regression, rather than univaria Read more about the concept of Regression and think about what kinds of questions can be answered by this technique. Take this [tutorial](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-15963-cxa) to deepen your understanding. -**Assignment**: [A different dataset](assignment.md) +## Assignment + +[A different dataset](assignment.md) diff --git a/2-Regression/2-Data/README.md b/2-Regression/2-Data/README.md index 357d8c20..ef3e2fe8 100644 --- a/2-Regression/2-Data/README.md +++ b/2-Regression/2-Data/README.md @@ -149,4 +149,6 @@ Explore the different types of visualization that matplotlib offers. Which types Take a look at the many ways to visualize data. Make a list of the various libraries available and note which are best for given types of tasks, for example 2D visualizations vs. 3D visualizations. What do you discover? -**Assignment**: [Exploring visualization](assignment.md) +## Assignment + +[Exploring visualization](assignment.md) diff --git a/2-Regression/3-Linear/README.md b/2-Regression/3-Linear/README.md index 2dd98428..f2612ac5 100644 --- a/2-Regression/3-Linear/README.md +++ b/2-Regression/3-Linear/README.md @@ -255,4 +255,6 @@ Test several different variables in this notebook to see how correlation corresp In this lesson we learned about Linear Regression. There are other important types of Regression. Read about Stepwise, Ridge, Lasso and Elasticnet techniques. A good course to study to learn more is the [Stanford Statistical Learning course](https://online.stanford.edu/courses/sohs-ystatslearning-statistical-learning) -**Assignment**: [Build a Model](assignment.md) +## Assignment + +[Build a Model](assignment.md) diff --git a/2-Regression/4-Logistic/README.md b/2-Regression/4-Logistic/README.md index 66bf5a7b..628b0a29 100644 --- a/2-Regression/4-Logistic/README.md +++ b/2-Regression/4-Logistic/README.md @@ -256,4 +256,6 @@ There's a lot more to unpack regarding Logistic Regression! But the best way to Read the first few pages of [this paper from Stanford](https://web.stanford.edu/~jurafsky/slp3/5.pdf) on some practical uses for Logistic Regression. Think about tasks that are better suited for one or the other type of Regression tasks that we have studied up to this point. What would work best? -**Assignment**: [Retrying this Regression](assignment.md) +## Assignment + +[Retrying this Regression](assignment.md) diff --git a/3-Web-App/1-Web-App/README.md b/3-Web-App/1-Web-App/README.md index 57775db0..64e2d35f 100644 --- a/3-Web-App/1-Web-App/README.md +++ b/3-Web-App/1-Web-App/README.md @@ -273,6 +273,8 @@ Instead of working in a notebook and importing the model to the Flask app, you c There are many ways to build a web app to consume ML models. Make a list of the ways you could use JavaScript or Python to build a web app to leverage machine learning. Consider architecture: should the model stay in the app or live in the cloud? If the latter, how would you access it? Draw out an architectural model for an applied ML web solution. -**Assignment**: [Try a different model](assignment.md) +## Assignment + +[Try a different model](assignment.md) diff --git a/4-Classification/1-Data/README.md b/4-Classification/1-Data/README.md index 24dc19af..1a899428 100644 --- a/4-Classification/1-Data/README.md +++ b/4-Classification/1-Data/README.md @@ -51,4 +51,6 @@ Optional: add a screenshot of the completed lesson's UI if appropriate ## Review & Self Study -**Assignment**: [Assignment Name](assignment.md) +## Assignment + +[Assignment Name](assignment.md) diff --git a/4-Classification/2-Discriminative/README.md b/4-Classification/2-Discriminative/README.md index 029678ca..7ac65b4c 100644 --- a/4-Classification/2-Discriminative/README.md +++ b/4-Classification/2-Discriminative/README.md @@ -52,4 +52,6 @@ Optional: add a screenshot of the completed lesson's UI if appropriate ## Review & Self Study -**Assignment**: [Assignment Name](assignment.md) +## Assignment + +[Assignment Name](assignment.md) diff --git a/4-Classification/3-Generative/README.md b/4-Classification/3-Generative/README.md index 029678ca..7ac65b4c 100644 --- a/4-Classification/3-Generative/README.md +++ b/4-Classification/3-Generative/README.md @@ -52,4 +52,6 @@ Optional: add a screenshot of the completed lesson's UI if appropriate ## Review & Self Study -**Assignment**: [Assignment Name](assignment.md) +## Assignment + +[Assignment Name](assignment.md) diff --git a/4-Classification/4-Applied/README.md b/4-Classification/4-Applied/README.md index 029678ca..7ac65b4c 100644 --- a/4-Classification/4-Applied/README.md +++ b/4-Classification/4-Applied/README.md @@ -52,4 +52,6 @@ Optional: add a screenshot of the completed lesson's UI if appropriate ## Review & Self Study -**Assignment**: [Assignment Name](assignment.md) +## Assignment + +[Assignment Name](assignment.md) diff --git a/5-Clustering/1-Visualize/README.md b/5-Clustering/1-Visualize/README.md index b6e62125..695d4847 100644 --- a/5-Clustering/1-Visualize/README.md +++ b/5-Clustering/1-Visualize/README.md @@ -312,4 +312,6 @@ Before you apply clustering algorithms, as we have learned, it's a good idea to [This helpful article](https://www.freecodecamp.org/news/8-clustering-algorithms-in-machine-learning-that-all-data-scientists-should-know/) walks you through the different ways that various clustering algorithms behave, given different data shapes. -**Assignment**: [Research other visualizations for clustering](assignment.md) +## Assignment + +[Research other visualizations for clustering](assignment.md) diff --git a/5-Clustering/2-K-Means/README.md b/5-Clustering/2-K-Means/README.md index f5a7aa64..667c1bce 100644 --- a/5-Clustering/2-K-Means/README.md +++ b/5-Clustering/2-K-Means/README.md @@ -6,9 +6,12 @@ ## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/27/) -In this lesson, you will learn how to create clusters using Scikit-Learn and the Nigerian music dataset you imported earlier. We will cover +In this lesson, you will learn how to create clusters using Scikit-Learn and the Nigerian music dataset you imported earlier. We will cover the basics of K-Means for Clustering. Keep in mind that, as you learned in the earlier lesson, there are many ways to work with clusters and the method you use depends on your data. We will try K-Means as it's the most common Clustering technique. Let's get started! - Data variance +- Silhouette Scoring +- Elbow Method +- K-Means for Clustering ### Introduction @@ -17,7 +20,9 @@ In this lesson, you will learn how to create clusters using Scikit-Learn and the Preparatory steps to start this lesson +### Silhouette score +"The value of the Silhouette score varies from -1 to 1. If the score is 1, the cluster is dense and well-separated than other clusters. A value near 0 represents overlapping clusters with samples very close to the decision boundary of the neighboring clusters. A negative score [-1, 0] indicates that the samples might have got assigned to the wrong clusters." - https://dzone.com/articles/kmeans-silhouette-score-explained-with-python-exam ✅ Knowledge Check - use this moment to stretch students' knowledge with open questions @@ -25,9 +30,7 @@ Preparatory steps to start this lesson ## 🚀Challenge Spend some time with this notebook, tweaking parameters. Can you improve the accuracy of the model by cleaning the data more (removing outliers, for example)? What else can you do to create better clusters? - ## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/28/) - ## Review & Self Study Take a look at Stanford's K-Means Simulator [here](https://stanford.edu/class/engr108/visualizations/kmeans/kmeans.html). You can use this tool to visualize sample data points and determine its centroids. With fresh data, click 'update' to see how long it takes to find convergence. You can edit the data's randomness, numbers of clusters and numbers of centroids. Does this help you get an idea of how the data can be grouped? @@ -35,4 +38,6 @@ Take a look at Stanford's K-Means Simulator [here](https://stanford.edu/class/en Also, take a look at [this handout on k-means](https://stanford.edu/~cpiech/cs221/handouts/kmeans.html ) from Stanford -**Assignment**: [Try different clustering methods](assignment.md) +## Assignment + +[Try different clustering methods](assignment.md) diff --git a/5-Clustering/2-K-Means/solution/notebook.ipynb b/5-Clustering/2-K-Means/solution/notebook.ipynb index 39aa3d24..87ac6e70 100644 --- a/5-Clustering/2-K-Means/solution/notebook.ipynb +++ b/5-Clustering/2-K-Means/solution/notebook.ipynb @@ -369,9 +369,7 @@ }, { "source": [ - "Those numbers don't mean much to us, so let's get a 'silhouette score' to see the accuracy. \"The value of the Silhouette score varies from -1 to 1. If the score is 1, the cluster is dense and well-separated than other clusters. A value near 0 represents overlapping clusters with samples very close to the decision boundary of the neighboring clusters. A negative score [-1, 0] indicates that the samples might have got assigned to the wrong clusters.\" - https://dzone.com/articles/kmeans-silhouette-score-explained-with-python-exam\n", - "\n", - "Our score is in the middle" + "Those numbers don't mean much to us, so let's get a 'silhouette score' to see the accuracy. Our score is in the middle." ], "cell_type": "markdown", "metadata": {} diff --git a/6-NLP/1-Introduction-to-NLP/README.md b/6-NLP/1-Introduction-to-NLP/README.md index 84d35a43..815e27f2 100644 --- a/6-NLP/1-Introduction-to-NLP/README.md +++ b/6-NLP/1-Introduction-to-NLP/README.md @@ -124,4 +124,6 @@ Take a look at the references below as further reading opportunities. 1. Schubert, Lenhart, "Computational Linguistics", *The Stanford Encyclopedia of Philosophy* (Spring 2020 Edition), Edward N. Zalta (ed.), URL = . 2. Princeton University "About WordNet." [WordNet](https://wordnet.princeton.edu/). Princeton University. 2010. -**Assignment**: [Search for a Bot](assignment.md) +## Assignment + +[Search for a Bot](assignment.md) diff --git a/6-NLP/2-Tasks/README.md b/6-NLP/2-Tasks/README.md index c9786e61..e4749561 100644 --- a/6-NLP/2-Tasks/README.md +++ b/6-NLP/2-Tasks/README.md @@ -185,4 +185,6 @@ Take a task in the prior knowledge check and try to implement it. Test the bot o In the next few lessons you will learn more about sentiment analysis. Research this interesting technique in articles such as these on [KDNuggets](https://www.kdnuggets.com/tag/nlp) -**Assignment**: [Make a bot talk back](assignment.md) +## Assignment + +[Make a bot talk back](assignment.md) diff --git a/6-NLP/3-Translation-Sentiment/README.md b/6-NLP/3-Translation-Sentiment/README.md index e47b8d3a..7524a07f 100644 --- a/6-NLP/3-Translation-Sentiment/README.md +++ b/6-NLP/3-Translation-Sentiment/README.md @@ -144,4 +144,6 @@ Can you make Marvin even better by extracting other features from the user input There are many ways to extract sentiment from text. Think of the business applications that might make use of this technique. Think about how it can go awry. Read more about sophisticated enterprise-ready systems that analyze sentiment such as [Azure Text Analysis](https://docs.microsoft.com/en-us/azure/cognitive-services/Text-Analytics/how-tos/text-analytics-how-to-sentiment-analysis?tabs=version-3-1?WT.mc_id=academic-15963-cxa). Test some of the Pride and Prejudice sentences above and see if it can detect nuance. -**Assignment**: [Poetic License](assignment.md) +## Assignment + +[Poetic License](assignment.md) diff --git a/6-NLP/4-Hotel-Reviews-1/README.md b/6-NLP/4-Hotel-Reviews-1/README.md index 029678ca..f845bfcb 100644 --- a/6-NLP/4-Hotel-Reviews-1/README.md +++ b/6-NLP/4-Hotel-Reviews-1/README.md @@ -52,4 +52,4 @@ Optional: add a screenshot of the completed lesson's UI if appropriate ## Review & Self Study -**Assignment**: [Assignment Name](assignment.md) +## Assignment [Assignment Name](assignment.md) diff --git a/6-NLP/5-Hotel-Reviews-2/README.md b/6-NLP/5-Hotel-Reviews-2/README.md index 029678ca..f845bfcb 100644 --- a/6-NLP/5-Hotel-Reviews-2/README.md +++ b/6-NLP/5-Hotel-Reviews-2/README.md @@ -52,4 +52,4 @@ Optional: add a screenshot of the completed lesson's UI if appropriate ## Review & Self Study -**Assignment**: [Assignment Name](assignment.md) +## Assignment [Assignment Name](assignment.md) diff --git a/7-TimeSeries/1-Introduction/README.md b/7-TimeSeries/1-Introduction/README.md index f894fb31..0c6d36c8 100644 --- a/7-TimeSeries/1-Introduction/README.md +++ b/7-TimeSeries/1-Introduction/README.md @@ -150,4 +150,6 @@ Make a list of all the industries and areas of inquiry you can think of that wou Although we won't cover them here, neural networks are sometimes used to enhance classic methods of Time Series Forecasting. Read more about them [in this article](https://medium.com/microsoftazure/neural-networks-for-forecasting-financial-and-economic-time-series-6aca370ff412) -**Assignment**: [Visualize some more Time Series](assignment.md) +## Assignment + +[Visualize some more Time Series](assignment.md) diff --git a/7-TimeSeries/2-ARIMA/README.md b/7-TimeSeries/2-ARIMA/README.md index 53d7fb66..11f2c7e2 100644 --- a/7-TimeSeries/2-ARIMA/README.md +++ b/7-TimeSeries/2-ARIMA/README.md @@ -357,4 +357,6 @@ Dig into the ways to test the accuracy of a Time Series Model. We touch on MAPE This lesson touches on only the basics of Time Series Forecasting with ARIMA. Take some time to deepen your knowledge by digging into [this repository](https://microsoft.github.io/forecasting/) and its various model types to learn other ways to build Time Series models. -**Assignment**: [A new ARIMA model](assignment.md) +## Assignment + +[A new ARIMA model](assignment.md) diff --git a/8-Reinforcement/1-Concepts/README.md b/8-Reinforcement/1-Concepts/README.md index 3598a41a..ea3dc76d 100644 --- a/8-Reinforcement/1-Concepts/README.md +++ b/8-Reinforcement/1-Concepts/README.md @@ -52,4 +52,4 @@ Optional: add a screenshot of the completed lesson's UI if appropriate ## Review & Self Study -**Assignment**: [Assignment Name](assignment.md) +## Assignment [Assignment Name](assignment.md) diff --git a/8-Reinforcement/2-Build/README.md b/8-Reinforcement/2-Build/README.md index 029678ca..f845bfcb 100644 --- a/8-Reinforcement/2-Build/README.md +++ b/8-Reinforcement/2-Build/README.md @@ -52,4 +52,4 @@ Optional: add a screenshot of the completed lesson's UI if appropriate ## Review & Self Study -**Assignment**: [Assignment Name](assignment.md) +## Assignment [Assignment Name](assignment.md) diff --git a/9-Real-World/1-Applications/README.md b/9-Real-World/1-Applications/README.md index 89b02e9f..a25eeb65 100644 --- a/9-Real-World/1-Applications/README.md +++ b/9-Real-World/1-Applications/README.md @@ -133,4 +133,6 @@ Discover one more sector that benefits from some of the techniques you learned i Wayfair Data Science group has several interesting videos on how they use ML in their company. It's worth [taking a look](https://www.youtube.com/channel/UCe2PjkQXqOuwkW1gw6Ameuw/videos)! -**Assignment**: [A ML scavenger hunt](assignment.md) +## Assignment + +[A ML scavenger hunt](assignment.md)