From 6998354fbbcabf8910391e3d50a9def1f475d094 Mon Sep 17 00:00:00 2001 From: Abhinav Sharma <63901956+abhi-bhatra@users.noreply.github.com> Date: Wed, 28 Jul 2021 19:12:54 +0530 Subject: [PATCH 1/8] Update README.md --- 2-Regression/4-Logistic/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/2-Regression/4-Logistic/README.md b/2-Regression/4-Logistic/README.md index a4488c11e..f694a554a 100644 --- a/2-Regression/4-Logistic/README.md +++ b/2-Regression/4-Logistic/README.md @@ -284,7 +284,7 @@ In future lessons on classifications, you will learn how to iterate to improve y --- ## 🚀Challenge -There's a lot more to unpack regarding logistic regression! But the best way to learn is to experiment. Find a dataset that lends itself to this type of analysis and build a model with it. What do you learn? tip: try [Kaggle](https://kaggle.com) for interesting datasets. +There's a lot more to unpack regarding logistic regression! But the best way to learn is to experiment. Find a dataset that lends itself to this type of analysis and build a model with it. What do you learn? tip: try [Kaggle](https://www.kaggle.com/search?q=logistic+regression+datasets) for interesting datasets. ## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/16/) ## Review & Self Study From f744fec54a47c1201ca8dcd7b0ca3f3dfb3fef5d Mon Sep 17 00:00:00 2001 From: Abhinav Sharma <63901956+abhi-bhatra@users.noreply.github.com> Date: Wed, 28 Jul 2021 19:18:39 +0530 Subject: [PATCH 2/8] Update README.md --- 2-Regression/1-Tools/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/2-Regression/1-Tools/README.md b/2-Regression/1-Tools/README.md index e36c34fe6..6225f7cd7 100644 --- a/2-Regression/1-Tools/README.md +++ b/2-Regression/1-Tools/README.md @@ -95,7 +95,7 @@ For this task we will import some libraries: - **matplotlib**. It's a useful [graphing tool](https://matplotlib.org/) and we will use it to create a line plot. - **numpy**. [numpy](https://numpy.org/doc/stable/user/whatisnumpy.html) is a useful library for handling numeric data in Python. -- **sklearn**. This is the Scikit-learn library. +- **sklearn**. This is the [Scikit-learn](https://scikit-learn.org/stable/user_guide.html) library. Import some libraries to help with your tasks. From cdb7befec9a8b6d18025a7e5e9865904705f96d7 Mon Sep 17 00:00:00 2001 From: Abhinav Sharma <63901956+abhi-bhatra@users.noreply.github.com> Date: Wed, 28 Jul 2021 20:13:21 +0530 Subject: [PATCH 3/8] Update README.md Stanford's K-Means Simulator had been removed from the parent directory. Added new K-Means Simulator --- 5-Clustering/2-K-Means/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/5-Clustering/2-K-Means/README.md b/5-Clustering/2-K-Means/README.md index 153932e63..bd59e080d 100644 --- a/5-Clustering/2-K-Means/README.md +++ b/5-Clustering/2-K-Means/README.md @@ -242,7 +242,7 @@ Hint: Try to scale your data. There's commented code in the notebook that adds s ## 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? +Take a look at K-Means Simulator [here](https://user.ceng.metu.edu.tr/~akifakkus/courses/ceng574/k-means/). You can use this tool to visualize sample data points and determine its centroids. 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? Also, take a look at [this handout on k-means](https://stanford.edu/~cpiech/cs221/handouts/kmeans.html) from Stanford. From 6cf1303a6bf1b62c3f65b11b5c597893d749a500 Mon Sep 17 00:00:00 2001 From: Abhinav Sharma <63901956+abhi-bhatra@users.noreply.github.com> Date: Thu, 29 Jul 2021 07:19:32 +0530 Subject: [PATCH 4/8] Update README.md address link broken --- 6-NLP/1-Introduction-to-NLP/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/6-NLP/1-Introduction-to-NLP/README.md b/6-NLP/1-Introduction-to-NLP/README.md index 924ce73c7..4a7a88d63 100644 --- a/6-NLP/1-Introduction-to-NLP/README.md +++ b/6-NLP/1-Introduction-to-NLP/README.md @@ -69,7 +69,7 @@ The idea for this came from a party game called *The Imitation Game* where an in ### Developing Eliza -In the 1960's an MIT scientist called *Joseph Weizenbaum* developed [*Eliza*](https:/wikipedia.org/wiki/ELIZA), a computer 'therapist' that would ask the human questions and give the appearance of understanding their answers. However, while Eliza could parse a sentence and identify certain grammatical constructs and keywords so as to give a reasonable answer, it could not be said to *understand* the sentence. If Eliza was presented with a sentence following the format "**I am** sad" it might rearrange and substitute words in the sentence to form the response "How long have **you been** sad". +In the 1960's an MIT scientist called *Joseph Weizenbaum* developed [*Eliza*](https://wikipedia.org/wiki/ELIZA), a computer 'therapist' that would ask the human questions and give the appearance of understanding their answers. However, while Eliza could parse a sentence and identify certain grammatical constructs and keywords so as to give a reasonable answer, it could not be said to *understand* the sentence. If Eliza was presented with a sentence following the format "**I am** sad" it might rearrange and substitute words in the sentence to form the response "How long have **you been** sad". This gave the impression that Eliza understood the statement and was asking a follow-on question, whereas in reality, it was changing the tense and adding some words. If Eliza could not identify a keyword that it had a response for, it would instead give a random response that could be applicable to many different statements. Eliza could be easily tricked, for instance if a user wrote "**You are** a bicycle" it might respond with "How long have **I been** a bicycle?", instead of a more reasoned response. From 8539234e6b083928d7a39d808328254a0ee84fd3 Mon Sep 17 00:00:00 2001 From: Abhinav Sharma <63901956+abhi-bhatra@users.noreply.github.com> Date: Thu, 29 Jul 2021 07:26:26 +0530 Subject: [PATCH 5/8] Update README.md Fix bot.py address --- 6-NLP/1-Introduction-to-NLP/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/6-NLP/1-Introduction-to-NLP/README.md b/6-NLP/1-Introduction-to-NLP/README.md index 4a7a88d63..512358562 100644 --- a/6-NLP/1-Introduction-to-NLP/README.md +++ b/6-NLP/1-Introduction-to-NLP/README.md @@ -133,7 +133,7 @@ Let's create the bot next. We'll start by defining some phrases. It was nice talking to you, goodbye! ``` - One possible solution to the task is [here](../solution/bot.py) + One possible solution to the task is [here](solution/bot.py) ✅ Stop and consider From 0ef0e1e35e805fc04efa1d2c8f4cf781fda9953e Mon Sep 17 00:00:00 2001 From: Abhinav Sharma <63901956+abhi-bhatra@users.noreply.github.com> Date: Thu, 29 Jul 2021 07:34:20 +0530 Subject: [PATCH 6/8] Update README.md Address Link fix --- 6-NLP/3-Translation-Sentiment/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/6-NLP/3-Translation-Sentiment/README.md b/6-NLP/3-Translation-Sentiment/README.md index bcd6cdd1a..0c6b568bf 100644 --- a/6-NLP/3-Translation-Sentiment/README.md +++ b/6-NLP/3-Translation-Sentiment/README.md @@ -143,7 +143,7 @@ Your task is to determine, using sentiment polarity, if *Pride and Prejudice* ha 1. If the polarity is 1 or -1 store the sentence in an array or list of positive or negative messages 5. At the end, print out all the positive sentences and negative sentences (separately) and the number of each. -Here is a sample [solution](solutions/notebook.ipynb). +Here is a sample [solution](solution/notebook.ipynb). ✅ Knowledge Check From fe31fe3d735396380fe977963ebd04a4458e318b Mon Sep 17 00:00:00 2001 From: Abhinav Sharma <63901956+abhi-bhatra@users.noreply.github.com> Date: Thu, 29 Jul 2021 07:48:06 +0530 Subject: [PATCH 7/8] Update README.md Fix link to Jupyter-notebook --- 6-NLP/5-Hotel-Reviews-2/README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/6-NLP/5-Hotel-Reviews-2/README.md b/6-NLP/5-Hotel-Reviews-2/README.md index 7d8a4d031..12b9a15af 100644 --- a/6-NLP/5-Hotel-Reviews-2/README.md +++ b/6-NLP/5-Hotel-Reviews-2/README.md @@ -347,13 +347,13 @@ print("Saving results to Hotel_Reviews_NLP.csv") df.to_csv(r"../data/Hotel_Reviews_NLP.csv", index = False) ``` -You should run the entire code for [the analysis notebook](solution/notebook-sentiment-analysis.ipynb) (after you've run [your filtering notebook](solution/notebook-filtering.ipynb) to generate the Hotel_Reviews_Filtered.csv file). +You should run the entire code for [the analysis notebook](solution/3-notebook.ipynb) (after you've run [your filtering notebook](solution/1-notebook.ipynb) to generate the Hotel_Reviews_Filtered.csv file). To review, the steps are: -1. Original dataset file **Hotel_Reviews.csv** is explored in the previous lesson with [the explorer notebook](../4-Hotel-Reviews-1/solution/notebook-explorer.ipynb) -2. Hotel_Reviews.csv is filtered by [the filtering notebook](solution/notebook-filtering.ipynb) resulting in **Hotel_Reviews_Filtered.csv** -3. Hotel_Reviews_Filtered.csv is processed by [the sentiment analysis notebook](solution/notebook-sentiment-analysis.ipynb) resulting in **Hotel_Reviews_NLP.csv** +1. Original dataset file **Hotel_Reviews.csv** is explored in the previous lesson with [the explorer notebook](../4-Hotel-Reviews-1/solution/notebook.ipynb) +2. Hotel_Reviews.csv is filtered by [the filtering notebook](solution/1-notebook.ipynb) resulting in **Hotel_Reviews_Filtered.csv** +3. Hotel_Reviews_Filtered.csv is processed by [the sentiment analysis notebook](solution/3-notebook.ipynb) resulting in **Hotel_Reviews_NLP.csv** 4. Use Hotel_Reviews_NLP.csv in the NLP Challenge below ### Conclusion From d20f51dc5c7a7bc7dc52e75cde76288be2c9e3eb Mon Sep 17 00:00:00 2001 From: Abhinav Sharma <63901956+abhi-bhatra@users.noreply.github.com> Date: Thu, 29 Jul 2021 08:30:58 +0530 Subject: [PATCH 8/8] Update README.md Link to K-Means Clustering Simulator --- 5-Clustering/2-K-Means/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/5-Clustering/2-K-Means/README.md b/5-Clustering/2-K-Means/README.md index bd59e080d..6e0724b53 100644 --- a/5-Clustering/2-K-Means/README.md +++ b/5-Clustering/2-K-Means/README.md @@ -242,7 +242,7 @@ Hint: Try to scale your data. There's commented code in the notebook that adds s ## Review & Self Study -Take a look at K-Means Simulator [here](https://user.ceng.metu.edu.tr/~akifakkus/courses/ceng574/k-means/). You can use this tool to visualize sample data points and determine its centroids. 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? +Take a look at K-Means Simulator [such as this one](https://user.ceng.metu.edu.tr/~akifakkus/courses/ceng574/k-means/). You can use this tool to visualize sample data points and determine its centroids. 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? Also, take a look at [this handout on k-means](https://stanford.edu/~cpiech/cs221/handouts/kmeans.html) from Stanford.