Merge pull request #247 from abhi-bhatra/main

Minor Link Fix

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.
 

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

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 [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.
 

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** <u>sad</u>" it might rearrange and substitute words in the sentence to form the response "How long have **you been** <u>sad</u>". 
+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** <u>sad</u>" it might rearrange and substitute words in the sentence to form the response "How long have **you been** <u>sad</u>". 
 
 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 <u>bicycle</u>" it might respond with "How long have **I been** a <u>bicycle</u>?", instead of a more reasoned response.
 
@@ -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
 

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
 

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