diff --git a/1-Introduction/1-intro-to-ML/README.md b/1-Introduction/1-intro-to-ML/README.md index ce3b13ef..bca8dcdc 100644 --- a/1-Introduction/1-intro-to-ML/README.md +++ b/1-Introduction/1-intro-to-ML/README.md @@ -143,3 +143,6 @@ Take a [Learning Path](https://docs.microsoft.com/learn/modules/introduction-to- # Assignment [Get up and running](assignment.md) + +--- +[Next Lesson >>>](../2-history-of-ML/README.md) diff --git a/1-Introduction/2-history-of-ML/README.md b/1-Introduction/2-history-of-ML/README.md index 48e519e1..bb4686d0 100644 --- a/1-Introduction/2-history-of-ML/README.md +++ b/1-Introduction/2-history-of-ML/README.md @@ -148,3 +148,6 @@ Here are items to watch and listen to: ## Assignment [Create a timeline](assignment.md) + +--- +[Next Lesson >>>](../3-fairness/README.md) diff --git a/1-Introduction/3-fairness/README.md b/1-Introduction/3-fairness/README.md index 240181a8..601a884a 100644 --- a/1-Introduction/3-fairness/README.md +++ b/1-Introduction/3-fairness/README.md @@ -156,3 +156,6 @@ Read about Azure Machine Learning's tools to ensure fairness: ## Assignment [Explore RAI Toolbox](assignment.md) + +--- +[Next Lesson >>>](../4-techniques-of-ML/README.md) diff --git a/1-Introduction/4-techniques-of-ML/README.md b/1-Introduction/4-techniques-of-ML/README.md index 4fcd5ea3..c44056f0 100644 --- a/1-Introduction/4-techniques-of-ML/README.md +++ b/1-Introduction/4-techniques-of-ML/README.md @@ -116,3 +116,6 @@ Search online for interviews with data scientists who discuss their daily work. ## Assignment [Interview a data scientist](assignment.md) + +--- +[Next Lesson >>>](../../2-Regression/README.md) diff --git a/2-Regression/1-Tools/README.md b/2-Regression/1-Tools/README.md index 912de1bc..81b2d173 100644 --- a/2-Regression/1-Tools/README.md +++ b/2-Regression/1-Tools/README.md @@ -224,3 +224,6 @@ Read more about the concept of regression and think about what kinds of question ## Assignment [A different dataset](assignment.md) + +--- +[Next Lesson >>>](../2-Data/README.md) diff --git a/2-Regression/2-Data/README.md b/2-Regression/2-Data/README.md index 69e8adaa..668d5daf 100644 --- a/2-Regression/2-Data/README.md +++ b/2-Regression/2-Data/README.md @@ -209,3 +209,6 @@ Take a look at the many ways to visualize data. Make a list of the various libra ## Assignment [Exploring visualization](assignment.md) + +--- +[Next Lesson >>>](../3-Linear/README.md) diff --git a/2-Regression/3-Linear/README.md b/2-Regression/3-Linear/README.md index c9060034..7eba47dc 100644 --- a/2-Regression/3-Linear/README.md +++ b/2-Regression/3-Linear/README.md @@ -365,3 +365,6 @@ In this lesson we learned about Linear Regression. There are other important typ ## Assignment [Build a Model](assignment.md) + +--- +[Next Lesson >>>](../4-Logistic/README.md) diff --git a/2-Regression/4-Logistic/README.md b/2-Regression/4-Logistic/README.md index 34e49aab..3c768dc5 100644 --- a/2-Regression/4-Logistic/README.md +++ b/2-Regression/4-Logistic/README.md @@ -393,3 +393,6 @@ Read the first few pages of [this paper from Stanford](https://web.stanford.edu/ ## Assignment [Retrying this regression](assignment.md) + +--- +[Next Lesson >>>](../../3-Web-App/README.md) diff --git a/3-Web-App/1-Web-App/README.md b/3-Web-App/1-Web-App/README.md index 29278660..0f254bd4 100644 --- a/3-Web-App/1-Web-App/README.md +++ b/3-Web-App/1-Web-App/README.md @@ -343,3 +343,6 @@ There are many ways to build a web app to consume ML models. Make a list of the ## Assignment [Try a different model](assignment.md) + +--- +[Next Lesson >>>](../../4-Classification/README.md) diff --git a/4-Classification/1-Introduction/README.md b/4-Classification/1-Introduction/README.md index da84c188..49ddbeef 100644 --- a/4-Classification/1-Introduction/README.md +++ b/4-Classification/1-Introduction/README.md @@ -297,3 +297,6 @@ Explore SMOTE's API. What use cases is it best used for? What problems does it s ## Assignment [Explore classification methods](assignment.md) + +--- +[Next Lesson >>>](../2-Classifiers-1/README.md) diff --git a/4-Classification/2-Classifiers-1/README.md b/4-Classification/2-Classifiers-1/README.md index 24544e95..819ecff7 100644 --- a/4-Classification/2-Classifiers-1/README.md +++ b/4-Classification/2-Classifiers-1/README.md @@ -239,3 +239,6 @@ Dig a little more into the math behind logistic regression in [this lesson](http ## Assignment [Study the solvers](assignment.md) + +--- +[Next Lesson >>>](../3-Classifiers-2/README.md) diff --git a/4-Classification/3-Classifiers-2/README.md b/4-Classification/3-Classifiers-2/README.md index 48cc2329..82044a85 100644 --- a/4-Classification/3-Classifiers-2/README.md +++ b/4-Classification/3-Classifiers-2/README.md @@ -233,3 +233,6 @@ There's a lot of jargon in these lessons, so take a minute to review [this list] ## Assignment [Parameter play](assignment.md) + +--- +[Next Lesson >>>](../4-Applied/README.md) diff --git a/4-Classification/4-Applied/README.md b/4-Classification/4-Applied/README.md index 2e069631..db7f49f4 100644 --- a/4-Classification/4-Applied/README.md +++ b/4-Classification/4-Applied/README.md @@ -312,3 +312,6 @@ While this lesson just touched on the utility of creating a recommendation syste ## Assignment [Build a new recommender](assignment.md) + +--- +[Next Lesson >>>](../../5-Clustering/README.md) diff --git a/5-Clustering/1-Visualize/README.md b/5-Clustering/1-Visualize/README.md index cbff4726..ab8a233d 100644 --- a/5-Clustering/1-Visualize/README.md +++ b/5-Clustering/1-Visualize/README.md @@ -328,3 +328,6 @@ Before you apply clustering algorithms, as we have learned, it's a good idea to ## Assignment [Research other visualizations for clustering](assignment.md) + +--- +[Next Lesson >>>](../2-K-Means/README.md) diff --git a/5-Clustering/2-K-Means/README.md b/5-Clustering/2-K-Means/README.md index 18a08fdd..823b6bca 100644 --- a/5-Clustering/2-K-Means/README.md +++ b/5-Clustering/2-K-Means/README.md @@ -245,3 +245,6 @@ Also, take a look at [this handout on K-Means](https://stanford.edu/~cpiech/cs22 ## Assignment [Try different clustering methods](assignment.md) + +--- +[Next Lesson >>>](../../6-NLP/README.md) diff --git a/6-NLP/1-Introduction-to-NLP/README.md b/6-NLP/1-Introduction-to-NLP/README.md index 430b683d..5e00fd68 100644 --- a/6-NLP/1-Introduction-to-NLP/README.md +++ b/6-NLP/1-Introduction-to-NLP/README.md @@ -163,3 +163,6 @@ Take a look at the references below as further reading opportunities. ## Assignment [Search for a bot](assignment.md) + +--- +[Next Lesson >>>](../2-Tasks/README.md) diff --git a/6-NLP/2-Tasks/README.md b/6-NLP/2-Tasks/README.md index 036e30f7..a54f36cf 100644 --- a/6-NLP/2-Tasks/README.md +++ b/6-NLP/2-Tasks/README.md @@ -212,3 +212,6 @@ In the next few lessons you will learn more about sentiment analysis. Research t ## Assignment [Make a bot talk back](assignment.md) + +--- +[Next Lesson >>>](../3-Translation-Sentiment/README.md) diff --git a/6-NLP/3-Translation-Sentiment/README.md b/6-NLP/3-Translation-Sentiment/README.md index 9ed7a186..b0a9390c 100644 --- a/6-NLP/3-Translation-Sentiment/README.md +++ b/6-NLP/3-Translation-Sentiment/README.md @@ -185,3 +185,6 @@ There are many ways to extract sentiment from text. Think of the business applic ## Assignment [Poetic license](assignment.md) + +--- +[Next Lesson >>>](../4-Hotel-Reviews-1/README.md) diff --git a/6-NLP/4-Hotel-Reviews-1/README.md b/6-NLP/4-Hotel-Reviews-1/README.md index fd2ba705..4b5dbebc 100644 --- a/6-NLP/4-Hotel-Reviews-1/README.md +++ b/6-NLP/4-Hotel-Reviews-1/README.md @@ -402,3 +402,6 @@ Take [this Learning Path on NLP](https://docs.microsoft.com/learn/paths/explore- ## Assignment [NLTK](assignment.md) + +--- +[Next Lesson >>>](../5-Hotel-Reviews-2/README.md) diff --git a/6-NLP/5-Hotel-Reviews-2/README.md b/6-NLP/5-Hotel-Reviews-2/README.md index 36c00bed..70796cb4 100644 --- a/6-NLP/5-Hotel-Reviews-2/README.md +++ b/6-NLP/5-Hotel-Reviews-2/README.md @@ -372,3 +372,6 @@ Take [this Learn module](https://docs.microsoft.com/en-us/learn/modules/classify ## Assignment [Try a different dataset](assignment.md) + +--- +[Next Lesson >>>](../../7-TimeSeries/README.md) diff --git a/7-TimeSeries/1-Introduction/README.md b/7-TimeSeries/1-Introduction/README.md index 742b89c8..baadbe14 100644 --- a/7-TimeSeries/1-Introduction/README.md +++ b/7-TimeSeries/1-Introduction/README.md @@ -183,3 +183,6 @@ Although we won't cover them here, neural networks are sometimes used to enhance ## Assignment [Visualize some more time series](assignment.md) + +--- +[Next Lesson >>>](../2-ARIMA/README.md) diff --git a/7-TimeSeries/2-ARIMA/README.md b/7-TimeSeries/2-ARIMA/README.md index 6de07c0d..ad658a5f 100644 --- a/7-TimeSeries/2-ARIMA/README.md +++ b/7-TimeSeries/2-ARIMA/README.md @@ -392,3 +392,6 @@ This lesson touches on only the basics of Time Series Forecasting with ARIMA. Ta ## Assignment [A new ARIMA model](assignment.md) + +--- +[Next Lesson >>>](../3-SVR/README.md) diff --git a/7-TimeSeries/3-SVR/README.md b/7-TimeSeries/3-SVR/README.md index 8d4eea60..a8156918 100644 --- a/7-TimeSeries/3-SVR/README.md +++ b/7-TimeSeries/3-SVR/README.md @@ -384,3 +384,6 @@ This lesson was to introduce the application of SVR for Time Series Forecasting. [^1]: The text, code and output in this section was contributed by [@AnirbanMukherjeeXD](https://github.com/AnirbanMukherjeeXD) [^2]: The text, code and output in this section was taken from [ARIMA](https://github.com/microsoft/ML-For-Beginners/tree/main/7-TimeSeries/2-ARIMA) + +--- +[Next Lesson >>>](../../8-Reinforcement/README.md) diff --git a/8-Reinforcement/1-QLearning/README.md b/8-Reinforcement/1-QLearning/README.md index 9535b501..94a57d1d 100644 --- a/8-Reinforcement/1-QLearning/README.md +++ b/8-Reinforcement/1-QLearning/README.md @@ -318,3 +318,6 @@ Overall, it is important to remember that the success and quality of the learnin ## Assignment [A More Realistic World](assignment.md) + +--- +[Next Lesson >>>](../2-Gym/README.md) diff --git a/8-Reinforcement/2-Gym/README.md b/8-Reinforcement/2-Gym/README.md index b5e4237a..f5015220 100644 --- a/8-Reinforcement/2-Gym/README.md +++ b/8-Reinforcement/2-Gym/README.md @@ -339,3 +339,6 @@ You should see something like this: We have now learned how to train agents to achieve good results just by providing them a reward function that defines the desired state of the game, and by giving them an opportunity to intelligently explore the search space. We have successfully applied the Q-Learning algorithm in the cases of discrete and continuous environments, but with discrete actions. It's important to also study situations where action state is also continuous, and when observation space is much more complex, such as the image from the Atari game screen. In those problems we often need to use more powerful machine learning techniques, such as neural networks, in order to achieve good results. Those more advanced topics are the subject of our forthcoming more advanced AI course. + +--- +[Next Lesson >>>](../../9-Real-World/README.md) diff --git a/9-Real-World/1-Applications/README.md b/9-Real-World/1-Applications/README.md index 36643172..8437a057 100644 --- a/9-Real-World/1-Applications/README.md +++ b/9-Real-World/1-Applications/README.md @@ -145,3 +145,6 @@ The Wayfair data science team has several interesting videos on how they use ML ## Assignment [A ML scavenger hunt](assignment.md) + +--- +[Next Lesson >>>](../2-Debugging-ML-Models/README.md)