Machine Learning Exercises

This repository contains the Python exercises accompanying the theory from my machine learning book.

You might also want to have a look at the cheat sheet, which includes a summary of the most important steps when developing a machine learning solution, incl. code snippets.

If you're unfamiliar with Python, please have a look at this tutorial before working on the exercises, which also includes some notes on how to install Python and Jupyter Notebook on your own computer (please make sure you're using Python 3 and all libraries listed in the requirements.txt file are installed and up to date; you can verify this with the test_installation.ipynb notebook).
If you have a Google account, you can also run the code in the cloud using Google Colab:

If you have any questions, please drop me a line at hey[at]franziskahorn.de.

Have fun!

Course Overview

(You can also find the course syllabus on the last page of the course description.)

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Part 1:

Block 1.1:

• Read the whole chapter: "Introduction: Solving Problems with ML"
• Answer Quiz 1

Block 1.2:

• Read the whole chapter: "ML with Python"
• Install Python on your computer and complete the Python tutorial (if you haven't done this already)

Block 1.3:

• Read the whole chapter: "Data & Preprocessing"
• Answer Quiz 2
• Read the first section of the chapter "ML Algorithms: Unsupervised & Supervised Learning"

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Part 2:

Block 2.1:

• Read the section: "UL: Dimensionality Reduction"
• Work through Notebook 1: visualize text

Block 2.2:

• Read the section: "UL: Outlier / Anomaly Detection"
• Read the section: "UL: Clustering"
• Work through Notebook 2: image quantization

Block 2.3:

• Read the section: "Supervised Learning: Overview"
• Answer Quiz 3

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Part 3:

Block 3.1:

• Read the sections: "SL: Linear Models" up to and including "SL: Kernel Methods"
• In parallel, work through the respective sections of Notebook 3: supervised comparison

Block 3.2:

• Read the section: "Information Retrieval (Similarity Search)" and review the sections on TF-IDF feature vectors and cosine similarity
• Work through Notebook 4: information retrieval

Block 3.3:

• Read the section: "SL: Neural Networks"
• Work through Notebook 5: MNIST with torch (recommended) or MNIST with keras (in case others in your organization are already working with TensorFlow)
• Read the sections: "Time Series Forecasting" and "Recommender Systems (Pairwise Data)"

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Part 4:

Block 4.1:

• Read the whole chapter: "Avoiding Common Pitfalls"

Block 4.2:

• Answer Quiz 4
• Work through Notebook 6: analyze toy dataset

Block 4.3:

• Case Study! Notebook 7: predicting hard drive failures (plan at least 5 hours for this!)

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Part 5:

Block 5.1:

• Read the whole chapter: "ML Algorithms: Reinforcement Learning"
• Work through Notebook 8: RL gridmove

Block 5.2:

• Answer Quiz 5
• Read the whole chapter: "Conclusion: Using ML in Practice"
• Complete the exercise: "Your next ML Project"

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