Machine Learning Exercises

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

Also 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 first, 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 also 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.)

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

Block 1.3:

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

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

Part 3:

Block 3.1:

• Read the sections: "SL: Linear Models" - "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)"

Part 4:

Block 4.1:

• Read the whole chapter: "Avoiding Common Pitfalls"
• Answer Quiz 4

Block 4.2:

• 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!)

Part 5:

Block 5.1:

• Read the whole chapter: "ML Algorithms: Reinforcement Learning"

Block 5.2:

• Answer Quiz 5
• Read the whole chapter: "Conclusion: Using ML in Practice"
• Exercise: plan your next ML project