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# Machine Learning Exercises
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This repository contains the Python exercises accompanying the theory from my [machine learning book](https://franziskahorn.de/mlbook/).
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This repository contains the exercises accompanying the theory from my [machine learning book](https://franziskahorn.de/mlbook/).
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You might also want to have a look at the [cheat sheet](/cheatsheet.pdf), which includes a summary of the most important steps when developing a machine learning solution, incl. code snippets.
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If you're unfamiliar with Python, please have a look at [this tutorial](https://github.com/cod3licious/python_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). <br>
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The programming exercises are written in Python. If you're unfamiliar with Python, please have a look at [this tutorial](https://github.com/cod3licious/python_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`](/requirements.txt) file are installed and up to date; you can verify this with the [`test_installation.ipynb`](/test_installation.ipynb) notebook). <br>
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If you have a Google account, you can also run the code in the cloud using **Google Colab**:
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[](https://colab.research.google.com/github/cod3licious/ml_exercises)
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[](https://colab.research.google.com/github/cod3licious/ml_exercises) <br>
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While Google Colab already includes most packages that we need, should you require an additional library (e.g., `skorch` for training PyTorch neural networks in notebook 5), you can install it by executing `!pip install package` in a notebook cell. With Colab, it is also possible to run code on a GPU, but this has to be manually selected.
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If you have any questions, please drop me a line at `hey[at]franziskahorn.de`.
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If you have any questions, please send me an [email](mailto:hey@franziskahorn.de).
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Have fun!
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## Course Overview
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(You can also find the course syllabus on the last page of the [course description](/course_description.pdf).)
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For an optimal learning experience, the chapters from the [machine learning book](https://franziskahorn.de/mlbook/) should be interleaved with quizzes and programming exercises as shown below.
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(You can also find the course syllabus on the last page of the [course description](/course_description.pdf), which explicitly lists all the sections of the book for each block.)
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---
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### Part 1:
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### Part 1: Getting started: What is ML?
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##### Block 1.1:
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- [ ] Read the whole chapter: ["Introduction: Solving Problems with ML"](https://franziskahorn.de/mlbook/_introduction_solving_problems_with_ml.html)
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##### Block 1.3:
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- [ ] Read the whole chapter: ["Data & Preprocessing"](https://franziskahorn.de/mlbook/_data_preprocessing.html)
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- [ ] Answer [Quiz 2](https://forms.gle/Pqr6EKHNxzrWb7MF9)
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- [ ] Read the first section of the chapter ["ML Algorithms: Unsupervised & Supervised Learning"](https://franziskahorn.de/mlbook/_ml_algorithms_unsupervised_supervised_learning.html)
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- [ ] Read the introductory part of the chapter ["ML Algorithms: Unsupervised & Supervised Learning"](https://franziskahorn.de/mlbook/_ml_algorithms_unsupervised_supervised_learning.html)
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---
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### Part 2:
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### Part 2: Your first algorithms
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##### Block 2.1:
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- [ ] Read the section: ["UL: Dimensionality Reduction"](https://franziskahorn.de/mlbook/_ul_dimensionality_reduction.html)
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---
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### Part 3:
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### Part 3: Advanced models
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##### Block 3.1:
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- [ ] Read the sections: ["SL: Linear Models"](https://franziskahorn.de/mlbook/_sl_linear_models.html) up to and including ["SL: Kernel Methods"](https://franziskahorn.de/mlbook/_sl_kernel_methods.html)
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- [ ] In parallel, work through the respective sections of [Notebook 3: supervised comparison](/exercises/3_supervised_comparison.ipynb)
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- [ ] **In parallel**, work through the respective sections of [Notebook 3: supervised comparison](/exercises/3_supervised_comparison.ipynb)
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##### Block 3.2:
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- [ ] Read the section: ["Information Retrieval (Similarity Search)"](https://franziskahorn.de/mlbook/_information_retrieval_similarity_search.html) and review the sections on [TF-IDF feature vectors](https://franziskahorn.de/mlbook/_feature_extraction.html) and [cosine similarity](https://franziskahorn.de/mlbook/_computing_similarities.html)
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---
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### Part 4:
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### Part 4: Avoiding common pitfalls
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##### Block 4.1:
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- [ ] Read the whole chapter: ["Avoiding Common Pitfalls"](https://franziskahorn.de/mlbook/_avoiding_common_pitfalls.html)
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---
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### Part 5:
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### Part 5: RL & Conclusion
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##### Block 5.1:
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- [ ] Read the whole chapter: ["ML Algorithms: Reinforcement Learning"](https://franziskahorn.de/mlbook/_ml_algorithms_reinforcement_learning.html)
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