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## Machine Learning Exercises
# Machine Learning Exercises
This repository contains the Python exercises accompanying the theory from my [machine learning book](https://franziskahorn.de/mlbook/).
You might also want to have a look at the [cheat sheet](https://github.com/cod3licious/ml_exercises/blob/main/cheatsheet.pdf), 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](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 also verify this with the `test_installation.ipynb` notebook). <br>
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>
If you have a Google account, you can also run the code in the cloud using **Google Colab**:
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/cod3licious/ml_exercises)
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Have fun!
### Course Overview
## Course Overview
(You can also find the course syllabus on the last page of the course description.)
#### Part 1:
### Part 1:
##### Block 1.1:
- [ ] Read the whole chapter: ["Introduction: Solving Problems with ML"](https://franziskahorn.de/mlbook/_introduction_solving_problems_with_ml.html)
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#### Part 2:
### Part 2:
##### Block 2.1:
- [ ] Read the section: ["UL: Dimensionality Reduction"](https://franziskahorn.de/mlbook/_ul_dimensionality_reduction.html)
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#### Part 3:
### Part 3:
##### Block 3.1:
- [ ] Read the sections: ["SL: Linear Models"](https://franziskahorn.de/mlbook/_sl_linear_models.html) - ["SL: Kernel Methods"](https://franziskahorn.de/mlbook/_sl_kernel_methods.html)
- [ ] 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)
- [ ] In parallel, work through the respective sections of [Notebook 3: supervised comparison](/exercises/3_supervised_comparison.ipynb)
##### Block 3.2:
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##### Block 3.3:
- [ ] Read the section: ["SL: Neural Networks"](https://franziskahorn.de/mlbook/_sl_neural_networks.html)
- [ ] Work through [Notebook 5: MNIST with torch](/exercises/5_mnist_torch.ipynb) (recommended) or [MNIST with keras](/exercises/5_mnist_keras.ipynb) (in case others in your organization are already working with TensorFlow)
- [ ] Work through [Notebook 5: MNIST with torch](/exercises/5_mnist_torch.ipynb) (recommended) **_or_** [MNIST with keras](/exercises/5_mnist_keras.ipynb) (in case others in your organization are already working with TensorFlow)
- [ ] Read the sections: ["Time Series Forecasting"](https://franziskahorn.de/mlbook/_time_series_forecasting.html) and ["Recommender Systems (Pairwise Data)"](https://franziskahorn.de/mlbook/_recommender_systems_pairwise_data.html)
#### Part 4:
### Part 4:
##### Block 4.1:
- [ ] Read the whole chapter: ["Avoiding Common Pitfalls"](https://franziskahorn.de/mlbook/_avoiding_common_pitfalls.html)
- [ ] Answer [Quiz 4](/exercises/1_visualize_text.ipynb)
##### Block 4.2:
- [ ] Answer [Quiz 4](/exercises/1_visualize_text.ipynb)
- [ ] Work through [Notebook 6: analyze toy dataset](/exercises/6_analyze_toydata.ipynb)
##### Block 4.3:
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##### Block 5.2:
- [ ] Answer [Quiz 5](https://forms.gle/uzdzytpsYf9sFG946)
- [ ] Read the whole chapter: ["Conclusion: Using ML in Practice"](https://franziskahorn.de/mlbook/_conclusion_using_ml_in_practice.html)
- [ ] [Exercise: plan your next ML project](https://forms.gle/uzdzytpsYf9sFG946)
- [ ] Complete the exercise: ["Your next ML Project"](https://forms.gle/uzdzytpsYf9sFG946)