Welcome¶
Welcome to Applied Machine Learning with Python. This is a draft of an in-depth guide to machine learning in Python with scikit-learn.
It’s based on my course on Applied Machine Learning that I held at Columbia.
The book is available online at https://amueller.github.io/aml. and all notebooks are available on github.
Please feel free to open issues and pull requests there to improve the book.
This book is aimed at practitioners that have some experience with Python, but not necessarily a strong mathematical background. Therefore, I tried to avoid going into too much detail on the mathematical details of particular methods. I highly recommend the book Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani and Jerome Friedman and Machine Learning: a Probabilistic Perspective Kevin Murphy for those that are interested.
Mathematical Background
In some places, there are side-notes with mathematical details where those can be helpful for understanding the materials. However, these parts are optional and not required to follow the main text.
This is an early draft, and feedback is very welcome! It’s based on scikit-learn 0.23, though might require some features of scikit-learn 0.24 when finished.