Master Machine Learning with scikit-learn: A Practical Guide to Building Better Models with Python

This is a practical guide to help you transform from Machine Learning novice to skilled Machine Learning practitioner.

Throughout the book, you’ll learn the best practices for proper Machine Learning and how to apply those practices to your own Machine Learning problems. By the end of this book, you’ll be more confident when tackling new Machine Learning problems because you’ll understand what steps you need to take, why you need to take them, and how to correctly execute those steps using scikit-learn. You’ll know what problems you might run into, and you’ll know exactly how to solve them. Because you’re learning a better way to work in scikit-learn, your code will be easier to write and to read, and you’ll get better Machine Learning results faster than before!

"If you think that Machine Learning is too complex for you to learn, I cannot recommend this book enough. It will give you the confidence you need, along with the knowledge you want."
- Reuven Lerner, Python trainer

"By far the best book I've read on scikit-learn. The later chapters, in particular, helped me significantly deepen my understanding and improve my use of the library."
- Patrick Ryan, Software Engineer

"Exceptionally well-structured and easy to grasp."
- Marco Peters, Business Intelligence Analyst

Kevin Markham is the founder of Data School, an online school for learning Data Science with Python. He has been teaching Machine Learning in the classroom and online for more than 10 years, and is passionate about teaching people who are new to the field. He has a degree in Computer Engineering from Vanderbilt University and lives in Asheville, North Carolina.

Topics covered:

• Review of the basic Machine Learning workflow
• Encoding categorical features
• Encoding text data
• Handling missing values
• Preparing complex datasets
• Creating an efficient workflow for preprocessing and model building
• Tuning your workflow for maximum performance
• Avoiding data leakage
• Proper model evaluation
• Automatic feature selection
• Feature standardization
• Feature engineering using custom transformers
• Linear and non-linear models
• Model ensembling
• Model persistence
• Handling high-cardinality categorical features
• Handling class imbalance