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!

This is an intermediate-level book about scikit-learn, though it also includes many advanced topics.

You’re ready for this book if you can use scikit-learn to solve simple classification or regression problems, including loading a dataset, defining the features and target, training and evaluating a model, and making predictions with new data.

If you’re brand new to scikit-learn, I recommend first taking my free introductory course, Introduction to Machine Learning with scikit-learn. Once you’ve completed lessons 1 through 7, you’ll know the Machine Learning and scikit-learn fundamentals that you’ll need for this book.

If you’ve used scikit-learn before but you just need a refresher, there’s no need to take my introductory course because I’ll be reviewing the Machine Learning workflow in chapter 2.

• 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