Machine learning is applied everywhere, from business to research and academia, while Scikit-Learn is a versatile library that is popular among machine learning practitioners. This book serves as a practical guide for anyone looking to provide hands-on machine learning solutions with Scikit-Learn and Python toolkits.
This book goes beyond Scikit-Learn, and introduces you to complementary libraries such as NumPy, Pandas, SpaCy, imbalanced-learn, and Scikit-Surprise. The theoretical knowledge in this book should also prepare you to use libraries not mentioned here such as TensorFlow and PyTorch.
This book is composed of 13 chapters. Here is a brief about each chapter:
In this chapter, we are going to start by looking at our first supervised learning algorithm - decision trees. The decision tree algorithm is versatile and easy to understand. It is widely used and also serves as the building block for numerous advanced algorithms, such as Random Forest and Gradient Boosted Trees. By the end of this chapter, you will have a very good understanding of the following topics:
Since this is the first chapter where we use a supervised learning algorithm here, we will also discuss how to split your data, and how to evaluate your model's performance.