Learn Machine Learning with Me

This book systematically explains several common types of models in machine learning, including the ideas, principles and implementation details of the models. At the same time, this book also combines the currently popular machine learning framework Sklearn to explain in detail the usage of the models involved in the book. The book has 10 chapters in total. Chapter 1 introduces the configuration of the machine learning development environment; Chapter 2 explains the basic principles of linear regression models, several common evaluation indicators in regression models, and the gradient descent algorithm used for supervised model training; Chapter 3 introduces the basic principles of logistic regression models and several common evaluation indicators in classification models; Chapter 4 introduces the improvement and generalization of the model, including feature standardization, how to avoid overfitting, and how to select models; Chapter 5 explains K recently The basic principles of the neighbor classification algorithm and the construction and search of kd trees; Chapter 6 introduces the basic principles of the Naive Bayes algorithm; Chapter 7 introduces several common text feature extraction methods, including bag-of-words models and TF-IDF, etc.; Chapter 8 explains the basic principles of decision trees, including several classic decision tree generation algorithms and ensemble models; Chapter 9 introduces the basic principles and solution process of support vector machines; Chapter 10 introduces several classic clustering algorithms and corresponding evaluation index calculation methods. This book contains a large number of code examples and practical case introductions. It can not only be used as an introduction to machine learning for students in computer-related majors, but also suitable as a reference study book for non-computer majors and training institutions.