Learning Machine Learning from Scratch

The goal of this book is to enable people who are not in the field of machine learning or even non-computer majors but have learning needs to easily master the basic knowledge of machine learning, so as to have relevant practical capabilities. This book unfolds through a conversation between AI "novice" Xiaobing and programmer Ka Ge learning about machine learning. The content is easy and practical. It mainly includes the path to quickly get started with machine learning, basic knowledge of mathematics and Python, basic machine learning algorithms (linear regression and logistic regression), deep neural networks, convolutional neural networks, recurrent neural networks, classic algorithms, ensemble learning, unsupervised and semi-supervised unsupervised learning types, reinforcement learning practices, etc., As well as related practical cases. All cases in this book are implemented through Python, Scikit-learn machine learning library and Keras deep learning framework, and also contain rich data analysis and data visualization content. This book is suitable for programmers, project managers, college students who are interested in AI, and anyone who wants to learn machine learning from scratch. It can be used to get started in the field of machine learning and establish a knowledge channel from theory to practice.