Dear readers and friends, today I would like to recommend a very practical basic book for introductory machine learning - " Introduction to Machine Learning (Micro-Lecture Edition)". This book is created by Mr. Huang Haiguang with all his heart. It is suitable for beginners who only have a third-year undergraduate mathematics level or above. This book has been designated as an undergraduate textbook by many schools.
background introduction
The author of this book, Mr. Huang Haiguang, is a university teacher. He has translated and sorted out many introductory course materials for artificial intelligence, such as the translation and notes sorting of "Wu Enda Machine Learning". He is currently undertaking machine learning courses for undergraduates and postgraduates. Teaching. After learning from many excellent machine learning courses and works at home and abroad, Mr. Huang Haiguang decided to write an introductory book on machine learning suitable for beginners, so that everyone can get started quickly.
content summary
This book has been printed for the second time by Tsinghua University Press. It is not only suitable for beginners to learn, but also suitable for novice teachers to teach. The main content includes classic algorithms such as linear regression, logistic regression, and decision tree, as well as integrated learning algorithms such as XGBoost and LightGBM. In addition, practical techniques for solving problems using machine learning are explained, including the use of Python, Scikit-learn tools, and more.
The course catalog is as follows:
Highlight analysis
1. Both pictures and texts
The content of this book is full of pictures and texts. Most of the illustrations are produced by Mr. Huang Haiguang, and the images are vivid. Some illustrations are as follows:
2. Code sharing
The courseware and codes of this book have been shared on Github and have received 1400+ stars.
3. Video explanation
Most of the content in this book has video explanations. Scan the QR code below the content to view the video explanations, or you can join the Chinese University MOOC to learn the complete course. The Chinese University MOOC "Machine Learning" taught by Mr. Huang Haiguang has been recognized as a first-class undergraduate course in Zhejiang Province.
4. Practice after class
In this book, each chapter has codes and more than 20 practice questions, which is convenient for readers to consolidate the knowledge they have learned.
5. Supporting teaching materials
In this book, each chapter has courseware suitable for teaching, and provides teaching progress and syllabus, which can be shared with in-service teachers. At present, the author has shared the complete courseware and code with about 1300 teachers in China.
6. Teaching arrangements
This book can be used as a textbook for junior college students, undergraduate students, and graduate students.
As textbooks for undergraduates, Chapter 2 Mathematics Fundamentals Review and Chapter 11 Artificial Neural Networks can be used as electives. Suggested class hours: 32 class hours for theoretical courses , 16~32 class hours for experimental courses ;
When used as a textbook for junior college students, it is recommended to use the code to explain the course, increase the hours of the experimental part, and reduce the hours of the theoretical part .
As a textbook for graduate students, the recommended class hours are 36 class hours, and self-study is recommended for the experimental part.
For people
This book is suitable for readers who want to get started with machine learning. As long as they have mathematics knowledge in the third grade or above and know a programming language, they can master most of the content of this book.
This book is also suitable for introductory machine learning courses for masters and doctoral students who are new to machine learning.
author's words
The author said that the original intention of writing this book is to help beginners better get started with machine learning, solve problems with too much information and difficult choices, and problems that are theoretically strong and difficult for beginners. At the same time, the author also hopes that through this book, more people can understand and master machine learning technology and contribute to the development of related fields. In short, this is an introductory book on machine learning with rich content and strong practicability, suitable for beginners and practitioners in related fields. If you are interested in machine learning, you might as well read it, I believe you will gain something.
In the process of writing this book, I have received support and help from many people, such as Teacher Li Hang and Teacher Xu Yida, who are very supportive of my work, and I would like to express my gratitude!
My level is limited. If there are any formulas and algorithms that are wrong, readers are welcome to correct and criticize.
related resources
The video content of this book has been taught in MOOCs of Chinese universities, and it is currently the sixth round. MOOC address:
https://www.icourse163.org/course/WZU-1464096179
Course resources (pdf version courseware and code) are published on Github:
https://github.com/fengdu78/WZU-machine-learning-course
The courseware and teaching materials of this book can be shared with in-service teachers, please use the edu email to contact: [email protected]
Address to purchase this book (read the original text):
https://item.jd.com/13935772.html
Dry goods learning, like three times ↓