Please recommend some machine learning ai books for beginners

121018ww

Please recommend some machine learning ai books for beginners [Copy link]

 

Please recommend some machine learning ai books for beginners

之敖赵先生

Here are some good books to get started in the field of machine learning and artificial intelligence (AI):

1. "Statistical Learning Methods" (written by Li Hang): This book is a classic textbook in the field of machine learning in China. It introduces the basic theories, algorithms, and methods of machine learning, including supervised learning, unsupervised learning, and semi-supervised learning.

2. "Machine Learning" (written by Zhou Zhihua): This book is a good introductory textbook that covers the basic concepts, algorithms, and applications of machine learning. It is suitable for beginners and practitioners.

3. "Deep Learning" (by Ian Goodfellow, Yoshua Bengio, and Aaron Courville): This book is a classic textbook in the field of deep learning. It introduces the basic principles, algorithms, and applications of deep learning and is a good resource for learning deep learning.

4. "Python Machine Learning Basics" (by Sebastian Raschka and Vahid Mirjalili): This book introduces the basics and techniques of implementing machine learning algorithms using Python, including data preprocessing, model selection, evaluation, and optimization.

5. "Practical Machine Learning" (by Peter Harrington): This book is mainly practical. It teaches machine learning algorithms by writing code and solving practical problems. It is suitable for readers who want to practice.

The above books cover the basic theories, algorithms, and practical skills in the field of machine learning and artificial intelligence, and are suitable for readers of different levels.

半步天涯

I recommend a few introductory books on machine learning and artificial intelligence:

1. "Python Machine Learning Basics" (by Sebastian Raschka and Vahid Mirjalili) :

   • This book uses Python as the programming language to introduce the basic concepts, common algorithms, and practical skills of machine learning. It is suitable for beginners and contains a large number of example codes and case studies to help readers quickly understand the basic principles and application methods of machine learning.

2. Machine Learning in Action (by Peter Harrington) :

   • This book is practical and introduces the application and implementation of machine learning algorithms through a series of real-world project cases. It is suitable for people who want to learn machine learning through practical projects, and is very helpful for readers who are interested in a combination of theory and practice.

3. "Statistical Learning Methods" (Author: Li Hang) :

   • This is a classic machine learning textbook that introduces the basic principles and common algorithms of statistical learning. The book is rigorous in theory, covers a lot of mathematical knowledge and machine learning algorithms, and is suitable for readers who want to have a deep understanding of machine learning theory.

4. "Deep Learning" (Authors: Ian Goodfellow, Yoshua Bengio, Aaron Courville) :

   • This book is a classic textbook on deep learning, which introduces the basic principles, algorithms and applications of deep learning in detail. It is suitable for people who want to learn deep learning in depth, and is very helpful for understanding the mathematical principles and model structure behind deep learning.

5. Artificial Intelligence: A Modern Approach (by Stuart Russell and Peter Norvig) :

   • This is a classic textbook on artificial intelligence, covering the basic concepts, algorithms and applications of artificial intelligence. It is suitable for readers who want to have a comprehensive understanding of the field of artificial intelligence, and is very helpful for understanding the basic concepts and development history of artificial intelligence.

The above books are classic introductory textbooks in the field of machine learning and artificial intelligence. They cover all aspects from basic concepts to practical applications and are suitable for readers of different levels and interests.

215

For electronic engineers who want to get started with machine learning and artificial intelligence, here are some recommended books for beginners:

1. "Machine Learning in Action" - Author: Peter Harrington: This book focuses on practice, introducing the basic concepts and common algorithms of machine learning, and demonstrates through examples how to apply these algorithms in actual projects.

2. "Statistical Learning Methods" - Author: Li Hang: This book introduces the basic concepts and common methods of statistical learning, covering supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning. It is a very good introductory textbook.

3. "Deep Learning" - Author: Ian Goodfellow, Yoshua Bengio, Aaron Courville: This book is a classic textbook in the field of deep learning. It comprehensively introduces the basic principles, common models and training techniques of deep learning. It is suitable for readers with a certain mathematical foundation.

4. "Deep Learning with Python" - Author: Franois Cholet, Pierre Bol and Ian Goodfellow: This book uses Python as the programming language and introduces the basic principles of deep learning and common tool libraries, including TensorFlow and Keras, which is suitable for beginners.

5. "Machine Learning" - Author: Zhou Zhihua: This is a textbook that systematically introduces the theory, methods, and applications of machine learning. It has comprehensive content and a clear structure, making it suitable as an introductory reference book for machine learning.

The above are some book recommendations for electronic engineers to get started with machine learning and artificial intelligence. I hope they can help you get started quickly and understand the relevant knowledge.