Machine Learning (ML) is a multi-field interdisciplinary subject involving probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and other disciplines. It is the core of artificial intelligence and the fundamental way to make computers intelligent, and its applications are in all fields of artificial intelligence.
Therefore, the entry threshold for machine learning is very high. Fortunately, just now, Google officially launched a free Chinese machine learning crash course.
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The course includes:
***25 lessons**
***15 hours**
*** 40+ exercises **
The most important thing is that it is free, Chinese, officially produced by Google , and contains a lot of machine learning termbases, which is very suitable for beginners to learn.
Course URL: * https://developers.google.cn/machine-learning/crash-course/ *
Recently, I am also taking courses related to [Deep Learning] and [Machine Learning]. To be honest, I have been working for many years. Although it is okay to say that it is about the data structures and basic algorithms commonly used by computers, but for high school and The basic knowledge of mathematics related to the university has been returned to the teacher, and it is really very difficult to start learning.
If you are like me, the following sharing may be of some help to you.
Both [Deep Learning] and [Machine Learning] require a mathematical foundation, in particular:
linear algebra,
probability statistics,
calculus,
These three knowledge systems are very different from basic computer algorithms. For example, the related algorithms of List, Map, Tree, and Graph are commonly used programming algorithms, but they are fundamentally different from linear algebra, probability statistics, and calculus.
After working for a few years, I have almost forgotten this knowledge. In order to review this knowledge, I bought a lot of books, but most of the books are very obscure to read, and I don't understand why, and it is very painful to read.
But a few were so good that after reading them, I threw away the others altogether.
they are:
"Mathematics for Programmers"
Mathematics for Programmers 2: Probability and Statistics
Math for Programmers 3: Linear Algebra
The Princeton Calculus Reader
If you, like me, gave back all your high school and college math knowledge to your teachers, I suggest you buy these books.
1. "Programmer's Mathematics" is a series of books, which contains three books: "Programmer's Mathematics", "Programmer's Mathematics 2: Probability and Statistics", "Programmer's Mathematics 3: Linear Algebra" , this set The book is easy to understand, interspersed with a lot of interesting examples and diagrams. Great for beginners, it makes it easy for non-mathematicians to master basic computer algorithms, probability statistics, and thread algebra in a fun, easy-to-understand way.
Jingdong price: 150RMB (three copies).
2. "Princeton Calculus Reader" , it was originally used to provide weekly calculus review courses for first-year undergraduates at Princeton University. At the same time, I think this book is very affectionate, easy to understand, and can be read by people It's very easy, and if you don't finish the part, it will make people want to continue reading. For introductory calculus courses, I personally think this book is the best, no one.
Jingdong price: about 70RMB.
After reading these books, you should have the basic knowledge of linear algebra, probability statistics, and calculus. At this time, it is not too stressful to read other courses on artificial intelligence.
I don't know if my sharing can help you. If you have any better suggestions, please leave a message.
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