Artificial Intelligence (Machine Learning) Learning Route

1. A must-see for beginners: artificial intelligence (machine learning) learning route2020-08-22

https://www.bilibili.com/read/cv7272427/

At present, artificial intelligence, machine learning, and natural language processing have become the mainstream of Internet technology.

As of 2020, major universities across the country are gradually building artificial intelligence colleges, such as: Shenzhen University, Chongqing College of the University of Chinese Academy of Sciences, Nanjing University of Information Engineering and other double-first-class construction universities.

Machine Learning
However, when many students first apply for the major of artificial intelligence, they feel that it is very advanced and has a strong sense of technology; but when they really get in touch with it, they are a little strange.

In fact, artificial intelligence and machine learning are a professional direction that combines mathematics, statistics, computer technology, neural network, electronic information and other disciplines. But because of this, many people have taken a detour, because they did not learn systematically and in a planned way at the beginning; in this column, I will share with you how to quickly and systematically master these technologies based on my own experience.

Learning route:

1. Programming language: python

Due to the advantages of python itself, such as simple and fast program writing, simple and powerful functions, high code development efficiency, and high scalability; it is suitable for beginners and easy to learn; and python's TensorFlow framework can assist us in the following machine learning algorithms (such as: SVM etc.) learning

2. Programming software installation: pycharm, Octave

pycharm: It is a python language compiler, you can go to the official website to download; if you are a student at school, you can use the education mailbox to activate it, free to use

Octave: is the software used in machine learning, the official website can be downloaded for free

3. Theoretical knowledge learning route:

For beginners in machine learning, Mr. Zhou Zhihua's "Watermelon Book" may be recommended by many people; however, if you are a beginner or a student who is not very strong in mathematics (advanced numbers, line algebra, probability), it is not recommended. Look; you can follow the following route to learn:

1. Mathematical theoretical knowledge:

Li Hang "Statistical Learning Methods" Second Edition

"Matrix Theory" Northwestern Polytechnical University Press (mainly understand matrix derivation rules, Hessian matrix, paradigm, etc.)

Stephen_Boyd "Convex Optimization" (translated by Wang Shuning)

After reading the above mathematical foundations, you can study professional theory and practice.

2. Professional theoretical knowledge

An Introduction to Machine Learning Theory

Zhou Zhihua's "Machine Learning" supporting "Pumpkin Book"

Video: Ng Enda "Machine Learning"

Three, actual combat

"Deep Learning" Huashu AI

"Hands-On Deep Learning"

2. A collection of learning routes and learning resources for beginners in artificial intelligence (including python/machine learning/deep learning/tensorflow) 2020-04-09

https://www.cnblogs.com/zhengzhicong/p/12670260.html

3. Machine learning route

First, the language suggests python

Second, the basics of mathematics, calculus, probability theory, linear algebra, convex functions, etc. Can you learn without these mathematical knowledge? OK. But it will be difficult to improve, perfect, and add your own ideas

Third, basic knowledge, machine learning concepts, classification, model evaluation, performance measurement. Basic models: linear models, decision trees, artificial neural networks, support vector machines, Bayesian classification, integrated learning, clustering and other knowledge.

Fourth, advanced, feature engineering, dimensionality reduction, etc. Also, feature selection, sparse selection, computational learning theory, semi-supervised learning, probabilistic graphical models, rule learning.

Fifth, integration, research, and mastery.

4. Python deep learning entry learning route (simple and quick without hair loss) 2020-11-15

https://blog.csdn.net/weixin_44414948/article/details/109704871