Machine Learning is a wide range field, which mainly includes the following four parts:
- Supervised Learning
- Unsupervised Learning
- Weakly Supervised Learning
- Reinforcement Learning
What's more, deep learning is a sub-field of machine learning. Knowing what machine learning includes, understanding the mathematical principles and implementation details of these contents, and learning the specific ideas behind them are far more important than just calling python libraries to solve certain tasks. I have been playing machine learning for more than 2 years, including taking various machine learning-related courses and completing machine learning-related tasks. These courses include:
- Standford MachineLearning course (taught by Andrew Ng)
- Coursera DeepLearning course (taught by Andrew Ng)
- SJTU CS420 Machine Learning course
- SJTU CS410 Artificial Intelligence course
- SJTU CS485 Reinforcement Learning course
- CSDN machine learning course (taught by Shunxiang Liu)
The machine learning-related books I have read include:
- Professor Zhou Zhihua’s "machine learning book" (watermelon book)
- Ian Goodfellow's "Deep Learning" book (flower book)
- Francois Chollet's "Deep Learning with Python"
- Zhuge Yue's "The Quest for Machine Learning" (Hulu book)
They are all very helpful to me. The machine learning related projects (codes and their corresponding description, reports .etc) are also included in this repository. In short, this repository contains a lot of content, and I hope it can be helpful to you.