Recently, I have used my spare time to learn machine learning. During this period, I collected a lot of learning materials, including public classes and lectures at Stanford/Cornell and other universities, as well as original English books in the field of machine learning. Personally, I think it is more in-depth and accurate than the introductory course with unknown sources in major domestic communities, or the authoritative information of the first hand, so why not eat minced meat?
In addition, the field of machine learning is abundant and wide, and its application is even more extensive than it can be. As an ML white, the content of this article will not be more complete, not seeking perfection, but merely throwing bricks to attract jade and exchanging learning together.
1. Computer Foundation
- Getting Started with python:
- Getting started with Java:
- Discrete mathematics:
- Operating system:
2. Mathematical and Statistical Basis
- Linear algebra
- Basic Statistics
- Real Analysis and Functional Analysis [Not Required]
3. Getting Started
- Introduction to Machine Learning: This lesson in Andrew Ng may be unknown to everyone
- Introduction to Statistical LearningThe programming in this book is very light, but it is still good as intuition training and thinking training.
- Introduction to Deep Learning: This lesson in Andrew Ng is also good for getting started and studying deeply.
- Introduction to Artificial Intelligence: Udacity’s first flagship AI course, based on an undergraduate course at Stanford University, will involve a broader concept than previous introductory courses.
- [Mining Massive Data Sets
](http://web.stanford.edu/class …The previous courses were mainly about supervised learning. this Stanford course is slightly broader and not entirely machine learning, but it is helpful to expand knowledge and practice.
- Advanced machine learning: Cornell University’s machine learning course, unfortunately there is no video, only courseware, and the part about actual combat is still good.
- 《The Elements of Statistical Learning》: classic ESL
- 《Pattern Recognition and Machine Learning》A classic book
- Information extraction and search
- Recommendation system
- Image recognition
- stanford CS231nAndrej’s version is a classic
- natural language processing
- Strengthen study
- Unmanned vehicle
7. Extended Precautions
Some of the whitest introductions can be skipped by big brothers.
- Distributed system:
- system design
- Distributed OLTP and OLAP
8. Machine Learning System and Platform
- What is a machine learning system?
- Machine learning platform
- FB Learner flow [https://code.fb.com/ml-applications/introducing-fblearner-flow-facebook-s-ai-backbone/](https://code.fb.com/ml-applications/introducing-fblearner-flow-facebook-s-ai-backbone/)
- Michelangelo [https://eng.uber.com/scaling-michelangelo/](https://eng.uber.com/scaling-michelangelo/)
- Deep learning Horovodhttps://eng.uber.com/horovod/
In the process of web development, we have all seen or used some strange techniques and tricks. this technique is collectively referred to as black magic. these black magic are scattered in all corners. in order to make it convenient for everyone to consult and learn, we have collected, sorted out and classified them, and made a project on github-awesome-blackmargic, I hope you love to study the developers can like, also hope you can share their unique skills, if you are interested can send us pr.
Welcome to join our QQ group (784383520) and learn together.