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docs/机器学习/ML.en.md

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+# Coursera: Machine Learning
+
+## Descriptions
+
+- Offered by: Stanford
+- Prerequisites: entry level of AI and proficient in Python
+- Programming Languages: Python
+- Difficulty: 🌟🌟🌟
+- Class Hour: 100 hours
+
+When it comes to Andrew Ng, no one in the AI community should be unaware of him. He is one of the founders of the famous online education platform [Coursera](https://www.coursera.org), and also a famous professor at Stanford. This introductory machine learning course must be one of his famous works (the other is his deep learning course), and has hundreds of thousands of learners on Coursera (note that this is people who paid for the certificate, which costs several hundred dollars), and the number of nonpaying learners should be far more than that.
+
+The class is extremely friendly to novices, and Andrew has the ability to make machine learning as straightforward as 1+1=2. You'll learn about linear regression, logistic regression, support vector machines, unsupervised learning, dimensionality reduction, anomaly detection, and recommender systems, etc. and solidify your understanding with hands-on programming. The quality of the assignments is evident, with detailed code frameworks and backgrounds drawn from life so that you can use what you've learned.
+
+Of course, as a public mooc, the difficulty of this course has been deliberately lowered, and many mathematical derivations are skimmed over. If you are interested in machine learning theory and want to investigate the mathematical theory behind these algorithms, you can refer to [CS229](. /CS229.md) and [CS189](. /CS189.md).
+
+## Course Resources
+
+- Course Website: <https://www.coursera.org/learn/machine-learning>
+- Recordings: refer to the course website
+- Textbook: None
+- Assignments: refer to the course website
+
+## Personal Resources
+
+Due to system reinstallation, my code disappeared. However, the course is so famous that you can easily find related resoures online. Also, course material is available on Coursera.