Summary and thank you 总结

Summary: Main topics

Supervised Learning

1. Linear regression
2. Logistic regression
3. Neural networks
4. SVMs

Unsupervised Learning

1. K-means
2. PCA
3. Anomaly detection

Special applications/Special topics

1. Recommender systems
2. Large scale machine learning

Advice on building a machine learning system

1. Bias/Variance
2. Regularization
3. Deciding what to work on next
4. Evaluation of learning algorithms
5. Learning curves
6. Error analysis
7. Ceiling analysis

机器学习（Machine Learning）- 吴恩达（Andrew Ng） 学习笔记（十九）

原文：https://www.cnblogs.com/songjy11611/p/12326628.html