This repository includes my understanding of some ideas in machine learning

The first part ---> The study of Machine Learning in Action

Classification

1. C2_KNN

2. C3_Trees

3. C4_Bayes

4. C5_LogisticRegression

5. C6_SVM

6. C7_AdaBoost

Prediction

7. C8_Regression

8. C9_CART

Unsupervised Learning

9. C10_K-means

10. C11_Apriori

11. C12_FP-growth

Compelmentary tools

12. C13_PCA

13. C14_SVD

The second part ---> The study of Coursera--Machine Learning by Andrew Ng

1. Linear Regression

2. Logistic Regression

3. Multi-class Classification and Neural Networks

4. Neural Networks Learning

5. Regularized Linear Regression and Bias v.s. Variance

6. Support Vector Machines

7. K-means Clustering and Principal Component Analysis

8. Anomaly Detection and Recommender Systems

The third part ---> The study of Computer Vision (CS231N) by Fei-Fei Li

A1-1 KNN

A1-2 SVM

A1-3 Softmax

A1-4 FCN

A2-1 BN

A2-2 Dropout

A2-3 SGD with Momentum

A2-4 CNN

A3-1 RNN

A3-2 LSTM

A3-3 Generating Captions