Machine-Learning-Study-Note

Study Note of Machine Learning

Record the original codes in the video.

If it may be a litter helpful for you, please star me.

Thank you!

Reference: https://www.cnblogs.com/volcao/category/1193714.html

Original website： https://www.bilibili.com/video/BV1gg4y167o3?

or 链接: https://pan.baidu.com/s/1ximmFMsjy7MBqA4EIjiT7A 提取码: 3ffy

Name		Name	Last commit message	Last commit date
Latest commit History 63 Commits
KNNClassifier		KNNClassifier
attachment		attachment
kNN_boxing		kNN_boxing
my_module		my_module
playML		playML
01.jupyter notebook的魔法命令.ipynb		01.jupyter notebook的魔法命令.ipynb
02.Numpy.array基础.ipynb		02.Numpy.array基础.ipynb
03. Numpy.array的基本操作.ipynb		03. Numpy.array的基本操作.ipynb
04. numpy.array中的运算.ipynb		04. numpy.array中的运算.ipynb
05. 聚合操作.ipynb		05. 聚合操作.ipynb
06. Fancy Indexing.ipynb		06. Fancy Indexing.ipynb
07. matplotlib基础.ipynb		07. matplotlib基础.ipynb
08. 读取数据和简单的数据探索.ipynb		08. 读取数据和简单的数据探索.ipynb
09. k~Nearest Neighbor.ipynb		09. k~Nearest Neighbor.ipynb
10. scikit-learn packaging .ipynb		10. scikit-learn packaging .ipynb
11. Train Test Split.ipynb		11. Train Test Split.ipynb
12. Accuracy.ipynb		12. Accuracy.ipynb
13. Hyperparameter.ipynb		13. Hyperparameter.ipynb
14. Grid Search.ipynb		14. Grid Search.ipynb
15. Feature Scaling.ipynb		15. Feature Scaling.ipynb
16. Scikit-learn Scaler.ipynb		16. Scikit-learn Scaler.ipynb
17. Linear Regression.ipynb		17. Linear Regression.ipynb
18. Accuracy of Linear Refression.ipynb		18. Accuracy of Linear Refression.ipynb
19. Multiple Linear Regression.ipynb		19. Multiple Linear Regression.ipynb
20. Gradient Descent.ipynb		20. Gradient Descent.ipynb
21. Gradient Descent in Linear Regression.ipynb		21. Gradient Descent in Linear Regression.ipynb
22. Stochastic Gradient Descent.ipynb		22. Stochastic Gradient Descent.ipynb
23. Debug Gradient Descent.ipynb		23. Debug Gradient Descent.ipynb
24. Principal Component Analysis.ipynb		24. Principal Component Analysis.ipynb
25. High-dimensional data to low-dimensional data.ipynb		25. High-dimensional data to low-dimensional data.ipynb
26. Noise Reduction by PCA.ipynb		26. Noise Reduction by PCA.ipynb
27. Polynomial Regression.ipynb		27. Polynomial Regression.ipynb
28. Overfitting and Underfitting.ipynb		28. Overfitting and Underfitting.ipynb
29. Cross Validation.ipynb		29. Cross Validation.ipynb
30. Regularization and Ridge Regression.ipynb		30. Regularization and Ridge Regression.ipynb
31. LASSO Regularization.ipynb		31. LASSO Regularization.ipynb
32. Logistic Regression.ipynb		32. Logistic Regression.ipynb
33. OvR and OvO.ipynb		33. OvR and OvO.ipynb
34. Accuracy.ipynb		34. Accuracy.ipynb
35. SVM(Support Vector Machine).ipynb		35. SVM(Support Vector Machine).ipynb
36. Kernel Function in SVM.ipynb		36. Kernel Function in SVM.ipynb
37. Decision Tree.ipynb		37. Decision Tree.ipynb
38. Information Entropy and Gina Coefficient.ipynb		38. Information Entropy and Gina Coefficient.ipynb
39. CART (Classification and Regression Tree).ipynb		39. CART (Classification and Regression Tree).ipynb
40. Limitation of Decision Tree.ipynb		40. Limitation of Decision Tree.ipynb
41. Ensemble Learning.ipynb		41. Ensemble Learning.ipynb
42. Random Forest.ipynb		42. Random Forest.ipynb
README.md		README.md

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