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Andrew-Ng-Machine-Learning-Notes

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The offical notes of Andrew Ng Machine Learning in Stanford University

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# Andrew-Ng-Machine-Learning-Notes
The notes of Andrew Ng Machine Learning in Stanford University
 * [1. Supervised learning, Linear Regression, LMS algorithm,  The normal equation, 
Probabilistic interpretat, Locally weighted linear regression , Classification and logistic regression,  The perceptron learning algorith, Generalized Linear Models, softmax regression](https://github.com/mxc19912008/Andrew-Ng-Machine-Learning-Notes/blob/master/cs229-notes1.pdf)
 * [2. Generative Learning algorithms, Gaussian discriminant analysis, Naive Bayes, Laplace smoothing, Multinomial event model](https://github.com/mxc19912008/Andrew-Ng-Machine-Learning-Notes/blob/master/cs229-notes2.pdf)
 * [3. SVM, Lagrange duality, Kernel, SMO](https://github.com/mxc19912008/Andrew-Ng-Machine-Learning-Notes/blob/master/cs229-notes3.pdf)
 * [4. Bias-Variance trade-off, Learning Theory](https://github.com/mxc19912008/Andrew-Ng-Machine-Learning-Notes/blob/master/cs229-notes4.pdf)
 * [5. Cross-validation, Feature Selection, Bayesian statistics and regularization](https://github.com/mxc19912008/Andrew-Ng-Machine-Learning-Notes/blob/master/cs229-notes5.pdf)
 * [6. Online Learning, Online Learning with Perceptron](https://github.com/mxc19912008/Andrew-Ng-Machine-Learning-Notes/blob/master/cs229-notes6.pdf)
 * [7a. K-Means](https://github.com/mxc19912008/Andrew-Ng-Machine-Learning-Notes/blob/master/cs229-notes7a.pdf)
 * [7b. EM Algorithm, GMM](https://github.com/mxc19912008/Andrew-Ng-Machine-Learning-Notes/blob/master/cs229-notes7b.pdf)
 * [8. EM Algorithm in detail](https://github.com/mxc19912008/Andrew-Ng-Machine-Learning-Notes/blob/master/cs229-notes8.pdf)
 * [9. Factor Analysis, EM for Factor Analysis](https://github.com/mxc19912008/Andrew-Ng-Machine-Learning-Notes/blob/master/cs229-notes9.pdf)
 * [10. PCA](https://github.com/mxc19912008/Andrew-Ng-Machine-Learning-Notes/blob/master/cs229-notes10.pdf)
 * [11. Independent Component Analysis(ICA)](https://github.com/mxc19912008/Andrew-Ng-Machine-Learning-Notes/blob/master/cs229-notes11.pdf)
 * [12. Reinforcement Learning](https://github.com/mxc19912008/Andrew-Ng-Machine-Learning-Notes/blob/master/cs229-notes12.pdf)
 
 * [Tree based ML algorithms](https://www.analyticsvidhya.com/blog/2016/04/tree-based-algorithms-complete-tutorial-scratch-in-python/)
 * [Deep Learning Notes](https://github.com/mbadry1/DeepLearning.ai-Summary)

Andrew-Ng-Machine-Learning-Notes

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