DIY_MachLearning
a machine learning basic algorithms study in Python
Stuff implemented/explored:
- Gradient Descent
- (Linear regression)
- Logistic regression
cs229 notes
- Naive Bayes Classifier source1, sklearn docs
- Gaussian Discriminant Analysis cs229 notes
- SVM. Kernels article, cs229 notes
- K-means cs221 notes
- PCA cs229 notes
- ICA data, paper1, cs229 notes
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- Maximum likelihood estimation PennState stats course
- Generalized linear models metacademy, wiki, sklearn docs
- Regularization cs229 notes
- Neural Networks (brief overview)
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