This reporsitory includes my study notes of machine learning and here gives the index. 1. Statistical 1.1 Bayes inference 1.2 MLE/MAP_cost_function 1.3 Probability and inference 1.3.1 Important distributions M_distance/z-score 1.3.2 A/B test 1.3.3 Statistical inference Statistic test/Sampling distributions IID assumption Normality assumption Confidence interval 1.4 Experimental design 2. Linear algebra 3. Optimization 4. Machine learning concepts 4.1 Normalization/Standardization 4.2 Bias-variation 4.3 Overfitting- regularization/cross validation 4.4 Hyperparameter 4.5 Gradient descent 4.6 Cost function 4.7 Covariance matrix 5 Machine learning algorithms 5.1 Linear model Linear regression Logistic regression 5.2 Tree model 5.3 Statistical learning 5.5 Feature Engineering 6. Python for data science 6.1 Pandas 6.2 Numpy 6.3 Linear algebra 6.4 Statistical.. 6.5 Sklearn 7. SQL 8. Algorithms - leetcode 9. Important reference Study note R1