Machine Learning Notes Notebook 00 Python Basics NumPy NumPy array manipulation NumPy calculation (linalg, matmul, etc.) Matplotlib Simple plot Plot styles Subplots 3D plot Notebook 01 Regression Linear Model LASSO and Ridge Notebook 02 Feature Feature exploration and visualization Notebook 03 Bias and Variance Bias and variance decomposition Notebook 04 Maximum Likelihood Estimation MLE on Normal Distribution Compare biased and unbiased estimators Notebook 05 Optimization Methods Gradient decent Newton's method Notebook 06 Classification Perceptron Logistic Regression Softmax Regression Notebook 07 Nonparametric Modeling kNN Decision Tree The curse of dimensionality Notebook 08 Ensemble Learning Bootstrap Aggregation (Bagging) Random Forest Regression Adaptive Boosting (AdaBoost) Gradient Boost Notebook 09 Neural Learning Notebook 10 Unsupervised Learning PCA k-Means Notebook 11 PyTorch