Week 1: Introduction to Machine Learning; Review of Probability Theory; Review of Linear Algebra
Week 2: Review of Linear Algebra continued; Linear Regression; k-Nearest Neighbors Regression; Kernel Regression + (Tutorial-1: Hands On Python Examples)
Week 3: Continuation - Review of Linear Algebra continued; Linear Regression; k-Nearest Neighbors Regression; Kernel Regression + (Tutorial-1: Hands On Python Examples),
Week 4: Logistic Regression + (Tutorial-2: Hands On Python Examples)
Week 5: Multilayer Perceptron (MLP)/NN and Optimization + (Tutorial-3: Hands On Python Examples)
Week 6: Practical Machine Learning: Bias-Variance; Training/Testing; Overfitting; Cross-Validation; Occam's razor; Regularization and Model Selection (Tutorial-3: Hands On Python Examples continued)
Week 7: Support Vector Machines; Radial Basis Functions and Kernel SVMs + (Tutorial-4: Hands On Python Examples)
Week 8: Continuation - Support Vector Machines; Radial Basis Functions and Kernel SVMs + (Tutorial-4: Hands On Python Examples)
Week 9: Naïve Bayes Classification; Decision Tree & Random Forests; Bagging & Boosting + (Tutorial-5: Hands On Python Examples)
Week 10: Clustering: K-means/Kernel K-means, K-NN classifier; Spectral Clustering; Mixture of Gaussians; Dimensionality Reduction: PCA and kernel PCA+ (Tutorial-6: Hands On Python Examples)
Week 11: Continuation - Clustering: K-means/Kernel K-means, K-NN classifier; Spectral Clustering; Mixture of Gaussians; Dimensionality Reduction: PCA and kernel PCA+ (Tutorial-6: Hands On Python Examples)
Week 12: Introduction to Deep Learning: CNN for Image Classification and Autoencoders + (Tutorial-7: Hands On Python Examples)
DOWNLOAD APP
FOLLOW US