Machine Learning

Part 1: Set up

• Generic Programming Structure, Jupter Notebooks, Tools, Python

Part 2: Regression

• Simple Linear Regression
• Multiple Linear Regression
• Polynomial Regression
• Gradient Descent
• Stochiastic Gradient Descent
• Support Vector Regression (SVR)
• Decision Tree Regression
• Random Forest Regression
• Evaluating Regression Models Performance
• Hands-on Assignments

Part 3: Classification

• Logistic Regression
• K-Nearest Neighbors (K-NN)
• Support Vector Machine (SVM)
• Kernel SVM
• Naive Bayes
• Decision Tree Classification
• Random Forest Classification
• Evaluating Classification Models Performance
• Hands-on Assignments

Part 4: Clustering

• K-Means Clustering
• Hierarchical Clustering
• Hands-on Assignments

Part 5: Association Rule Learning

• Apriori
• Eclat
• Hands-on Assignments

Part 6: Reinforcement Learning

• Upper Confidence Bound (UCB)
• Thompson Sampling

Part 7: Natural Language Processing(Introduction)

Part 8: Deep Learning(Introduction)

• Artificial Neural Networks
• Convolutional Neural Networks

Part 9: Dimensionality Reduction

• Principal Component Analysis (PCA)
• Linear Discriminant Analysis (LDA)
• Kernel PCA

Part 10: Model Selection & Boosting

• Model Selection
• Interview Prep : Grooming Session
• Bonus Lecture : Review