Machine Learning
- What is Machine Learning? How Does Machine Learning Work?
- Gradient Descent Algorithm
- Regression (Simple, Multiple, NonLinear)
- Regularization
- Bias-Variance Tradeoff
- Classification (Logistic Regression, Decision Tree, Support Vector Machine (SVM), k-Nearest Neighbors (kNN) , Bayesian Classification)
- Ensemble Learning: Parallel (Homogenous, Heterogenous) and Sequential Methods, Simple Ensemble Techniques (Max Voting, Averaging, Weighted Averaging), Advanced Ensemble techniques (Bagging, Boosting, Stacking, Blending)
- Clustering: Types of Clustering Algorithms (Centroid-based, Density-based, Connectivity-based and Distribution-based Clustering), Evaluating Clustering Methods, K-Means, Bisecting K-Means, Fuzzy C-Means, K-Medoids, DBSCAN, Hierarchical Clustering, Gaussian Mixture Model
- Dimensionality Reduction Methods: Principal component analysis (PCA), Singular Value Decomposition (SVD), Multidimensional scaling (MDS)
- Introduction to Neural Networks
- Reinforcement Learning