L01 - Course Overview

L02 - Linear Algebra & Statistical Methods Refresher

L03 - Linear Regression - Analytical & Numerical Approaches

L04 - Defining Bias and Variance of Data

L05 - Regularized Linear Regression (Lasso, Ridge, Elastic Net)

L06 - Evaluating Regression Models - R squared, RMSE, MAE, MAPE

L07 - Logistic Regression - Constructing Decision Boundaries

L08 - Regularized Logistic Regression

L09 - Evaluating Classification Models - ROC AUC, Confusion Matrix

L10 - Support Vector Machines - Hyperplane, Margin Separation, Kernels

L11 - Neural Networks - Forward Propagation, Activation Functions

L12 - Neural Networks - Backward Propagation, Gradient Descent

L13 - Tree Ensembling Techniques

L14 - Advanced Bagging - Random Forest

L15 - Advanced Boosting - AdaBoost, XGBoost

L16 - Advanced Boosting - LightGBM, CatBoost

L17 - Hyperparameter Tuning - Grid Search, Random Search, Bayesian Search

L18 - Clustering Techniques

L19 - Novelty and Outlier Detection Techniques

L20 - Dimensionality Reduction Techniques

L21 - Recommender systems - Content-based & Collaborative filtering

L22 - Hybrid Recommender Systems and Supervised Approach

L23 - Sampling Methods and Handling Class Imbalance

L24 - Orthogonalization, Metric of Interest, Sizing datasets

L25 - Structuring a ML Project, Distribution matching

L26 - Defining the Problem Statement

L27 - Modeling Approaches

L28 - Writing Capstone Project in your Resume

L29 - Nailing your Data Science Interviews

L30 - One-on-One Mock Interview