•Fundamentals of Statistical Modeling and Machine Learning Techniques
•The Course Overview
•Machine Learning
•Statistical Terminology for Model Building and Validation
•Bias Versus Variance TradeOff
•Linear Regression Versus Gradient Descent
•Machine Learning Losses
•Train Validation and Test Data
•CrossValidation and Grid Search
•Machine Learning Model Overview
•Compensating Factors in Machine Learning Models
•Simple Linear Regression from First Principles
•Simple Linear Regression Using Wine Quality Data
•MultiLinear Regression
•Linear Regression Model Ridge Regression
•Linear Regression Model Lasso Regression
•Maximum Likelihood Estimation
•Logistic Regression
•Random Forest
•Variable Importance Plot
•Test your knowledge
•Advanced Statistics for Machine Learning
•Artificial Neural Networks
•Forward Propagation and Back Propagation
•Optimization of Neural Networks
•ANN Classifier Applied on Handwritten Digits
•Introduction to Deep Learning
•Kmeans Clustering
•Principal Component Analysis
•Singular Value Decomposition
•Deep Autoencoders
•Deep Autoencoders Applied on Handwritten Digits
•Introduction to Reinforcement Learning
•Reinforcement Learning Basics
•Markov Decision Process and Bellman Equations
•Dynamic Programming
•Monte Carlo Methods
•Temporal Difference Learning
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