Lecture 1 - Introduction to Data Science, Artificial Intelligence, and Machine Learning
Lecture 2 - Fundamentals of Machine Learning
Lecture 3 - Probability and Statistical Foundations for Machine Learning - I
Lecture 4 - Probability and Statistical Foundations for Machine Learning - II
Lecture 5 - Probability Distributions for Discrete and Continuous Random Variables
Lecture 6 - Transforming Random Variables and Their Distributions
Lecture 7 - Understanding Population and Sample Statistics - I
Lecture 8 - Understanding Population and Sample Statistics - II
Lecture 9 - Multivariate Linear Regression and Model Evaluation
Lecture 10 - Hypothesis Testing and Confidence Intervals: Z-Test and T-Test
Lecture 11 - Overfitting, Underfitting, and Ridge Regression
Lecture 12 - LASSO and Elastic Net Regularization Techniques
Lecture 13 - Bias-Variance TradeOff in Machine Learning
Lecture 14 - Logistic Regression and Evaluation of Binary Classification Models
Lecture 15 - ROC Analysis and Multiclass Classification
Lecture 16 - Tutorial - I (Introduction to Python)
Lecture 17 - Loss Functions - I
Lecture 18 - Loss Functions - II
Lecture 19 - Loss Functions - III
Lecture 20 - Loss Functions - IV
Lecture 21 - Training ML Models: Gradient Descent
Lecture 22 - Training ML Models: Gradient Descent and Hessian Matrix Analysis
Lecture 23 - Practical Aspects of ML Model Training and Stochastic Gradient Descent
Lecture 24 - Variants of Stochastic Gradient Descent for ML Model Training
Lecture 25 - Gradient Descent Applied to Toy Quadratic Regression
Lecture 26 - Cross Validation in Machine Learning
Lecture 27 - Hyperparameter Tuning for ML Models
Lecture 28 - Tutorial - II (Linear Regression using Python)
Lecture 29 - Tutorial - III (Ridge and LASSO Regression, Cross Validation and Hyperparameter tuning using Python)
Lecture 30 - Unsupervised Learning: Principal Component Analysis (PCA)
Lecture 31 - Singular Value Decomposition and PCA
Lecture 32 - Fundamentals of Clustering with K-means
Lecture 33 - Advanced Clustering Techniques
Lecture 34 - Nonlinear Dimensionality Reduction Techniques - I
Lecture 35 - Nonlinear Dimensionality Reduction Techniques - II
Lecture 36 - Unsupervised Learning on Toy Datasets
Lecture 37 - Decision Trees in Supervised ML
Lecture 38 - Decision Trees for Regression and Classification
Lecture 39 - Classification using Simple Decision Trees
Lecture 40 - Ensemble Learning
Lecture 41 - Random Forest and Boosting of Decision Trees
Lecture 42 - Adaptive Boosting (AdaBoost)
Lecture 43 - Unsupervised Learning in Python (Tutorial 1)
Lecture 44 - Unsupervised Learning in Python (Tutorial 2)
Lecture 45 - Gradient Boosted Decision Trees and Advanced Boosting Libraries
Lecture 46 - Decision Stump
Lecture 47 - Support Vector Machines (SVM)
Lecture 48 - Mathematics of SVM Margins
Lecture 49 - Support Vector Regression (SVR)
Lecture 50 - Kernel Ridge Regression (KRR)
Lecture 51 - Gaussian Process Regression (GPR)
Lecture 52 - Supervised Machine Learning Tutorial with Python (Tutorial I)
Lecture 53 - Supervised Machine Learning Tutorial with Python (Tutorial II)
Lecture 54 - Supervised Machine Learning Tutorial with Python (Tutorial III)
Lecture 55 - Introduction to Deep Learning and Neural Networks
Lecture 56 - Overview of Advanced Neural Network Architectures: From CNNs to GANs and GNNs
Lecture 57 - Mathematical Foundation and Activation Functions of Neural Networks
Lecture 58 - Training an Artificial Neural Network: Forward Propagation, Backpropagation, and Hyperparameters
Lecture 59 - Challenges in Training Neural Networks and their Mitigation
Lecture 60 - Neural Network Challenges (Gradients, Overfitting) and Logic Gate Implementation
Lecture 61 - Recurrent Neural Networks and Sequential Data Processing
Lecture 62 - Graph Neural Networks and Generative AI Fundamentals
Lecture 63 - Variational Autoencoders and Bayesian Generative Modeling
Lecture 64 - Neural Networks with Tensorflow (Tutorial I)
Lecture 65 - Neural Networks with Tensorflow (Tutorial II)
