Lecture 1: Basics of Signals and Systems (Signal operations)

Lecture 5: Gradient Tape Continued: Higher Order Derivatives

Lecture 4: Gradient Tape

Lecture 3: TensorFlow Variables

Lecture 2: TensorFlow Constant, Reshape, Slicing and Broadcasting

Natural Language Processing 3: Text Pre-processing : Tokenization

Lecture 15: Support Vector Machine 3 (Soft SVM and Kernel SVM)

Lecture 14: Support Vector Machine 2

Lecture 13: Support Vector Machine (SVM) 1

ISRO Sponsored Generative Adversarial Network (GAN) Seminar

ISRO Sponsored Natural Language Processing (NLP) Seminar

Lecture 12: Perceptron Algorithm

Lecture 11: Logistic Regression 2

Lecture 10: Logistic Regression 1

Lecture 9: Proximal Gradient Descent

Lecture 8 : LASSO Regression

Lecture 7: Role of Regularization in Regression

Lecture 6 : Kernel Regression

Lecture 24 : Transformers

Lecture 5 : Non Linear Regression and Kernel Trick

Lecture 4: Linear Regression 2

Lecture 23

Lecture 1: Introduction

Lecture 2 : Basics of Machine Learning

Lecture 3: Linear Regression