📘 Fundamentals of Machine Learning - Lectures

Lecture 0- Course Introduction Lecture 1- Basic Linear Algebra Lecture 2- Eigenvalue Decomposition Lecture 3- Singular Value Decomposition Lecture 4 - Kernal Function and Norm Lecture 5- Definite Matrices Lecture 6- Vector Calculas Lecture 7-Principal Component Analysis Lecture 8- Gaussian Random Vectors Lecture 9-Optimization Techniques Introduction-1 Lecture 10-Optimization Techniques Introduction-2 Lecture 11- Convex and Non-convex Optimization Lecture 12-Linear and Non-Linear Programming Lecture 13-Qudratic Programming and KKT Lecture 14-Optimization for ML in Practice Lecture 15- Examples-Linear Regression Lecture 16- Example-Logistic Regression Lecture 17- Example-1 Lecture 18- Example_2 Lecture 19- Example_3 Lecture 20- Example_4 Lecture 21-Introduction to ML-1 Lecture 22-Introduction to ML-2 Lecture 23-Introduction to ML-3 Lecture 24-Under Process Lecture 25-Under Process Lecture 26-Under Process Lecture 27-Under Process Lecture 28-Under Process Lecture 29-Under Process Lecture 30-Under Process Lecture 31-Under Process Lecture 32-Under Process Lecture 33-Under Process Lecture 34-Under Process Lecture 35-Under Process Lecture 36-Under Process Lecture 37-Under Process Lecture 38-Under Process Lecture 39-Under Process Lecture 40-Under Process Lecture 41-Under Process Lecture 42-Under Process Lecture 43-Under Process Lecture 44-Under Process Lab Experiment 1 Lab Experiment 2 Lab Experiment 3 Lab Experiment 4 Lab Experiment 5 Lab Experiment 6 Lab Experiment 7 Lab Experiment 8 Lab Experiment 9 Lab Experiment 10 Lab Experiment 11 Lab Experiment 12