1. This class will be conducted once in a week.

2. it's contents are essentials of Machine Learning.

3. Learning , Supervised & Unsupervised Learning , Reinforcement Learning.

4. Students shoud brush up basics of statastics before attending class.

5. Student must required gmail account to perform programs of machine learning.

Course Objective: Machine learning concerns with designing and developing of algorithms that allow machines, essentially computers, to evolve realistic or human like behavior based on the empirical data available. This course aims to discuss the building blocks of Computer vision and Natural Language Processing problems and provide an overview of the machine leaning and advance topics.

Contents :

Introduction to Machine Learning, Statistical Learning, Supervise Learning, Unsupervised Learning, Reinforcement Learning, Linear Algebra basics, Probability Basics

Linear Regression: Simple Linear Regression, Multiple Linear Regression

Classification : Logistic Regression : Cost function, problem of overfitting, Regularization, Support vector machine : support vector, kernel, K-nearest Neighbor(KNN)

Resampling Methods and Evaluation: Cross-Validation,The Validation Set Approach, Leave-One-Out CrossValidation, k-Fold Cross-Validation Bias-Variance Trade-Off for k-Fold CrossValidation ,Cross-Validation on Classification Problems, The Bootstrap, ROC curve, confusion matrix, Precision, Recall, F-score

Clustering : K-means, Hierarchical clustering

Reinforcement Learning : Introduction , RL Framework