The reason for Machine Learning is to find designs in your information and afterward make forecasts dependent on regularly complex examples to respond to business questions, recognize and break down patterns and help take care of issues. Machine leaning in business and different fields is viably a strategy for information investigation that works via robotizing the way toward building information models.

Machine Learning in business and different territories, for example, human services and legislative offices isn’t just another term for AI (Artificial Intelligence). While AI is the umbrella term given for machines copying human capacities, AI is a particular part of AI where machines are prepared to figure out how to process and utilize information; another depiction regularly utilized is ‘machine knowledge’.

2.         How Machine Learning is used in business and different territories

3.         Different algorithm used in Machine Learning.

4.         How Python is used in Machine Learning.

5.         The ultimate goal is to improve the learning in such a way that it becomes automatic.

1. Introduction to ML

2. ML Life Cycle

3. Supervised Learning

4. Unsupervised Learning

5. Real-World Applications

DAY 2

1. Tools for performing ML: Weka, Python, R

2. Hands-On Weka Tool

DAY 3

1. Supervised Learning Model

2. Example: K-Nearest Neighbour, Decision Tree,  Random Forest, Support Vector Machine

DAY 4

1. Unsupervised Learning Model

2. Example: Partitioning and Hierarchical Clustering, k-Means, k-Medoid

DAY 5

1. Introduction and Implementation of Convolutional Neural Network

Dr. Jasminder Kaur Sandhu

Mr. Varun Sapra

jasminder.sandhu@chitkara.edu.in

varun.sapra@ddn.upes.ac.in

Assistant Professor

Assistant Professor

Chitkara University, Chandigarh

UPES, Dehradun