Course Module

Day-1 : Machine Learning (ML)

           §  Introduction

           §  ML : Models

           §  ML Algorithms: How it works

           §  ML Projects : Challenges

           §  ML : Limitations

           §  ML : Application Areas

Day-1 and Day-2: Python Basics for ML

           §  IDE : Spyder, Jupyter Notebook : ANACONDA

           §  Python:

                    o    Commands : Input and Output

                    o   Logical Statements

                    o   Loop and Control Structures

                    o   Functions and modules

                    o   Handling Classes

                    o   Handling Exceptions

                    o   Handling Files

                    o   Handling Strings

Day-3, Day-4 and Day-5 :  ML Algorithms  

            §  Overview

            §  Flow Structure : Machine Learning Modelling

            §  Pre-processing in ML

            §  Regression Algorithms

            §  Classification : Algorithms

            §  Clustering : Algorithms

            §  Neural Network

            §  SVM

            §  Python libraries : ML (Numpy, Pandas, Matplolib)

            §  Evaluation of ML Systems

Day-6, Day-7, Day-8 and Day-9 :  Machine Learning : Case Studies and Projects  

           §  Recommendation Generation

           §  Text Analysis and Mining

           §  Image Processing

           §  Predictive Analytics

Day-10 : ML Algorithms Optimization

           §  Need of ML

           §  Types of Optimization Techniques

           §  Basic Optimization Techniques

           §  Metaheuristic approaches to optimization

           §  Improvisation of ML by optimizing the learning parameter

           §  Optimization using Python

Course Outcomes:

• Classify the types of learning - supervised and unsupervised
• Identify the various uses and applications of machine learning algorithms
•  Perform various Machine Learning Techniques
• Understand various data and models
• Learn to Create Machine Learning models
• How to Implement different Regression models
• Enhance capability to choose the best algorithms among many for any given ML problem
• To Make accurate predictions and powerful analysis
•  Case Studies on ML algorithms including clustering, Image Processing and recommendation systems

Who can Attend:

• Research Scholars (any stream of Science or Engineering)
• Post Graduate Students (any stream of Science or Engineering)
• Students (any stream of Engineering)
• Any Individuals and Working Professional