Academic Advisor - Prof. (Dr.) S.K. Yadav

M.Sc., M.Phil., M.Tech, Ph D., M.B.A, FIETE, MAeSI, MIIM, MAIMA, MIE(I), Former Director (Academic & Research), JJT university

Introduction

Welcome to our online course on Machine Learning, where you'll dive into essential concepts and techniques that power modern AI applications. This course covers a broad spectrum of topics, including Classification, Regression, and Neural Networks, providing a strong foundation in both basic and advanced methods. You'll explore Feature Selection and Extraction to enhance model performance and delve into Association Rules and Clustering for insightful data analysis. Practical Python programming sessions will enable you to implement these techniques effectively. Additionally, you'll learn Decision Tree Learning and the fundamentals of Reinforcement Learning, equipping you with the skills to tackle complex real-world problems. Join us on this exciting journey to master Machine Learning and unlock the potential of your data.

What You Get in This Course?

1. Module-Wise Study Material: A structured approach to learning, with In-depth Study Materials provided for each module to understand the topic better in an easy format.
2. Video Tutorials: Module-wise video tutorials that explain each topic briefly through textual, pictorial and graphical with audio explanation representations to enhance your learning experience.
3. Important Questions with Answers: This section includes essential questions with their answers for each module to solidify the student’s knowledge.
4. Practice Interview Questions: After studying each module, check yourself with practice questions that will prepare you for job interviews & real-world scenarios.
5. Final Exam Quiz: A comprehensive quiz at the end of the course to test the student’s overall Study and Knowledge of the course
6. Course Completion Certificate: A Course Study Completion Certificate is awarded upon achieving at least 40% marks in the Final Exam Quiz.

What will you learn

• Introduction to Machine Learning Methods
• Classification
• Regression
• Neural Networks and Deep Learning
• Feature Selection and Extraction
• Association Rules
• Clustering
• Machine Learning – Programming Using Python
• Decision Tree Learning
• Reinforcement Learning
• Genetic Algorithms