About the Course
This course provides a structured approach to teach the basics of machine learning, aimed at beginners. It covers the essential concepts, methodologies, and practical skills necessary to understand and apply in machine learning. Students will learn about the basics of Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) with the help of various real world applications in an interactive and fun way.
The course begins with an overview and brief history of artificial intelligence and subsequently covers the concepts of machine learning and its various algorithms with a real world examples. We follow a step by step approach to teach the complete process of ML, with a blend of theoretical lectures and practical hands-on sessions to ensure a well rounded learning experience. This course will also have a session for “python for machine learning” to ensure students have necessary python programming skills.
WHO CAN ENROL ON THIS COURSE?
The "Fundamentals of Machine Learning Using Python" is suitable for individuals, who has a keen interest in knowing how the world is progressing towards automation and how machine learning improves and makes the life of people easier. High demand for Machine Learning skills and wider career opportunities makes this course very suitable for all the school students.
PREREQUISITES FOR THIS COURSE
The prerequisite for this course is students are expected to have basic understanding of Python programming language and its libraries. We don’t expect to have complete understanding but knowing basics of python will help in the hands-on and the project. A session of python for machine learning is kept in the course module to ensure students have good understanding of libraries used in the project.
OUTCOMES OF THE COURSE
By the end of this course, students will be able to:
Understand the fundamental principles of machine learning and its various paradigms.
Pre-process data and perform feature engineering to improve model performance.
Implement and apply common machine learning algorithms to real world data sets.
Evaluate and compare the performance of different machine learning models.
Identify and implement the suitable algorithm on the given problem.