CS
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Session Details
About
This is an introduction to supervised machine learning. In this session we will learn what supervised machine learning is and see its application. The main focus of session will be on linear regression, an introductory machine learning algorithm which is easy to understand yet very powerful. We will understand single variable linear regression as well as multiple linear regression, we will also see polynomial regression. We will understand the whole process of parameter optimization with an algorithm called gradient descent. In the end, all the learnings will be implemented in Python from scratch.
Expected pre-requisites: Understanding of basic algebra and coordinate geometry. All other advanced math needed will be dealt with in the session itself.
working knowledge of python is also good to have, however not required. Basic understanding of programming principles will be needed for the implementation part of the session, but not required for theory part.
Tutor Qualifications
I understand the material very well, have multiple certifications from MOOCs, have applied the concepts in real life projects.