Theoretical Machine Learning From Scratch - Linear Models
What you'll learn
- Understand the math behind linear models particularly linear and logistic regression
- Uncover the black box understand the inner workings of linear and logistic regression
- Understand gradient descent in a great detail and apply it to solving problems
- Learn to apply the linear models to machine learning problems and use cases
- Code everything from scratch without using any ready made machine learning library
- Basic to intermediate programming skills(program flow, conditional statements, looping, object oriented approach)
- Taking derivative and partial derivatives using calculus
- Some basic probability and statistics
- Basic linear algebra(matrix multiplication)
This course will be your guide to learning how to use the power of theory, math and python to create linear regression and logistic regression, two of most popular and useful machine learning models from scratch.
This course is designed for folks with some programming experience or experienced developers looking to make the jump to data science and machine learning, I'll teach you how to dive deep into the math behind the linear models in an easy and understandable way. Once, you have understood the inner workings of the linear models and uncovered the black box, you are ready to code everything from the ground up without using any fancy ready made machine learning libraries and yes you will be taught that too! The course is beneficial for understanding the machine learning concepts deeply rather than just using some library to get results, it will guide you in the right direction for learning many other machine learning and deep learning algorithms, as this course covers all the basics required, you will be well on your way to becoming an expert Data Scientist!
Since this course goes deep into the math and has coding from scratch, a basic to intermediate knowledge of coding is a must, also good idea of derivatives(calculus), linear algebra(matrix multiplication) and basic probability is required to get the full out of this course.
Enroll today to go beyond!
Who this course is for:
- This course is meant for people who want to go beyond the basic understanding of machine learning paradigms and dive deeper into the math and theory
I have a BS in Computer Science along with an MS in Machine Learning and I'm currently working as a Machine Learning Engineer for a Multinational Company. I have rich programming expertise working with languages like C, C++, Java and most prominently Python and R.
I've created various Machine Learning as well as Deep Learning models for multiple projects in my company for use cases such as anomaly detection, incident prediction and marketing.
My aim on Udemy is to equip students with both the theoretical as well as the practical knowledge required to flourish in the field of Data Science and Machine Learning.