**Requirements**

- 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)

**Description**

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

**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