A comprehensive course in Logistic and Linear Regression

Understand ML models through first principle,develop mathematical understanding,build intuition & work out case studies

Ratings: 4.77 / 5.00




Description

A COMPREHENSIVE COURSE IN LOGISTIC AND LINEAR REGRESSION  IS SET UP TO MAKE LEARNING FUN AND EASY

This 100+ lesson course includes 20+ hours of high-quality video and text explanations of everything from Python, Linear Algebra, Mathematics behind the ML algorithms and case studies. Topic is organized into the following sections:


  • Python Basics, Data Structures - List, Tuple, Set, Dictionary, Strings

  • Pandas and Numpy

  • Linear Algebra - Understanding what is a point and equation of a line.

  • What is a Vector and Vector operations

  • What is a Matrix and Matrix operations

  • In depth mathematics behind Logistic Regression

  • Donors Choose case study

  • In depth mathematics behind Linear Regression.

AND HERE'S WHAT YOU GET INSIDE OF EVERY SECTION:


  • We will start with basics and understand the intuition behind each topic.

  • Video lecture explaining the concept with many real-life examples so that the concept is drilled in.

  • Walkthrough of worked out examples to see different ways of asking question and solving them.

  • Logically connected concepts which slowly builds up.

Enroll today! Can't wait to see you guys on the other side and go through this carefully crafted course which will be fun and easy.


YOU'LL ALSO GET:


  • Lifetime access to the course

  • Friendly support in the Q&A section

  • Udemy Certificate of Completion available for download

  • 30-day money back guarantee

What You Will Learn!

  • Basics of Python. If you already know Python then this can be skipped.
  • Linear Algebra to develop mathematical Intuition behind each algorithm.
  • Mathematics behind Logistic Regression.
  • Logistic Regression Case Study - Donors Choose
  • Mathematics behind Linear Regression
  • Linear Regression Case Study

Who Should Attend!

  • Data Analysts wanting to transition into Data Scientists
  • Dats Scientists wanting to understand the mathematical rigour behind the algorithms.
  • Just about anybody who is interested in Machine Learning
  • Maths enthusiasts