Logistic Regression

  • Learn how to solve classification problems in data science. Logistic regression is one of the
    foundational classification algorithms in machine learning. Learn the Ins and Outs of
    logistic regression theory, the math, in-depth concepts, do's and don'ts and code implementation
    With crystal clear explanations as seen in all of my courses.

Created by Selva Prabhakaran

  • 35 Video Lessons

  • English

  • English captions

What you will learn

01

What is logistic regression
and where can it be used?

02

Build the classification
model from scratch

03

Perform the pre model
built analysis

04

Check if model is overfitted and learn how to overcome
the problem of overfitting

05

How to use logistic regression
for multi class classification

06

Evaluate the model using
various evaluation
metrices

07

Implement logistic regression
in a project and solve it end to end

Course Curriculum

Requirements

  • Courses Page1 Basics of Python
  • Courses Page1 Foundational knowledge of Data Science
  • Courses Page1 High school maths

Who should attend this course?

  • Data Science Aspirants

  • Data Science Professionals

  • Software/Data engineers interested in quantitative analysis

  • Professionals working with large datasets

  • Data analysts, economists, researchers

Instructor

Selva Prabhakaran Principal Data Scientist

My name is Selva, and I am super excited to mentor you on this project!

I head the Data Science team for a global Fortune 500 company and over the last 10 years of my data science experience I’ve deployed 20+ global products. I’m also the Founder & Chief Author of Machine Learning Plus, which has over 4M annual readers.

I specialize in covering the in-depth intuition and maths of any concept or algorithm. And based on my existing student requests, I’ve put up the series of courses and projects with detailed explanations – just like an on the job experience. Hope you love it!

  • 4.5+Instructor rating

  • 200+ reviews

  • 10K+students

  • 15+ Courses

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