Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
6 Hour(s) 44 Minute(s)
Language
English
Taught by
Jay Bhatt
Rating
2 views
Course Overview
Learn linear regression from scratch, Statistics, R-Squared, Python, Gradient descent, Deep Learning, Machine Learning
Hi Everyone welcome to new course which is created to sharpen your linear regression and statistical basics. linear regression is starting point for a data science this course focus is on making your foundation strong for deep learning and machine learning algorithms. In this course I have explained hypothesis testing, Unbiased estimators, Statistical test , Gradient descent. End of the course you will be able to code your own regression algorithm from scratch.
Course Content
- 8 section(s)
- 45 lecture(s)
- Section 1 Introduction
- Section 2 Introduction to Linear Regression
- Section 3 coding a linear regression model from scratch
- Section 4 Basic Statistics
- Section 5 Statistical Tests
- Section 6 Assumptions of linear regression
- Section 7 Logistic Regression
- Section 8 Bonus Lectures
What You’ll Learn
- Mathematics behind R-Squared, Linear Regression,VIF and more!
- Deep understating of Gradient descent and Optimization
- Program your own version of a linear regression model in Python
- Derive and solve a linear regression model, and implement it appropriately to data science problems
- Statistical background of Linear regression and Assumptions
- Assumptions of linear regression hypothesis testing
- Writing codes for T-Test, Z-Test and Chi-Squared Test in python
Skills covered in this course
Reviews
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SSandeep Chiluveru
Introduced and covered all the concepts of linear regression conveying its general purpose and the problems that it solves. This is a huge learning for newcomers into the field. Gives a nice foundation for higher learnings in future on the topic.
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RRajesh Prasad
Great course on Mathematics and explanation is presented with example
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MMuny C
Very well explained, I am following everything until now :)
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SSteve Ligon
This is very hard to listen to and understand key points, because of the accent being so strong. And the captions are totally missing making sense of what is being said.
