Deep Learning Foundation : Linear Regression and Statistics
Learn linear regression from scratch, Statistics, R-Squared, Python, Gradient descent, Deep Learning, Machine Learning
4.99 (591 reviews)
6155
Students6 hours
ContentNov 2020
Last UpdateWhat you will 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
Description
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.
Screenshots
Content
Introduction
Introduction
Linear Regression prerequisites
Linear Regression prerequisites: statistics
What is hypothesis
Unbiased sample estimator
Histogram and Distributions
P-Value and Testing hypothesis
Normal Distribution Yet another example
Statistical Tests
T- Test
T-test in python
Z- Test
Chi-Square Test
Introduction to Linear Regression
Linear Regression Introduction
Reviews
A very good introduction to linear regression. The implementation of all the concepts from the ground up in code is an excellent way to drive home the concepts. My only complaint/wish would be to have a small section addressing the assumptions of linear regression, parts of which I feel were slightly glossed over.
This course is a great combination of hands on experience and in depth theoretical explanation of the mathematical concepts.
Course was a great match. Jay has the right approach of doling out math and theory, (no matter how difficult) and then correlate it to coding.
This course is exactly what I was looking for. The instructor does an impressive job making students understand they need to work hard in order to learned. The examples are clear, and the explanations of the theory is very interesting.
This is a great course! It covers theory, math, intuition, and coding implementation of linear regression in Python.
Delivery is a bit slow. The concept are covered in detail with examples. Overall a good course for basics as well as beginner programming
3279622
Udemy ID6/29/2020
Course created date7/3/2020
Course Indexed date
