Linear Regression In Python Statistics And Coding
Tags: Regression Analysis
Learn linear regression from scratch, Statistics, R-Squared, Python, Gradient descent, Deep Learning, Machine Learning
Last updated 2022-01-10 | 4
- 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
What you'll learn
* Requirements
* Jupyter notebook and simple python programmingDescription
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.
Who this course is for:
- Python developers curious about data science
- data science and machine leaning engineers
Course content
8 sections • 44 lectures
Introduction Preview 02:15
community message Preview 00:59
Linear Regression Introduction Preview 04:26
R-Squared Preview 04:34
SSE_SST_SSE Preview 04:18
Cost-Function and Optimization Preview 03:29
Cost-Function in 3-d Preview 07:15
Gradient Descent Maths Preview 04:49
Gradient Descent Example Preview 07:13
coding a linear regression model from scratch Preview 13:39
house price prediction with linear regression Preview 09:07
Effect of learning rate in gradient descent Preview 06:47
adaptive learning rates Preview 08:10
multivariate linear regression Preview 04:49
coding multivariate linear regression from scratch Preview 22:35
Linear Regression prerequisites: statistics Preview 02:33
What is hypothesis Preview 01:15
Unbiased sample estimator Preview 02:19
Histogram and Distributions Preview 05:08
P-Value and Testing hypothesis Preview 02:26
Normal Distribution Yet another example Preview 03:16
Types of Test for hypothesis Preview 05:34
Introduction to most Frequent types of test in statistics