Linear Regression In Python Statistics And Coding

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

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

* Requirements

* Jupyter notebook and simple python programming

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.

Who this course is for:

  • Python developers curious about data science
  • data science and machine leaning engineers

Course content

8 sections • 44 lectures

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

Problem Statement Preview 02:14

T-Statistics Preview 10:36

T-test in python Preview 05:09

Z- Test Preview 06:54

Chi-Square Test Preview 07:31

Introduction to Assumptions of Linear regression Preview 02:33

correlation and covariance Preview 09:15

Assumptions of linear regression Preview 22:34

VIF and multicollinearity Preview 10:56

upcoming-lectures Preview 00:15

Logistic regression binary classification Preview 15:55

Logistic regression multiclass classification Preview 20:00

Eigenvalues and Eigenvectors Preview 07:45

make moving charts with python Preview 27:44

Time Series forecasting Preview 25:01

PCA for data science and Machine learning Preview 09:01

Solve a neural network on paper Preview 25:01

Training Neural network with your own images Preview 18:28

Transfer learning Preview 04:06

Entropy a Mathematical view Preview 14:27

Eyes and Face detection with python Preview 11:11

Data Science Interview Questions Preview 07:36