## Deep Learning Foundation Linear Regression and Statistics

### 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.

### Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 community message

Section 2: Introduction to Linear Regression

Lecture 3 Linear Regression Introduction

Lecture 4 R-Squared

Lecture 5 SSE_SST_SSE

Lecture 6 Cost-Function and Optimization

Lecture 7 Cost-Function in 3-d

Lecture 8 Gradient Descent Maths

Lecture 9 Gradient Descent Example

Section 3: coding a linear regression model from scratch

Lecture 10 coding a linear regression model from scratch

Lecture 11 house price prediction with linear regression

Lecture 12 Effect of learning rate in gradient descent

Lecture 13 adaptive learning rates

Lecture 14 multivariate linear regression

Lecture 15 coding multivariate linear regression from scratch

Section 4: Basic Statistics

Lecture 16 Linear Regression prerequisites: statistics

Lecture 17 What is hypothesis

Lecture 18 Unbiased sample estimator

Lecture 19 Histogram and Distributions

Lecture 20 P-Value and Testing hypothesis

Lecture 21 Normal Distribution Yet another example

Section 5: Statistical Tests

Lecture 22 Types of Test for hypothesis

Lecture 23 Problem Statement

Lecture 24 T-Statistics

Lecture 25 T-test in python

Lecture 26 Z- Test

Lecture 27 Chi-Square Test

Section 6: Assumptions of linear regression

Lecture 28 Introduction to Assumptions of Linear regression

Lecture 29 correlation and covariance

Lecture 30 Assumptions of linear regression

Lecture 31 VIF and multicollinearity

Lecture 32 upcoming-lectures

Section 7: Logistic Regression

Lecture 33 Logistic regression binary classification

Lecture 34 Logistic regression multiclass classification

Section 8: Bonus Lectures

Lecture 35 Eigenvalues and Eigenvectors

Lecture 36 make moving charts with python

Lecture 37 Time Series forecasting

Lecture 38 PCA for data science and Machine learning

Lecture 39 Solve a neural network on paper

Lecture 40 Training Neural network with your own images

Lecture 41 Transfer learning

Lecture 42 Entropy a Mathematical view

Lecture 43 Eyes and Face detection with python

Lecture 44 Data Science Interview Questions

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

#### Course Information:

Udemy | English | 6h 31m | 4.49 GB

Created by: Jay Bhatt

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