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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