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**BaDshaH** · 06-13-2023, 11:11 AM

[Image: MEJgpe-RXUAy1-ECxfe-DPEbu-EJPZ0mg-Tm-M.jpg]

Published 6/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 7.12 GB | Duration: 13h 54m

Learn the Intuition and Math behind Every Model with it's implementation in R Programming Language

[b]What you'll learn[/b]
Learn Complete Supervised Machine Learning Models in R
Learn the Math behind every Machine Learning Model
Learn the Intuition of each Model
Learn to choose the best Machine Learning Model for a specific problem

[b]Requirements[/b]
Basic of any Programming Language is required

[b]Description[/b]
In this course, you are going to learn all types of Supervised Machine Learning Models implemented in R Programming Language. The Math behind every model is very important. Without it, you can never become a Good Data Scientist. That is the reason, I have covered the Math behind every model in the intuition part of each Model.Implementation in R is done in such a way so that not only you learn how to implement a specific Model in R Programming Language but you learn how to build real times models and find the accuracy rate of Models so that you can easily test different models on a specific problem, find the accuracy rates and then choose the one which give you the highest accuracy rate.The Data Part is very important in Training any Machine Learning Model. If the Data Contains Useless Entities, it will take down the Precision Level of your Machine Learning Model. We have covered many techniques of how to make high quality Datasets and remove the useless Entities so that we can get high quality and trustable Machine Learning Model. All this is done in this Course.Hence, by taking this course, you will feel mastered in all types of Supervised Machine Learning Models implemented in R Programming Language.I am looking forward to see you in the course..Best

Overview
Section 1: Introduction and Setting up R Studio
Lecture 1 Introduction to the Course
Lecture 2 What is Machine Learning
Lecture 3 Setting up the IDE
Lecture 4 Data Sets for the Course
Section 2: Simple Linear Regression Statistics - Intuition Parts
Lecture 5 Simple Linear Regression Statistics 1
Lecture 6 Simple Linear Regression Statistics 2
Lecture 7 Simple Linear Regression Statistics 3
Lecture 8 Data Sets for Simple Linear Regression
Section 3: Simple Linear Regression in R - Implementation Parts
Lecture 9 Simple Linear Regression in R Part - 1
Lecture 10 Simple Linear Regression in R Part - 2
Lecture 11 Simple Linear Regression in R Part - 3
Lecture 12 Simple Linear Regression in R Part - 4
Lecture 13 Simple Linear Regression in R Part - 5
Lecture 14 Simple Linear Regression in R Part - 6
Section 4: Multiple Linear Regression Statistics - Intuition Parts
Lecture 15 Multiple Linear Regression Statistics 1
Lecture 16 Multiple Linear Regression Statistics 2
Lecture 17 Multiple Linear Regression Statistics 3
Lecture 18 Multiple Linear Regression Statistics 4
Lecture 19 Multiple Linear Regression Statistics 5
Lecture 20 Multiple Linear Regression Statistics 6
Lecture 21 Data Sets for the Multiple Linear Regression Model
Section 5: Multiple Linear Regression in R - Implementation Parts
Lecture 22 Multiple Linear Regression in R Part - 1
Lecture 23 Multiple Linear Regression in R Part - 2
Lecture 24 Multiple Linear Regression in R Part - 3
Lecture 25 Multiple Linear Regression in R Part - 4
Lecture 26 Multiple Linear Regression in R Part - 5
Lecture 27 Multiple Linear Regression in R Part - 6
Lecture 28 Multiple Linear Regression in R Part - 7
Section 6: Polynomial Regression Statistics - Intuition Part
Lecture 29 Polynomial Regression Statistics
Lecture 30 Data Sets for the Polynomial Regression Model
Section 7: Polynomial Regression in R - Implementation Parts
Lecture 31 Polynomial Regression in R Part - 1
Lecture 32 Polynomial Regression in R Part - 2
Lecture 33 Polynomial Regression in R Part - 3
Lecture 34 Polynomial Regression in R Part - 4
Lecture 35 Polynomial Regression in R Part - 5
Section 8: Ridge Regression Statistics - Intuition Parts
Lecture 36 Ridge Regression Statistics 1
Lecture 37 Ridge Regression Statistics 2
Lecture 38 Data Sets for Ridge Regression Model
Section 9: Ridge Regression in R - Implementation Parts
Lecture 39 Ridge Regression in R Part - 1
Lecture 40 Ridge Regression in R Part - 2
Lecture 41 Ridge Regression in R Part - 3
Lecture 42 Ridge Regression in R Part - 4
Section 10: Lasso Regression Statistic - Intuition Part
Lecture 43 Lasso Regression Statistic
Lecture 44 Data Set for the Lasso Regression Model
Section 11: Lasso Regression in R - Implementation Parts
Lecture 45 Lasso Regression in R Part - 1
Lecture 46 Lasso Regression in R Part - 2
Lecture 47 Lasso Regression in R Part - 3
Lecture 48 Lasso Regression in R Part - 4
Section 12: Elastic Net Regression Statistic - Intuition Part
Lecture 49 Elastic Net Regression Statistic
Lecture 50 Data Set for the Elastic Net Regression Model
Section 13: Elastic Net Regression in R - Implementation Parts
Lecture 51 Elastic Net Regression in R Part - 1
Lecture 52 Elastic Net Regression in R Part - 2
Lecture 53 Elastic Net Regression in R Part - 3
Lecture 54 Elastic Net Regression in R Part - 4
Section 14: Decision Tree Regression Statistic - Intuition Part
Lecture 55 Decision Tree Regression Statistic
Lecture 56 Data Set#1 for Decision Tree Regression Model
Lecture 57 Data Set#2 for Decision Tree Regression Model
Section 15: Decision Tree Regression in R - Implementation Parts
Lecture 58 Decision Tree Regression in R Part - 1
Lecture 59 Decision Tree Regression in R Part - 2
Lecture 60 Decision Tree Regression in R Part - 3
Lecture 61 Decision Tree Regression in R Part - 4
Lecture 62 Decision Tree Regression in R Part - 5
Section 16: Random Forest Regression Statistic - Intuition Part
Lecture 63 Random Forest Regression Statistic
Lecture 64 Data Set#1 for Random Forest Regression Model
Lecture 65 Data Set#2 for Random Forest Regression Model
Section 17: Random Forest Regression in R - Implementation Part
Lecture 66 Random Forest Regression in R Part - 1
Lecture 67 Random Forest Regression in R Part - 2
Lecture 68 Random Forest Regression in R Part - 3
Lecture 69 Random Forest Regression in R Part - 4
Lecture 70 Random Forest Regression in R Part - 5
Section 18: Support Vector Regression Statistic - Intuition Part
Lecture 71 Support Vector Regression Statistic
Lecture 72 Data Set#1 for SVM
Lecture 73 Data Set#2 for SVM
Section 19: Support Vector Regression in R - Implementation Part
Lecture 74 Support Vector Regression in R Part - 1
Lecture 75 Support Vector Regression in R Part - 2
Lecture 76 Support Vector Regression in R Part - 3
Lecture 77 Support Vector Regression in R Part - 4
Section 20: ++++++++++++++++++ Beginning Classification ++++++++++++++++++
Lecture 78 Confusion Matrix
Lecture 79 Sparse Matrix
Lecture 80 Data Sets for the Classification Models
Section 21: Logistic Regression Statistic - Intuition Part
Lecture 81 Logistic Regression Statistic
Section 22: Logistic Regression in R - Implementation Part
Lecture 82 Logistic Regression in R Part - 1
Lecture 83 Logistic Regression in R Part - 2
Lecture 84 Logistic Regression in R Part - 3
Lecture 85 Logistic Regression in R Part - 4
Lecture 86 Logistic Regression in R Part - 5
Lecture 87 Logistic Regression in R Part - 6
Section 23: Support Vector Classification Statistic - Intuition Part
Lecture 88 Support Vector Classification Statistic
Section 24: Support Vector Classification in R - Implementation Part
Lecture 89 Support Vector Classification in R Part - 1
Lecture 90 Support Vector Classification in R Part - 2
Lecture 91 Support Vector Classification in R Part - 3
Section 25: K Nearest Neighbour Statistic - Intuition Part
Lecture 92 K Nearest Neighbour Statistic
Section 26: K Nearest Neighbour in R - Implementation Part
Lecture 93 K Nearest Neighbour in R Part - 1
Lecture 94 K Nearest Neighbour in R Part - 2
Section 27: Naiive Bayes Classification Statistic - Intuition Part
Lecture 95 Naiive Bayes Classification Statistics - 1
Lecture 96 Naiive Bayes Classification Statistics - 2
Section 28: Naiive Bayes Classification in R - Implementation Part
Lecture 97 Naiive Bayes Classification in R Part - 1
Lecture 98 Naiive Bayes Classification in R Part - 2
Lecture 99 Naiive Bayes Classification in R Part - 3
Lecture 100 Naiive Bayes Classification in R Part - 4
Section 29: Decision Tree Classification Statistics - Intuition Parts
Lecture 101 Decision Tree Classification Statistics 1
Lecture 102 Decision Tree Classification Statistics 2
Section 30: Decision Tree Classification in R - Implementation Parts
Lecture 103 Decision Tree Classification in R Part - 1
Lecture 104 Decision Tree Classification in R Part - 2
Lecture 105 Decision Tree Classification in R Part - 3
Lecture 106 Decision Tree Classification in R Part - 4
Lecture 107 Decision Tree Classification in R Part - 5
Section 31: Random Forest Classification Statistic - Intuition Part
Lecture 108 Random Forest Classification Statistic
Section 32: Random Forest Classification in R - Implementation Part
Lecture 109 Random Forest Classification in R Part - 1
Lecture 110 Random Forest Classification in R Part - 2
Lecture 111 Random Forest Classification in R Part - 3
Anyone who want to Learn Complete Supervised Machine Learning Models in R,Anyone who want to Learn the Math behind every Machine Learning Model,Anyone who want to Learn the Intuition of each Model,Anyone who want to Learn to choose the best Machine Learning Model for a specific problem

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