编码学校2023中的完全监督机器学习模型

学习每个模型背后的直觉和数学，以及它在R编程语言中的实现，在本课程中，您将学习用R编程语言实现的所有类型的监督机器学习模型。每个模型背后的数学非常重要。没有它，你永远无法成为一名优秀的数据科学家。这就是原因，我已经在每个模型的直觉部分介绍了每个模型背后的数学。R中的实现是以这样一种方式完成的，这样，您不仅可以学习如何用R编程语言实现特定的模型，还可以学习如何构建实时模型并找到模型的准确率，这样您就可以轻松地测试特定问题的不同模型，找到准确率，然后选择准确率最高的模型。数据部分对于训练任何机器学习模型都是非常重要的。如果数据包含无用的实体，它会降低你的机器学习模型的精度水平。我们已经讨论了如何生成高质量的数据集和去除无用的实体，从而得到高质量和可信的机器学习模型。这一切都是在这个课程中完成的。The Complete Supervised Machine Learning Models In R By Coding School 2023

因此，通过学习这门课程，你会感觉掌握了用R编程语言实现的所有类型的监督机器学习模型。我期待着在课程中见到最好的你..

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你会学到什么
在R中学习完整的监督机器学习模型
学习每个机器学习模型背后的数学
了解每个模型的直觉
学会为特定问题选择最佳的机器学习模型

要求
任何编程语言的基础是必需的

任何想学习R中的完全监督机器学习模型的人，任何想学习每个机器学习模型背后的数学的人，任何想学习每个模型的直觉的人，任何想学习为特定问题选择最佳机器学习模型的人

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

What you’ll learn
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

Requirements
Basic of any Programming Language is required

Description
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