The Complete Supervised Machine Learning Models in R

The Complete Supervised Machine Learning Models in R, available at $74.99, has an average rating of 4.5, with 111 lectures, based on 16 reviews, and has 165 subscribers.

You will learn about 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 This course is ideal for individuals who are Anyone who want to Learn Complete Supervised Machine Learning Models in R or Anyone who want to Learn the Math behind every Machine Learning Model or Anyone who want to Learn the Intuition of each Model or Anyone who want to Learn to choose the best Machine Learning Model for a specific problem It is particularly useful for Anyone who want to Learn Complete Supervised Machine Learning Models in R or Anyone who want to Learn the Math behind every Machine Learning Model or Anyone who want to Learn the Intuition of each Model or Anyone who want to Learn to choose the best Machine Learning Model for a specific problem.

Enroll now: The Complete Supervised Machine Learning Models in R

Summary

Title: The Complete Supervised Machine Learning Models in R

Price: $74.99

Average Rating: 4.5

Number of Lectures: 111

Number of Published Lectures: 111

Number of Curriculum Items: 111

Number of Published Curriculum Objects: 111

Original Price: $59.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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 ratesand 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 Modelsimplemented in R Programming Language.

I am looking forward to see you in the course..

Best

Course Curriculum

Chapter 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

Chapter 2: Simple Linear Regression Statistics – Intuition Parts

Lecture 1: Simple Linear Regression Statistics 1

Lecture 2: Simple Linear Regression Statistics 2

Lecture 3: Simple Linear Regression Statistics 3

Lecture 4: Data Sets for Simple Linear Regression

Chapter 3: Simple Linear Regression in R – Implementation Parts

Lecture 1: Simple Linear Regression in R Part – 1

Lecture 2: Simple Linear Regression in R Part – 2

Lecture 3: Simple Linear Regression in R Part – 3

Lecture 4: Simple Linear Regression in R Part – 4

Lecture 5: Simple Linear Regression in R Part – 5

Lecture 6: Simple Linear Regression in R Part – 6

Chapter 4: Multiple Linear Regression Statistics – Intuition Parts

Lecture 1: Multiple Linear Regression Statistics 1

Lecture 2: Multiple Linear Regression Statistics 2

Lecture 3: Multiple Linear Regression Statistics 3

Lecture 4: Multiple Linear Regression Statistics 4

Lecture 5: Multiple Linear Regression Statistics 5

Lecture 6: Multiple Linear Regression Statistics 6

Lecture 7: Data Sets for the Multiple Linear Regression Model

Chapter 5: Multiple Linear Regression in R – Implementation Parts

Lecture 1: Multiple Linear Regression in R Part – 1

Lecture 2: Multiple Linear Regression in R Part – 2

Lecture 3: Multiple Linear Regression in R Part – 3

Lecture 4: Multiple Linear Regression in R Part – 4

Lecture 5: Multiple Linear Regression in R Part – 5

Lecture 6: Multiple Linear Regression in R Part – 6

Lecture 7: Multiple Linear Regression in R Part – 7

Chapter 6: Polynomial Regression Statistics – Intuition Part

Lecture 1: Polynomial Regression Statistics

Lecture 2: Data Sets for the Polynomial Regression Model

Chapter 7: Polynomial Regression in R – Implementation Parts

Lecture 1: Polynomial Regression in R Part – 1

Lecture 2: Polynomial Regression in R Part – 2

Lecture 3: Polynomial Regression in R Part – 3

Lecture 4: Polynomial Regression in R Part – 4

Lecture 5: Polynomial Regression in R Part – 5

Chapter 8: Ridge Regression Statistics – Intuition Parts

Lecture 1: Ridge Regression Statistics 1

Lecture 2: Ridge Regression Statistics 2

Lecture 3: Data Sets for Ridge Regression Model

Chapter 9: Ridge Regression in R – Implementation Parts

Lecture 1: Ridge Regression in R Part – 1

Lecture 2: Ridge Regression in R Part – 2

Lecture 3: Ridge Regression in R Part – 3

Lecture 4: Ridge Regression in R Part – 4

Chapter 10: Lasso Regression Statistic – Intuition Part

Lecture 1: Lasso Regression Statistic

Lecture 2: Data Set for the Lasso Regression Model

Chapter 11: Lasso Regression in R – Implementation Parts

Lecture 1: Lasso Regression in R Part – 1

Lecture 2: Lasso Regression in R Part – 2

Lecture 3: Lasso Regression in R Part – 3

Lecture 4: Lasso Regression in R Part – 4

Chapter 12: Elastic Net Regression Statistic – Intuition Part

Lecture 1: Elastic Net Regression Statistic

Lecture 2: Data Set for the Elastic Net Regression Model

Chapter 13: Elastic Net Regression in R – Implementation Parts

Lecture 1: Elastic Net Regression in R Part – 1

Lecture 2: Elastic Net Regression in R Part – 2

Lecture 3: Elastic Net Regression in R Part – 3

Lecture 4: Elastic Net Regression in R Part – 4

Chapter 14: Decision Tree Regression Statistic – Intuition Part

Lecture 1: Decision Tree Regression Statistic

Lecture 2: Data Set#1 for Decision Tree Regression Model

Lecture 3: Data Set#2 for Decision Tree Regression Model

Chapter 15: Decision Tree Regression in R – Implementation Parts

Lecture 1: Decision Tree Regression in R Part – 1

Lecture 2: Decision Tree Regression in R Part – 2

Lecture 3: Decision Tree Regression in R Part – 3

Lecture 4: Decision Tree Regression in R Part – 4

Lecture 5: Decision Tree Regression in R Part – 5

Chapter 16: Random Forest Regression Statistic – Intuition Part

Lecture 1: Random Forest Regression Statistic

Lecture 2: Data Set#1 for Random Forest Regression Model

Lecture 3: Data Set#2 for Random Forest Regression Model

Chapter 17: Random Forest Regression in R – Implementation Part

Lecture 1: Random Forest Regression in R Part – 1

Lecture 2: Random Forest Regression in R Part – 2

Lecture 3: Random Forest Regression in R Part – 3

Lecture 4: Random Forest Regression in R Part – 4

Lecture 5: Random Forest Regression in R Part – 5

Chapter 18: Support Vector Regression Statistic – Intuition Part

Lecture 1: Support Vector Regression Statistic

Lecture 2: Data Set#1 for SVM

Lecture 3: Data Set#2 for SVM

Chapter 19: Support Vector Regression in R – Implementation Part

Lecture 1: Support Vector Regression in R Part – 1

Lecture 2: Support Vector Regression in R Part – 2

Lecture 3: Support Vector Regression in R Part – 3

Lecture 4: Support Vector Regression in R Part – 4

Chapter 20: ++++++++++++++++++ Beginning Classification ++++++++++++++++++

Lecture 1: Confusion Matrix

Lecture 2: Sparse Matrix

Lecture 3: Data Sets for the Classification Models

Instructors

Coding School
Software Engineer, Data Scientist and Entrepreneur

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2 stars: 1 votes
3 stars: 1 votes
4 stars: 1 votes
5 stars: 13 votes

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