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
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Coding School
Software Engineer, Data Scientist and Entrepreneur
Rating Distribution
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- 2 stars: 1 votes
- 3 stars: 1 votes
- 4 stars: 1 votes
- 5 stars: 13 votes
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