Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
13 Hour(s) 53 Minute(s)
Language
English
Taught by
Coding School
Certificate
- Available
- *The delivery and distribution of the certificate are subject to the policies and arrangements of the course provider.
Rating
2 views
Course Overview
Learn the Intuition and Math behind Every Model with it's implementation in R Programming Language
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
Course Content
- 32 section(s)
- 111 lecture(s)
- Section 1 Introduction and Setting up R Studio
- Section 2 Simple Linear Regression Statistics - Intuition Parts
- Section 3 Simple Linear Regression in R - Implementation Parts
- Section 4 Multiple Linear Regression Statistics - Intuition Parts
- Section 5 Multiple Linear Regression in R - Implementation Parts
- Section 6 Polynomial Regression Statistics - Intuition Part
- Section 7 Polynomial Regression in R - Implementation Parts
- Section 8 Ridge Regression Statistics - Intuition Parts
- Section 9 Ridge Regression in R - Implementation Parts
- Section 10 Lasso Regression Statistic - Intuition Part
- Section 11 Lasso Regression in R - Implementation Parts
- Section 12 Elastic Net Regression Statistic - Intuition Part
- Section 13 Elastic Net Regression in R - Implementation Parts
- Section 14 Decision Tree Regression Statistic - Intuition Part
- Section 15 Decision Tree Regression in R - Implementation Parts
- Section 16 Random Forest Regression Statistic - Intuition Part
- Section 17 Random Forest Regression in R - Implementation Part
- Section 18 Support Vector Regression Statistic - Intuition Part
- Section 19 Support Vector Regression in R - Implementation Part
- Section 20 ++++++++++++++++++ Beginning Classification ++++++++++++++++++
- Section 21 Logistic Regression Statistic - Intuition Part
- Section 22 Logistic Regression in R - Implementation Part
- Section 23 Support Vector Classification Statistic - Intuition Part
- Section 24 Support Vector Classification in R - Implementation Part
- Section 25 K Nearest Neighbour Statistic - Intuition Part
- Section 26 K Nearest Neighbour in R - Implementation Part
- Section 27 Naiive Bayes Classification Statistic - Intuition Part
- Section 28 Naiive Bayes Classification in R - Implementation Part
- Section 29 Decision Tree Classification Statistics - Intuition Parts
- Section 30 Decision Tree Classification in R - Implementation Parts
- Section 31 Random Forest Classification Statistic - Intuition Part
- Section 32 Random Forest Classification in R - Implementation Part
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
Skills covered in this course
Reviews
-
RRafael Soares
Decent, the course wording is correct, the intuition behind the models and nothing more. The R implementation my only complain is the script is not available for download.
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RRodney Jones
Yes, the course is fine. There seems some variability in the audio quality that is exacerbated by the tendency of the lecturer to speak rather rapidly.
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DDicky Cheung
Going to be a great course.
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CCryptoking Smith
5 Star Review for the course
