Video: .MP4, 1280x720 | Audio: AAC, 44100 Hz, 2ch | Duration: 2h
Genre: eLearning | Language: English + Sub | Size: 812 MB

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This is the bite size course to learn R Programming for Machine Learning and Statistical Learning. In CRISP DM data mining process, machine learning is at the modeling and evaluation stage.

You will need to know some R programming, and you can learn R programming from my "Create Your Calculator: Learn R Programming Basics Fast" course. You will learn R Programming for machine learning and you will be able to train your own prediction models with naive bayes, decision tree, knn, neural network, linear regression, and evaluate your models very soon after learning the course.

I will create Applied statistics using R for data understanding stage and advanced data visualizations for data understanding stage and includes some data processing for data preparation stage in future.

Content

Getting Started

Getting Started 2

Getting Started 3

Data Mining Process

Download Data set

Read Data set

Some Explanations

Simple Linear Regression

Build Linear Regression Models

Predict Linear Regression Models

KMeans Clustering

KMeans Clustering in R

Agglomeration Clustering

Agglomeration Clustering in R

Decision Tree ID3 ALgorithm

Decision Tree in R: Split train and test set

Decision Tree in R: Train Decision Tree

Decision Tree in R: Predict Decision Tree

KNN Classification

Train KNN in R

Predict KNN in R

Naive Bayes Classification

Naive Bayes in R

Neural Network Classification

Neural Network in R

What Algorithm to Use?

Model Evaluation

Model Evaluation using R for Classification

Model Evaluation using R for Regression

What you'll learn
Machine Learning using R
Are there any course requirements or prerequisites?
Fundamentals R programming
Who this course is for:
Beginner Data Scientist or Analyst interested in R programming

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