Beginner to Advanced Guide on Machine Learning with R Tool
3.3 (22 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
368 students enrolled

Beginner to Advanced Guide on Machine Learning with R Tool

Learn Machine Learning with the help of R programming
3.3 (22 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
368 students enrolled
Last updated 2/2019
English
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Current price: $11.99 Original price: $199.99 Discount: 94% off
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This course includes
  • 2 hours on-demand video
  • 17 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Master Machine Learning
  • Regression modelling

  • knn algorithm

  • naive bayes algorithm
  • BPN(Back Propagation Network)
  • SVM(Support Vector Machine)
  • Decision Tree
  • Forecasting
Course content
Expand all 38 lectures 02:09:56
+ Module-1 Introduction to Course
4 lectures 08:17

This video talks about a brief course description and tells you what to expect from this course. 

Preview 02:12

This video tells an outline about what you will learn in subsequent sessions of the course. 

1.3 What you will Learn
01:55

In this lecture you will learn about the various techniques of Machine Learning. 

1.4 Techniques of Machine Learning
03:30
+ Module-2 Introduction to validation and its Methods
3 lectures 12:46
2.1 Introduction to Cross Validation
01:56
2.2 Cross Validation Method
03:28
+ Module-3 Classification
7 lectures 32:42
3.1 Introduction to Classification
01:46
3.2 KNN- K Nearest Neighbors
03:23
3.3 Implementation of KNN Algorithm
06:19
3.4 Naive-Bayes Classifier
02:50
3.5 Implementation of Naive-Bayes Classifier
14:21
3.6 Linear Discriminant Analysis
01:16
3.7 Implementation of Linear Discriminant Analysis
02:47
+ Module-4 Black Box Method-Neural network and SVM
7 lectures 18:43
4.1 Introduction to Artificial Neural Network
01:33
4.2 Conceptualizing of Neural Network
02:41
4.3 Implement Neural Network in R
05:07
4.4 Back Propagation
01:23
4.5 Implementation of Back Propagation Network
01:41
4.6 Introduction to Support Vector Machine
02:41
4.7 Implementation of SVM in R
03:37
+ Module-5 Tree Based Models
6 lectures 20:28
5.1 Decision Tree
02:37
5.2 Implementation of Decision Tree
03:44
5.3 Bagging
03:19
5.4 Boosting
05:31
5.5 Introduction to Random Forest
02:07
5.6 Implementation of Random Forest
03:10
+ Module-6 Clustering
4 lectures 14:54
6.1 Introduction to Clustering
01:35
6.2 K-Means Clustering
06:44
6.3 Implementation of K-Means Clustering
03:19
6.4 Hierarchical Clustering
03:16
+ Module-7 Regression
7 lectures 22:06
7.1 Predicting with Linear Regression
02:22
7.2 Implementation of Linear Regression
05:08
7.3 Multiple Covariates Regression
04:20
7.4 Logistic Regression
02:32
7.5 Implementation of Logistic Regression
02:52
7.6 Forecasting
02:28
7.7 Implementation of Forecasting
02:24
Requirements
  • R programming
  • R studio should be installed already
  • Basic knowledge of programming
  • Basic knowledge of mathematics
Description

Inspired by the field of Machine Learning? Then this course is for you!

This course is intended for both freshers and experienced hoping to make the bounce to Data Science.

R is a statistical programming language which provides tools to analyze data and for creating high-level graphics.

The topic of Machine Learning is getting exceptionally hot these days in light of the fact that these learning algorithms can be utilized as a part of a few fields from software engineering to venture managing an account. Students, at the end of this course, will be technically sound in the basics and the advanced concepts of Machine Learning.


Who this course is for:
  • Freshers
  • Professionals
  • Anyone interested in machine learning