Machine Learning with R Programming

Learn in Detail How to execute Machine Learning Algorithms in R Programming
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  • Lectures 42
  • Length 2 hours
  • Skill Level All Levels
  • Languages English
  • Includes Lifetime access
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    Available on iOS and Android
    Certificate of Completion
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About This Course

Published 1/2016 English

Course Description

This course will cover the basic algorithm that helps us to build and apply prediction functions with an emphasis on practical applications. Students, at the end of this training, will be technically competent in the basics and the fundamental concepts of Machine Learning such as:

  • Understand components of a machine learning algorithm
  • Apply multiple machine learning tools to build and evaluate predictors on real data
  • Learn how to perform different classification algorithm to filtering the Email data
  • Forecasting on Time series Data
  • Perform Clustering with the help of case study
  • This course contains lectures as videos along with the hands-on implementation of the concepts, additional assignments are also provided in the last section for your self-practice, working files are provided along with the first lecture.

What are the requirements?

  • You should have solid foundation in R
  • You should have good understanding of general statistics

What am I going to get from this course?

  • Understanding Machine Learning and its techniques in brief
  • Learn Implementation of different methods to estimate the model performance using caret package
  • Learn how to apply machine learning tools to build and evaluate predictors on real data
  • How to cluster the data using Machine Learning clustering algorithm such as K-Means, etc
  • Perform Prediction on large dataset using Regression Techniques
  • Forecast the Time series data with the help of casestudy

What is the target audience?

  • This course is intended for Analyst professionals who will be using Machine Learning algorithms to analyze big data, Data scientist who will identify, analyze, and interpret trends or patterns in complex data sets, Software professionals and analytics Professionals

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.

Curriculum

Section 1: Introduction
Introduction
Preview
01:47
Pre-Requisite
Preview
00:34
What you will learn
Preview
01:27
Techniques of Machine Learning
Preview
02:57
Section 2: Introduction to validation and its Methods
Introduction to Cross Validation
Preview
02:01
Cross Validation Method
Preview
04:10
Caret Package
05:50
Section 3: Classification
Introduction to Classification
01:37
KNN-K Nearest Neighbors
02:52
Implementation of KNN algorithm
05:51
Naives-Bayes Classifier
02:42
Implementation of Naive Bayes Classifier
10:22
Linear Discriminant Analysis
01:05
Implementation of Linear Discriminant Analysis
02:35
Section 4: Black Box Methods – Neural Networks and Support Vector Machines
Introduction to Artificial Neural Network
01:38
Conceptualizing of Neural Network
02:03
Implementation of Neural Network in R
05:01
Back Propagation
02:59
Introduction to Support Vector Machine
02:14
Implementation of SVM in R
04:13
Section 5: Tree Based Models
Decision Tree
02:16
Implementation of decision tree
04:28
Bagging
03:09
Random Forest
05:00
Boosting
04:28
Section 6: Clustering
Introduction to Clustering
01:20
K-Means Clustering
06:52
Implementation to K-Means Clustering
02:47
Hierarchical Clustering
01:32
Implementation of Hierarchical Clustering
02:21
Section 7: Regression
Predicting with Linear Regression
02:28
Implementation of Linear Regression
04:25
Multiple Covariates Regression
03:28
Logistic Regression
06:00
Forecasting
02:20
Implementation of Forecasting
02:14
Section 8: Assignments and Quiz
Introduction to validation and its Methods
1 page
Classification
1 page
Black Box Methods – Neural Networks and Support Vector Machines
1 page
Tree Based Models
2 pages
Clustering
1 page
Regression
2 pages

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