Beginner to Advanced Guide on Machine Learning with R Tool

Beginner to Advanced Guide on Machine Learning with R Tool, available at $19.99, has an average rating of 2.85, with 38 lectures, based on 24 reviews, and has 379 subscribers.

You will learn about Master Machine Learning Regression modelling knn algorithm naive bayes algorithm BPN(Back Propagation Network) SVM(Support Vector Machine) Decision Tree Forecasting This course is ideal for individuals who are Freshers or Professionals or Anyone interested in machine learning It is particularly useful for Freshers or Professionals or Anyone interested in machine learning.

Enroll now: Beginner to Advanced Guide on Machine Learning with R Tool

Summary

Title: Beginner to Advanced Guide on Machine Learning with R Tool

Price: $19.99

Average Rating: 2.85

Number of Lectures: 38

Number of Published Lectures: 38

Number of Curriculum Items: 38

Number of Published Curriculum Objects: 38

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Master Machine Learning

Regression modelling

knn algorithm

naive bayes algorithm

BPN(Back Propagation Network)

SVM(Support Vector Machine)

Decision Tree

Forecasting

Who Should Attend

Freshers

Professionals

Anyone interested in machine learning

Target Audiences

Freshers

Professionals

Anyone interested in machine learning

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.

Course Curriculum

Chapter 1: Module-1 Introduction to Course

Lecture 1: 1.1 Introduction to the Course

Lecture 2: 1.2 Pre-Requisite

Lecture 3: 1.3 What you will Learn

Lecture 4: 1.4 Techniques of Machine Learning

Chapter 2: Module-2 Introduction to validation and its Methods

Lecture 1: 2.1 Introduction to Cross Validation

Lecture 2: 2.2 Cross Validation Method

Lecture 3: 2.3 Caret package

Chapter 3: Module-3 Classification

Lecture 1: 3.1 Introduction to Classification

Lecture 2: 3.2 KNN- K Nearest Neighbors

Lecture 3: 3.3 Implementation of KNN Algorithm

Lecture 4: 3.4 Naive-Bayes Classifier

Lecture 5: 3.5 Implementation of Naive-Bayes Classifier

Lecture 6: 3.6 Linear Discriminant Analysis

Lecture 7: 3.7 Implementation of Linear Discriminant Analysis

Chapter 4: Module-4 Black Box Method-Neural network and SVM

Lecture 1: 4.1 Introduction to Artificial Neural Network

Lecture 2: 4.2 Conceptualizing of Neural Network

Lecture 3: 4.3 Implement Neural Network in R

Lecture 4: 4.4 Back Propagation

Lecture 5: 4.5 Implementation of Back Propagation Network

Lecture 6: 4.6 Introduction to Support Vector Machine

Lecture 7: 4.7 Implementation of SVM in R

Chapter 5: Module-5 Tree Based Models

Lecture 1: 5.1 Decision Tree

Lecture 2: 5.2 Implementation of Decision Tree

Lecture 3: 5.3 Bagging

Lecture 4: 5.4 Boosting

Lecture 5: 5.5 Introduction to Random Forest

Lecture 6: 5.6 Implementation of Random Forest

Chapter 6: Module-6 Clustering

Lecture 1: 6.1 Introduction to Clustering

Lecture 2: 6.2 K-Means Clustering

Lecture 3: 6.3 Implementation of K-Means Clustering

Lecture 4: 6.4 Hierarchical Clustering

Chapter 7: Module-7 Regression

Lecture 1: 7.1 Predicting with Linear Regression

Lecture 2: 7.2 Implementation of Linear Regression

Lecture 3: 7.3 Multiple Covariates Regression

Lecture 4: 7.4 Logistic Regression

Lecture 5: 7.5 Implementation of Logistic Regression

Lecture 6: 7.6 Forecasting

Lecture 7: 7.7 Implementation of Forecasting

Instructors

Elementary Learners
Make learning online

Rating Distribution

1 stars: 2 votes

2 stars: 5 votes

3 stars: 4 votes

4 stars: 4 votes

5 stars: 9 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

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