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Machine Learning with R

Machine Learning with R online training course covers a detailed overview of various algorithms and techniques, such as regression, classification, time series modeling, supervised and unsupervised learning, text mining etc.

  • 40000
  • 45000
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  • Course Includes
  • Live Class Practical Oriented Training
  • 75+ Hrs Instructor LED Training
  • 45 + Hrs Practical Exercise
  • 25 + Hrs Project Work & Assignment
  • Timely Doubt Resolution
  • Dedicated Student Success Mentor
  • Certification & Job Assistance
  • Free Access to Workshop & Webinar
  • No Cost EMI Option
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What you will learn

  • Develop an understanding of categorical variables and continuous variables, that helps in using the boosting and bagging...
  • Understand kernel functions such as: spline kernels, linear, radial basis function and polynomial and Text Mining with R...
  • Explore R language fundamentals, including basic syntax, variables, and types
  • Why Support Vector Machines is called the most high-performing algorithm
  • How neural networks effective in image segmentation. How to use the calculus in simpler form

Requirements

  • The candidates should have knowledge of the basics of programming, SQL and math and statistic concepts.

Description

|| About Machine Learning with R Training Course 

The Machine Learning with R Online Training Course is for the candidates, who wants to learn algorithm coding and formula and other aspects of the data and analytics.  This Machine Learning Courses are the concoction of Data Science with R, Introduction to Machine Learning, Random Forest, General Boosting & Bagging, Support Vector Machines, Neural Networks  and Text Mining with R.

 

The training insights the candidates on the syntax, variables, and types, create functions and use control flow, work with data in R. Moreover, they would be able to gain insight on regression, clustering, classification, including measuring the variable importance through permutation and gaining hands-on experience on solving the algorithm with the complexity of a classifier to gain accuracy.

 

Course Content

Live Lecture

·       Exploratory Data Analysis and Visualization

·       R for Data Science

·       Data Mining

·       Data Analysis for Evidence Based Decision Making

·       Industry Applications of Advanced Analytics Models

·       Big Data Analytics with Spark

·       Project Management in Analytics

·       Information to Insight

·       Career Management
Live Lecture

·       An Introduction

·       The Regression Algorithms

·       The Classifiers: Bayesian and kNN

·       Tree Based Algorithms

·       SVM and Improving Performance
Live Lecture

·       Single Decision Tree

·       Rise of Ensemble Method

·       Practical Exercises in R on Business Case Studies              
Live Lecture

·       Decision Tree Ensembles: Bagging and Boosting

·       The Case Study: Analysis of Credit Data

·       The Case Study: The Titanic Accident

·       The Case Study: Comparing Algorithms