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Description

R programming is a programming language and a software analysis instrument for graphical representation. Machine learning with R programming will take you to the deep concepts of the R programming language. This online course for R language training is based on providing technical knowledge about this programming language with the covering theory part. 

If your foundation of a programming language is strong, then this course is going to suit you as we have dedicated tutorials on it. But if you don't have any technical knowledge then also you can understand as our tutorials are short and easy to understand. You can ask for any query in between the course we will solve that instantly, so the learning process will be more interactive and you will enjoy while studying this complex language.

In this online course, you will learn about the R language where we will cover the supervised and unsupervised learning approaches. We will also dive into the statistical modelling related to machine learning for comparing available data.

What are the key highlights of the course?

  • You will get an understanding of machine learning with the R language.
  • Learn the tools to build up and evaluate real data 
  • Implementing different methods to estimate the model performance.
  • You will also learn the clustering algorithms, including k-mean,
  • making predication over the large data set.
  • Also making predictions over time series with the case study.

If you are an analyst professional or a machine learning student who wants to get strong grips on the R language. Also, data scientists can take this course to identify and analyse the data pattern of complex data sets.

Course Content

Total: 29 lectures
  • The Origins of machine learning
  • Uses and abuses of machine learning
  • How do machine learn?
  • Using R for machine learning
  • R data structures
  • Vectors
  • Factors
  • Managing Data with R
  • Exploring and Understanding Data
  • The kNN algorithm
  • Why is the kNN algorithm lazy?
  • Diagnosing breast cancer with the kNN algorithm
  • Understanding Naïve Bayes
  • Understanding decision trees
  • Understanding classification rules
  • Understanding regression
  • Understanding regression trees and model trees
  • Understanding neural networks
  • Modeling the strength of concrete with ANNs
  • Understanding Support Vector Machines
  • Performing OCR with SVMs
  • Understanding Association Rules
  • Understanding Clustering
  • Measuring Performance for Classification
  • Estimating future performance
  • Tuning stock models for better performance
  • Improving Model performance with meta-learning
  • Working with specialized data
  • Improving the performance of R

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