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R is a language for statistical computing and graphics which provides for a wide range of statistical and graphical techniques like linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering. It is highly extensible, and makes available an Open Source route for active participation.

This programming training strength lies in the ease with which well-crafted publication-quality plots can be made, including mathematical symbols and formulae when and where required. R is accessible as Free Software and runs on a wide variety from Windows to MacOS.

R-Programming is designed around a true computer language, and it allows users to add additional functionality by defining new functions. The system, for most of it, has itself been written in the R dialect, which makes it much easier for users to follow the algorithmic choices been made. On the other hand, Advanced or Pro-users can write C-code to alter R objects directly.

R-Programming includes:

  • Handling the functional data and storage facility
  • Large, coherent, integrated collection of intermediate tools for data analysis
  • Graphical facility for data analysis and display for either on-screen or on hardcopy (off-screen)

Machine learning using R-training , is intended for Analyst professionals who will be using Machine Learning algorithms to analyse big data. Data scientist who will identify, analyse, and interpret course will cover the basic algorithm that helps us to build and apply prediction functions with an emphasis on practical applications. This course is trends or patterns in complex data sets, Software professionals and analytics Professionals. Students, at the end of this training, will be technically competent in the basics and the fundamental concepts of Machine Learning such as, understanding components of a machine learning algorithm and applying multiple machine learning tools to build and evaluate predictors on real data.

This program training also enables an effective learning of how to perform different classification algorithm to filtering the Email data, forecasting on Time series Data, and performing clustering with the help of case study. R-training 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.

Things you learn during Machine training using R-training:

  • Understanding and analysing Machine Learning and its techniques in depth
  • Learning how to implement different methods to estimate the model performance using caret package
  • Learning how to apply machine learning tools to build and evaluate predictors on real data
  • Learning how to formulate the data using Machine Learning clustering algorithm such as K-Means, etc
  • Performing Prediction on large dataset using Regression Techniques
  • Using case study to forecast the Time series data