Data Analytics for Machine Learning with R
IIT (ISM) 6 day (48 hours) Full Time, Lab Oriented PROFESSIONAL DEVELOPMENT PROGRAM

This IIT Certified Intensive Lab Oriented Course is focused on building industry ready Data Scientist who can work on machine learning, data mining, and statistical modelling for predictive and prescriptive enterprise analytics. This program will enable you to develop deep understanding of and experience with machine learning and data analysis. Familiarity with common tools for data management and analysis including machine learning can be applied on real world problems for building predictive models using machine learning on your own.
R is the most popular data analytics tool owing to it being open-source, its flexibility, packages and community. “R” wins on Statistical Capability, Graphical capability, Cost, rich set of packages and is the most preferred tool for Data Scientists.

Duration: 48 Hours Session: 8 hours/day - 6 days a week Batch Size: 50 Students

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On completion of the program, students will have developed a world-class skillset in their selected technology domain that provides “Employability Enhancing” skills and capabilities thereby substantially increasing their earning potential and compensation benchmarks. All enrolled participant’s will be provided access to other learning aids, reference materials, assessments and hands on workshops as appropriate. During the course students will also be allocated Project work that is designed to provide adequate practical and hands on experience in implementing the concepts learned during the course.

Syllabus Structure

What you will learn from this course


Module 1 : BASICS OF ARTIFICIAL INTELLIGENCE, MACHINE LEARNING & DATA ANALYTICS WITH R
  • Introduction to Artificial Intelligence (Evolution of Technology)
  • Branches of Artificial Intelligence and what is Machine Learning.
  • Supervised, Unsupervised & Reinforcement Learning.
  • How machine learning can be applied in technology, science, trading etc.
  • Comparison B/W R, Python & SAS
  • Why Learn R?
  • Introduction to R.
  • R Overview, R Interface, R Work Space, Help, Variables, Programming
  • Install R.
  • Running a few simple programs
Module 2 : 2 BASIC PROGRAMMING IN R
  • Some Common Terms & Basics in R
  • Data Types
  • Importing Data
  • Keyboard Input, Database Input, Export Data
  • Variable Labels, Value Labels, Missing Data, date Values
  • R Iteration & Conditional Constructs
  • R Packages: installation and Usages
  • Data Manipulation
  • Hands On Session
  • Some Advance Programs using Data from R Data repository
Module 3 : Convolutional Neural Network
  • Architecture of CNN
  • Types of layers in CNN
  • Building an image classifier using CNN
  • Deep Learning with CNN

Module 4 : DATA VISUALIZATION AND BASIC STATISTICS
  • Introduction to Data Visualization
  • Basic Graphics: line, bar, box, histogram plots
  • Trellis
  • Scatter plots
  • Basic Statistics: mean median, mode, percentile, quantile
  • Frequency Distribution, Histogram Analysis
  • Data: Distribution, Types of Data Distribution and Hypothesis Testing
Module 5 : BASIC STATISTICS
  • Introduction to Predictive Models
  • Linear Regression
  • Logistic Regression
  • Decision Tree
  • Random Forest
  • Implementation of Predictive Models using R
  • The Art of Feature Engineering, Pattern recognition & Principal component analysis
Module 6 : 6 INTRODUCTION TO LATEST TOOLS & TECHNOLOGIES
    ? ? ?
  • Classification & Clustering
  • Supervised Learning K- Nearest Neighbors Classification
  • Unsupervised Learning K – means Clustering Algorithm
  • Reinforcement Learning
  • Implementation of Classification & Clustering Using R
  • Introduction to Neural Networks
  • Introduction Deep Learning
  • Implementation of Neural Network using R, ? Multi layer Perceptron (MLP) ? Support Vector Machine (SOM)

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