Machine Learning With R


Description
Course Structure
Pre-requisites
Certification
FAQs
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Course Objectives

This course has been designed to help you learn complex theory and algorithms in a very simple and easy to understand way. The course is packed with practical exercises which are based on live examples. So not only will you learn the theory, but you will also get hands-on practice building your own solutions. You will build highly sought-after machine learning skills in both supervised and unsupervised learning. This includes predictive modeling, forecasting, classification techniques, deep learning, recommendation engines, text mining and much more.

As part of the program you will work on real-life industry-based projects across multiple domains like social media, retail, banking and life-sciences using our Learning Management System. You will work on multiple assignments, case studies and practice exercises to hone your skills.

Also, We will provide you code templates that are used in industry for real life solution building which you can download and use on your own projects.

The program includes bonus sessions on text minning, social media analysis & sentiment analysis

Course duration: 155 hours
Mode: Classroom / Online Instructor Led through Virtual classroom

Key Features

  • 55 hours of instructor led live training on weekends
  • Hand-on practice on 13 real life case studies
  • Access to LEAP - our analytics learning platform
  • Personal attention from faculty
  • Performance evaluation
  • Placement assistance
  • 100 hours of self learning
  • Practice exercises and assignments to enhance skills
  • Faculties from IIT/IIMs with rich industry experience
  • Full access to video lectures for self paced learning
  • 100% moneyback guarantee
  • Internship opportunity to work on dashboarding & insight generation projects
R fundamentals
R overview. Installation. Packages & walkthrough. Data structures (Vector, array,factors, data frames, lists). Vector calculation. Arithmetic & logical operators. Subsetting. Missing, indefinite & infinite values.
Hours = 3
Case Study = Labs are conducted with open source data to bring out the concept and insights
Assignment = Practice Excercises + Doubt Clearing + Answers
Tool = R
Control flow basics
For loops. While loops. Nested loops. Disadvantage of using loops. Alternates to loops.
Hours = 3
Case Study = Labs are conducted with open source data to bring out the concept and insights
Assignment = Practice Excercises + Doubt Clearing + Answers
Tool = R
Functions
Understand the structure of function. Build your own function. Usage of parameters and default values. Usage of return.
Hours = 3
Case Study = Labs are conducted with open source data to bring out the concept and insights
Assignment = Practice Excercises + Doubt Clearing + Answers
Tool = R
Packages
How to search & choose a new package. Package installation & updates. Help and learn. Access package functions. Hack a function. Build your own package.
Hours = 2
Case Study = Labs are conducted with open source data to bring out the concept and insights
Assignment = Practice Excercises + Doubt Clearing + Answers
Tool = R
Environment objects
Save, load & delete objects.
Hours = 1
Case Study = Labs are conducted with open source data to bring out the concept and insights
Assignment = Practice Excercises + Doubt Clearing + Answers
Tool = R
Data import & export
Import & export from Excel. Import & export from MySQL. Import & export from text file. Export to image & PDF. Present output in HTML webpage.
Hours = 3
Case Study = Case study on importing data from excel, formatting it in R using automated code and presenting insights from it in a web page
Assignment = Practice Excercises + Doubt Clearing + Answers
Tool = R
Data manipulation basics
Sort & rank. Data Aggregation. Merging.
Hours = 3
Case Study = Case study on data manipulation
Assignment = Practice Excercises + Doubt Clearing + Answers
Tool = R
Data manipulation advanced
Apply, Lapply, Tapply, By, Replicate functions. Dplyr. Tidyr.
Hours = 3
Case Study = Labs are conducted with open source data to bring out the concept and insights
Assignment = Practice Excercises + Doubt Clearing + Answers
Tool = R
Data Visualization fundamentals
Plot function. Changing parameters. Drawing basic charts. Adding chart elements.
Hours = 3
Case Study = Case study on plotting of stock market data
Assignment = Practice Excercises + Doubt Clearing + Answers
Tool = R
Data Visualization advanced
Qplot, Ggplot, Maps..
Hours = 3
Case Study = Case study on US airport data based map visualization
Assignment = Practice Excercises + Doubt Clearing + Answers
Tool = R
Introduction to machine learning
How do machines learn? Choosing a machine learning algorithm. Using R for machine learning.
Hours = 1
Case Study = None
Assignment = None
Tool = R
Classification using Nearest Neighbors
Understanding classification using nearest neighbors. The kNN algorithm - Calculating distance, Choosing an appropriate k, Preparing data for use with kNN.
Hours = 3
Case Study = Case study on diagnosing breast cancer using kNN algorithm.
Assignment = Case study on classifying the IRIS dataset using KNN
Tool = R
Classification using Naive Bayes
Understanding Naïve Bayes - basic concepts & algorithm.
Hours = 3
Case Study = Case study on filtering mobile phone spam with the naive Bayes algorithm.
Assignment = Case study on using Naïve Bayes Classifier to predict cancer
Tool = R
Classification using decision trees and random forest
Understanding decision trees. C5.0 decision tree algorithm. Understanding classification rules. Understanding Random forest. Modeling using Random Forest.
Hours = 3
Case Study = Case study on identifying risky bank loand using C5.0 decision trees.
Assignment = Case study on identifying poisonous mushrooms with rule learners.
Tool = R
Deep learning - Neural Networks
Understanding neural networks. Activation functions. Network topology. Training neural networks with backpropagation.
Hours = 3
Case Study = Case study on modeling the strength of concrete with neural network.
Assignment = Case study on fitting a neural network model to predict the median value of owner-occupied homes (medv) using all the other continuous variables available.
Tool = R
Support Vector Machines
Understanding SVM. Classification with hyperplanes. Finding the maximum margin. Using kernels for non linear spaces.
Hours = 3
Case Study = Case study on optical character recognition using SVM.
Assignment = Case study on classifying telecom churn using SVM
Tool = R
Market basket analysis
Understanding association rules. Apriori algorithm.
Hours = 3
Case Study = Case study on frequently purchased items for a large retailer.
Assignment = Case study to use transactions from an open source dataset to find association rules using Apriori
Tool = R
Text mining
Main concepts and components of text mining, text mining tasks and approaches. An understanding of the art of the possible in Text Analytics - the applicability, components and benefits.
Hours = 3
Case Study = Case study on analysis of book reviews on Amazon
Assignment = Practice case study on text mining on book reviews
Tool = R
Social media analysis
Learn different text mining techniques to discover various textual patterns from the social sites. Learn how to – (1) Access twitter data. (2) Build frequent term network (3) Topic modelling (4) Analysis of followers & retweets
Hours = 3
Case Study = Case study to access a twitter account and create several visualizations to draw interesting insights
Assignment = Practice case study on text mining using another twitter account
Tool = R
Sentiment analysis
Understand sentiment analysis and its key concepts. Sentiment polarity. Opinion summarization. Feature extraction. Classification based algorithms. Application of SVM.
Hours = 3
Case Study = Case study to perform sentiment analysis on tweets
Assignment = Practice case study on sentiment analysis using another twitter account
Tool = R

Is this course for you?

You should take this course if you are a:

  • Student (UG/PG) and want to learn machine learning techniques
  • IT professional looking to upgrade their skills & make them more relevant in the job market
  • Analytics professional who wants to work in machine learning or artificial intelligence
  • Data Science professionals who already have experience in R

What are the pre-requisites of the course?

There are no pre-requisites for this course. The course starts from scratch which makes it easy to understand for everyone and provides in-depth knowledge.

Knowledge of statistical modeling techniques would be a plus but it is not mandatory.

At the end of the course you will be entitled to Simplify Analytics "Machine Learning with R" Certificate, provided you fulfil the following terms:

  • Completion and submission of at least 6 projects/case studies
  • Attend at least 85% of the sessions
  • Clear the final online test by minimum 60%
What is the mode of this training course?
Classroom & Online instructor led. Classroom sessions are held at multiple training centres located in Delhi-NCR region. Live online sessions are conducted through our "Virtual Classroom". This will allow you to attend the course remotely from anywhere through your desktop/laptop/tablet/smartphone. Video recording of each session is provided at the end of live session.
Do I need to have computer programming background to take the course?
No, you don’t need to have a programming background to learn analytics. The program has been designed in a way that it starts from scratch and makes it easier to learn for everyone.
What if I miss a class?
You can attend the missed session, in any other live batch. You can also use the video recording of the session you missed.
What kind of placement assistance is offered by Simplify Analytics?
We are committed to getting you placed. All our courses include - Real life projects + Internship + Certificate + Interview QnA + Resume building & sharing + Job search guidance + Interview call assistance.
What if I still have doubts after attending a live session?
You can retake a class as many times as you wish across multiple batches. Also, we conduct separate doubt clearing sessions to help our students. We make sure that you understand all the concepts and are able to build solutions.
What if I want to cancel my enrollment post registration? Will I get a refund?
Yes, we have a 100% money back policy which allows you to cancel your enrollment after the first two classes (before third class). If you are not satisfied from the program, all your money will be refunded back to you.
What are system requirements?
You will require a laptop or workstation with a minimum 2 GB RAM & i3 processor (or equivalent) to practice & submit assignments. No constraint on OS.
Thank you for choosing "Machine Learning With R"

Course reviews
  1. Simplify Analytics - Course reviews
    5.00 out of 5

    Vaibhav Nellore

    Very knowledgeable!! Asks stimulating questions..Jaydeep is too good at explaining ideas; well designed course with great content.

  2. Simplify Analytics - Course reviews
    5.00 out of 5

    Rohit Kumar

    Great teaching techniques help you dwell into the field of analytics. would really recommend to anyone looking for a career in analytics