I T   R I D G E   G L O B A L
Education Images

Machine Learning Using R Training

  • Beginner & Advanced level Classes.
  • Hands-On Learning in Machine Learning Using R.
  • Best Practice for interview Preparation Techniques in Machine Learning Using R.
  • Lifetime Access for Student’s Portal, Study Materials, Videos & Top MNC Interview Question.
  • Affordable Fees with Best curriculum Designed by Industrial Machine Learning Using R Expert.
  • Delivered by 9+ years of Machine Learning Using R Certified Expert | 12402+ Students Trained & 350+ Recruiting Clients.
  • Next Machine Learning Using R Batch to Begin this week –  Enroll Your Name Now!
Call Course Advisor

This is How I.T. Ridge Students Prepare for Better Jobs

PLACED IMAGE I.T. Ridge

Learn From Experts, Practice On Projects & Get Placed in IT Company

  • We train students for interviews and Offer Placements in corporate companies.
  • Ideal for graduates with 0 – 3 years of experience & degrees in B. Tech, B.E and B.Sc. IT Or Any Computer Relevent.
  • You will not only gain knowledge of Machine Learning Using R and Advance tools, but also gain exposure to Industry best practices, Aptitude & SoftSkills.
  • TExperienced Trainers and Lab Facility.
  • Machine Learning Using R Certified Data Scientist Guidance Support with Exam Dumps.
  • For Corporate, we act as one stop recruiting partner.We provide right skilled candidates who are productive right from day one.
  • Resume & Interviews Preparation Support.
  • Concepts: Business Analytics, Data, Information, basics of R programming like datatypes and functions, Data Import techniques in R, Loops and Date, Exploratory Data Analysis, Statistics and Machine Learning – Regression and Classification, Project work.
  • START YOUR CAREER WITH MACHINE LEARNING USING R COURSE THAT GETS YOU A JOB OF UPTO 5 LACS IN JUST 60 DAYS!
Show More

Syllabus of Machine Learning using R Course

Module 1:Topics
  • Business Analytics, Data, Information
  • Understanding Business Analytics and R
  • Compare R with other software in analytics
  • Install R
  • Perform basic operations in R using command line
  • Learn the use of IDE R Studio
  • Use the ‘R help’ feature in R
Module 2: Introduction to R programming Module 3:Objectives:
  • This module starts from the basics of R programming like datatypes and functions.
  • In this module, we present a scenario and let you think about the options to resolve it, such as which datatype should one to store the variable or which R function that can help you in this scenario.
  • Will cover the basics of data types and conversion between them
Topics
  • Variables in R
  • Scalars
  • Vectors
  • Matrices
  • List
  • Data frames
  • Using c, Cbind, Rbind, attach and detach functions in R
Objectives:
  • In this module, we start with a sample of a messy dataset and perform Data Cleaning on it, resulting in a dataset, which is ready for any analysis.
  • Before starting any analysis data cleaning is the first process to do
Topics
  • Data sorting
  • Find and remove duplicates record
  • Cleaning data
  • Recoding data
  • Merging data
  • Slicing of Data
  • Merging Data
  • Apply functions
Module 4: Data Import techniques in R Objectives:
  • This module tells you about the versatility and robustness of R which can take-up data in a variety of formats, be it from a csv file
  • This module teaches you various data importing techniques in R.
Topics
  • Reading and writing flat files – text,csv,Excel
  • Reading Techniques in R
  • How to connect mysql with R
  • Saving and loading R format
Module 5: Loops and Date
  • This module tells you concepts of loop technique. From this we can automate anything in R and give the enhancement to your code
  • Dates will comes in any format, so that converting the dates, difference and slicing date information is most important in real time
Topics
  • For loop
  • While loop
  • If loop
  • Break and continue
  • How to convert to date format
  • How to slice dates information (Year, Quarters, Months, Days, Hours etc..,)
  • How to get difference between two days (Year, Quarters, Months, Days, Hours)
Module 6: Exploratory Data Analysis Objectives:
  • In this module, you will learn that exploratory data analysis is an important step in the analysis.
  • EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis. You will also learn about the various tasks involved in a typical EDA process.
Topics
  • Box plot
  • Histogram
  • Doughnut graph
  • Pie graph
  • Line chart
  • Scatterplot
Module 7: Statistics and Machine Learning – Regression and Classification Objectives:
  • This module touches the base of Descriptive and Inferential Statistics and machine learning concepts
  • All algorithm explained by – what is the mathematics behind for that, how it is getting different from other algorithm, which scenario want to apply, how to interpret with R and what insights getting from final result
Statistics Topics
  • Probability
  • Hypothesis Testing – Null Hypothesis and Alternate Hypothesis
  • Standard deviation and Variance
  • Outliers – Detection and Replacement method
  • Correlation
  • T test- Unpaired and Paired
  • Chi square
  • Anova
Machine Learning Topics
  • Linear Regression
  • Multiple Regression
  • Logisitic Regression
  • Naïve Bayes Classifier
  • Variable selection on model
  • K means Clustering
  • Decision Tree
  • SVM
  • Time series forecasting
Module 8: Project work:
  • 3 Real-time projects
Syllabus of Apache Spark Training
Module 1: Introduction to Spark
  • Introduction to Spark
  • Spark overcomes the drawbacks of working on MapReduce
  • Understanding in-memory MapReduce
  • Interactive operations on MapReduce
  • Spark stack, fine vs. coarse-grained update, Spark Hadoop YARN, HDFS Revision, and YARN Revision
  • The overview of Spark and how it is better than Hadoop
  • Deploying Spark without Hadoop
  • Spark history server and Cloudera distribution
Module 2: Spark Basics
  • Spark installation guide
  • Spark configuration
  • Memory management
  • Executor memory vs. driver memory
  • Working with Spark Shell
  • The concept of resilient distributed datasets (RDD)
  • Learning to do functional programming in Spark
  • The architecture of Spark
Module 3: Working with RDDs in Spark
  • Spark RDD
  • Creating RDDs
  • RDD partitioning
  • Operations and transformation in RDD
  • Deep dive into Spark RDDs
  • The RDD general operations
  • Read-only partitioned collection of records
  • Using the concept of RDD for faster and efficient data processing
  • RDD action for the collect, count, collects map, save-as-text-files, and pair RDD functions
Module 4: Aggregating Data with Pair RDDs
  • Understanding the concept of key-value pair in RDDs
  • Learning how Spark makes MapReduce operations faster
  • Various operations of RDD
  • MapReduce interactive operations
  • Fine and coarse-grained update
  • Spark stack
Module 5: Writing and Deploying Spark Applications
  • Comparing the Spark applications with Spark Shell
  • Creating a Spark application using Scala or Java
  • Deploying a Spark application
  • Scala built application
  • Creation of the mutable list, set and set operations, list, tuple, and concatenating list
  • Creating an application using SBT
  • Deploying an application using Maven
  • The web user interface of Spark application
  • A real-world example of Spark
  • Configuring of Spark
Module 6: Parallel Processing
  • Learning about Spark parallel processing
  • Deploying on a cluster
  • Introduction to Spark partitions
  • File-based partitioning of RDDs
  • Understanding of HDFS and data locality
  • Mastering the technique of parallel operations
  • Comparing repartition and coalesce
  • RDD actions
Module 7: Spark RDD Persistence
  • The execution flow in Spark
  • Understanding the RDD persistence overview
  • Spark execution flow, and Spark terminology
  • Distribution shared memory vs RDD
  • RDD limitations
  • Spark shell arguments
  • Distributed persistence
  • RDD lineage
  • Key-value pair for sorting implicit conversions like CountByKey, ReduceByKey, SortByKey, and AggregateByKey
Module 8: Spark MLlib
  • Introduction to Machine Learning
  • Types of Machine Learning
  • Introduction to MLlib
  • Various ML algorithms supported by MLlib
  • Linear regression, logistic regression, decision tree, random forest, and K-means clustering techniques
Module 9: Integrating Apache Flume and Apache Kafka
  • Why Kafka and what is Kafka?
  • Kafka architecture
  • Kafka workflow
  • Configuring Kafka cluster
  • Operations
  • Kafka monitoring tools
  • Integrating Apache Flume and Apache Kafka
Module 10: Spark Streaming
  • Introduction to Spark Streaming
  • Features of Spark Streaming
  • Spark Streaming workflow
  • Initializing StreamingContext, discretized Streams (DStreams), input DStreams and Receivers
  • Transformations on DStreams, output operations on DStreams, windowed operators and why it is useful
  • Important windowed operators and stateful operators
Module 11: Improving Spark Performance
  • Introduction to various variables in Spark like shared variables and broadcast variables
  • Learning about accumulators
  • The common performance issues
  • Troubleshooting the performance problems
Module 12: Spark SQL and Data Frames
  • Learning about Spark SQL
  • The context of SQL in Spark for providing structured data processing
  • JSON support in Spark SQL
  • Working with XML data
  • Parquet files
  • Creating Hive context
  • Writing data frame to Hive
  • Reading JDBC files
  • Understanding the data frames in Spark
  • Creating Data Frames
  • Manual inferring of schema
  • Working with CSV files
  • Reading JDBC tables
  • Data frame to JDBC
  • User-defined functions in Spark SQL
  • Shared variables and accumulators
  • Learning to query and transform data in data frames
  • Data frame provides the benefit of both Spark RDD and Spark SQL
  • Deploying Hive on Spark as the execution engine
Module 13: Scheduling/Partitioning
  • Learning about the scheduling and partitioning in Spark
  • Hash partition
  • Range partition
  • Scheduling within and around applications
  • Static partitioning, dynamic sharing, and fair scheduling
  • Map partition with index, the Zip, and GroupByKey
  • Spark master high availability, standby masters with ZooKeeper
  • Single-node recovery with the local file system and high order functions
Show More

Upcoming Batches

07-Oct-2024
Mon-Fri

Weekdays Regular

08:00 AM & 10:00 AM Batches

(Class 1Hr - 1:30Hrs) / Per Session

14-Oct-2024
Mon-Fri

Weekdays Regular

08:00 AM & 10:00 AM Batches

(Class 1Hr - 1:30Hrs) / Per Session

21-Oct-2024
Mon-Fri

Weekdays Regular

08:00 AM & 10:00 AM Batches

(Class 1Hr - 1:30Hrs) / Per Session

28-Oct-2024
Mon-Fri

Weekdays Regular

08:00 AM & 10:00 AM Batches

(Class 1Hr - 1:30Hrs) / Per Session

Key Features

  • 40 Hours Course Duration
  • Industry Expert Faculties
  • Completed 500+ Batches
  • 100% Job Oriented Training

.

  • Free Demo Class Available
  • Have a portfolio of various data analysis projects.
  • Certification Guidance

Overview of Machine Learning Using R Training

Machine learning is a branch in computer science that studies the design of algorithms that can learn. Typical machine learning tasks are concept learning, function learning or “predictive modeling”, clustering and finding predictive patterns. Start Learning with us I.T. Ridge Machine Learning Using R Classroom and Online Training Course.

Our Top Hiring Partner for Placements

I.T. Ridge offers placement opportunities as add-on to every student / professional who completed our classroom or online training. Some of our students are working in these companies listed below.

  • We are associated with top organizations like HCL, Wipro, Dell, Accenture, Google, CTS, TCS, IBM etc. It make us capable to place our students in top MNCs across the globe
  • We have separate student’s portals for placement, here you will get all the interview schedules and we notify you through Emails.
  • After completion of 70% Machine Learning Using R training course content, we will arrange the interview calls to students & prepare them to F2F interaction
  • Machine Learning Using R Trainers assist students in developing their resume matching the current industry needs
  • We have a dedicated Placement support team wing that assist students in securing placement according to their requirements
  • We will schedule Mock Exams and Mock Interviews to find out the GAP in Candidate Knowledge
Show More

Get Certified By Oracle & Industry Recognized I.T. Ridge Certificate

I.T. Ridge Certification is Accredited by all major Global Companies around the world. We provide after completion of the theoretical and practical sessions to fresher's as well as corporate trainees. Our certification at I.T. Ridge is accredited worldwide. It increases the value of your resume and you can attain leading job posts with the help of this certification in leading MNC's of the world. The certification is only provided after successful completion of our training and practical based projects.
Show More

FAQ's

Call now: +91 8328526484 and know the exciting offers available for you!

I.T. Ridge offers placement opportunities as add-on to every student / professional who completed our classroom or online training. Some of our students are working in these companies listed below.

We are associated with top organizations like HCL, Wipro, Dell, Accenture, Google, CTS, TCS, IBM etc. It make us capable to place our students in top MNCs across the globe.

We have separate student’s portals for placement, here you will get all the interview schedules and we notify you through Emails.

After completion of 70% Machine Learning Using R training course content, we will arrange the interview calls to students & prepare them to F2F interaction

Machine Learning Using R Trainers assist students in developing their resume matching the current industry needs

We have a dedicated Placement support team wing that assist students in securing placement according to their requirements

We will schedule Mock Exams and Mock Interviews to find out the GAP in Candidate Knowledge

I.T. Ridge is the unique Authorized Oracle Partner, Authorized Microsoft Partner, Authorized Pearson Vue Exam Center, Authorized PSI Exam Center, Authorized Partner Of AWS and National Institute of Education (NIE) Singapore

No worries. I.T. Ridge assure that no one misses single lectures topics. We will reschedule the classes as per your convenience within the stipulated course duration with all such possibilities. If required you can even attend that topic with any other batches.

All the instructors at I.T. Ridge are practitioners from the Industry with minimum 9-12 yrs of relevant IT experience. They are subject matter experts and are trained by I.T. Ridge for providing an awesome learning experience.

Video Images
Have Queries? Ask our Experts

+91 8328526484 Available 24x7 for your queries

For more details Enquiry Now
Name *
Email *
Phone *
Message *