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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!

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

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02-Dec-2024

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