Machine Learning Training in Chennai

Best Machine Learning Training in Chennai

Are you a total beginner or a Machine Learning professional who is looking for progress in the career? Then Aimore Technologies’ Machine Learning course in Chennai provides comprehensive training in this powerful concept.You can gain better insights on ML through our in-depth course.

Overview of Machine Learning

Generally, we confuse machine learning with AI and deep learning. It’s essential to clarify the differences between these terms. AI comprises using computers to mimic human intelligence. Deep learning includes neural networks and is the ultimate level of advancement. Machine learning lies between AI and deep learning. Machine learning is a system triggered by information, and it beyond just being a technique for evaluating data. Machine learning can learn from data and enhance continuously. Algorithms are the key here. To know more about machine learning, enroll in the best machine learning training institute in Chennai.

Advantages of machine learning

Expanding volumes and variations of data has made it complicated for coders to manually code and set right every program. Here comes the role of Machine Learning to simplify the human effort.

It finds out and corrects mistakes without human intervention. It also becomes flexible so that errors are never repeated. This leads to faster coding and compilation without any bugs. Besides, when coders are not present, machines can update algorithms every minute.

Comprehensive utilization of AI and Machine Learning has proved its importance in cybersecurity, virtual assistants. The key lies in serving humans better.

Role of machine learning engineers

Machine learning comprises scaling data science algorithms to large data sets. Machine learning engineers will generally work with data scientists. Machine learning engineers ensure that the models that data scientists run are always able to be performed. They apply programming frameworks and big data tools to ascertain the efficiency of the critical processes. They scale machine learning applications to the vast amounts of data that organizations are collecting.

Prerequisites of machine learning course in Chennai

  • Computer skills
  • Basic mathematics, statistics, and data science knowledge.

Who should attend the best machine learning training in Chennai?

  • Candidates wishing to become a data scientist
  • Big data analysts
  • Analytics professionals
  • Business analyst
  • Developer
  • Graduates who are wanting to start a career in Data Science and Machine Learning
  • Companies planning to migrate to big data tools
  • Managers with knowledge of fundamental programming

The future and career scope of Machine Learning

The future of machine learning appears bright. There is a rise in need for professionals who are trained in machine learning. Besides, machine learning jobs are still in their emerging stage. Most of the companies may expect you to take up the initiative and be innovative. Since the demand for machine learning jobs in increasing, you can get into it without any problem if you have the appropriate skills. So it’s the right time that you enroll in the best machine learning training institute in Chennai and become industry-ready.

Machine learning jobs are great opportunities for those candidates who are enthusiastic about innovating and want to be working in new technologies. Machine leaning positions can prove to be intellectually satisfying and provides an excellent career path.

Finally, the world’s challenges are quite complex, and they will need complex methods to solve them. Machine learning engineers are developing these systems. Enroll in the best machine learning training in Chennai because there’s no time like now to ace the skills.

The machine learning algorithms at a glance

Supervised machine learning algorithms can put into use what has been learned in the past to new data applying labeled examples to forecast future events.

Unsupervised machine learning algorithms are made use of when the specific information for training is neither classified nor labeled. Semi-supervised machine learning algorithms come in between supervised and unsupervised learning because they use both labeled and unlabeled data for training.

Reinforcement machine learning algorithms interact with its environment by generating actions and finds out errors or rewards. To know more about these terminologies in machine learning, you can join in the best machine learning training center in Chennai. Aimore Technologies believes in providing individual attention to every student, and hence, we restrict the batch sizes to not more than four students per batch. We also focus on hands-on practical sessions.

Machine Learning with R training in Chennai

The machine learning course with R in Chennai assists you to comprehend the core concepts of machine learning, followed by various machine learning algorithms and executing those machine learning algorithms with R.

Machine Learning Training Syllabus

The course syllabus is prepared diligently so that it meets the industry standards. The latest advancement in machine learning is kept in mind while preparing the syllabus.

Machine Learning with Python

Introduction to Data warehousing

  • Types of Scripts
  • Difference between Script & Programming Languages
  • Features of Scripting
  • Limitation of Scripting
  • Types of programming Language Paradigms

Introduction to Python

  • Who Uses Python?
  • Characteristics of Python
  • History of Python
  • What is PSF?
  • Install Python with Diff IDEs
  • Features of Python
  • Limitations of Python
  • Python Applications

Different Modes in Python

  • Python File Extensions
  • Python Sub Packages
  • Uses of Python in Data Science
  • Working with Python in Unix/Linux/Windows/Mac/Android

Python New IDEs

  • PyCharm IDE
  • How to Work on PyCharm
  • PyCharm Components
  • Debugging process in PyCharm
  • PYTHON Install Anaconda
  • What is Anaconda?
  • Coding Environments
  • Spyder Components
  • General Spyder Features
  • Spyder Shortcut Keys
  • Jupyter Notebook
  • What is Conda?
  • Conda List?
  • Jupyter and Kernels
  • What is PIP?

Python Sets

  • How to create a set?
  • Iteration Over Sets
  • Python Set Methods
  • Python Set Operations
  • Union of sets
  • Built-in Functions with Set
  • Python Frozenset

Python Dictionary

  • How to create a dictionary?
  • Python Dictionary Methods

Python OS Module

  • Shell Script Commands
  • Various OS operations in Python
  • Python File System Shell Methods

Python Exception Handling

  • Python Errors
  • Common Run Time Errors in PYTHON
  • Exception Handling
  • Ignore Errors
  • Assertions
  • Using Assertions Effectively

More Advanced PYTHON

  • Python Iterators
  • Python Generators
  • Python Closures
  • Python Decorators
  • Python @property

Python XML Parser

  • What is XML?
  • Difference between XML and HTML
  • Difference between XML and JSON and Gson
  • How to Parse XML
  • How to Create XML Node
  • Python vs JAVA
  • XML and HTML


  • What is Multi-Threading
  • Threading Module
  • Defining a Thread
  • Thread Synchronization

Web Scrapping

  • The components of a web page
  • Beautiful Soup
  • Urllib2
  • HTML, CSS, JS, jQuery
  • Dataframes
  • PIP
  • Installing External Modules Using PIP

Sequence or Collections in Python

  • Strings
  • Unicode Strings
  • Lists
  • Tuples
  • buffers
  • xrange

Python Lists

  • Lists are mutable
  • Getting to Lists
  • List indices
  • Traversing a list

Python TUPLE

  • Advantages of Tuple over List
  • Packing and Unpacking
  • Comparing tuples
  • Creating nested tuple
  • Using tuples as keys in dictionaries
  • Deleting Tuples
  • Slicing of Tuple
  • Tuple Membership Test

Advanced Python

Python Modules

  • The import Statement
  • The from…import Statement
  • Creating User defined Modules
  • Python Module Search Path

Packages in Python

  • What is a Package?
  • Introduction to Packages?
  • py file
  • Importing module from a package
  • Creating a Package
  • Creating Sub Package
  • Importing from Sub-Packages
  • Popular Python Packages

File Handling

  • What is a data, Information File?
  • File Objects
  • File Different Modes
  • file Object Attributes
  • Directories in Python
  • Working with CSV files

Python Class and Objects

  • Object Oriented Programming System
  • Define Classes
  • Creating Objects
  • Access Modifiers
  • Python Namespace
  • Self-variable in python
  • Garbage Collection
  • Python Multiple Inheritance
  • Overloading and Over Riding
  • Polymorphism
  • Abstraction
  • Encapsulation

Python Regular Expressions

  • What is Regular Expression?
  • Regular Expression Syntax
  • Understanding Regular Expressions
  • Regular Expression Patterns
  • Literal characters
  • Finding Pattern in Text (
  • Using re.findall for text
  • Python Flags
  • Methods of Regular Expressions

Unit Testing with PyUnit

  • What is Testing?
  • Types of Testings and Methods?
  • What is Unit Testing?
  • What is PyUnit?
  • Test scenarios, Test Cases, Test suites

Introduction to Python Web Frameworks

  • Django – Design
  • Advantages of Django
  • MVC and MVT
  • Installing Django
  • Designing Web Pages
  • HTML5, CSS3, AngularJS

GUI Programming-Tkinter

  • Introduction
  • Components and Events
  • Adding Controls
  • Entry Widget, Text Widget, Radio Button, Check Button
  • List Boxes, Menus, Combo Box

Machine Learning with R Training


  • What are Data Analysis, Data Analytics and Data Science?
  • Business Decisions
  • Case study of Walmart

Various analytics tools

  • Descriptive
  • Predictive
  • Web Analytics
  • Google Analytics

Various Analytics tools

  • R and features
  • Evolution of R?
  • Big data Hadoop and R

Working with R & RStudio

  • R & R Studio Installation

Data Types

  • Scalar
  • Vectors
  • Matrix
  • List
  • Data frames
  • Factors
  • Handling date in R
  • Conversion of data types
  • Operators in R

Importing Data

  • CSV files
  • Database data (Oracle 11g)
  • XML files
  • JSON files
  • Reading & Writing PDF files
  • Reading & Writing JPEG files
  • Saving Data in R

Manipulating Data

  • Cbind, Rbind
  • Sorting
  • Aggregating
  • dplyr

Conditional Statements

  • If …else
  • For loop
  • While loop
  • Repeat loop


  • Apply()
  • sApply()
  • rApply()
  • tApply

Statistical Concepts

  • Descriptive Statistics
  • Inferential Statistics
  • Central Tendency (Mean,Mode,Median)
  • Hypothesis Testing
  • Probability
  • tTest
  • zTest
  • Chi Square test
  • Correlation
  • Covariance
  • Anova

Predictive Modelling

  • Linear Regression
  • Normal distribution
  • Density

Data Visualization in R using GGPlot

  • Box Plot
  • Histograms
  • Scatter Plotter
  • Line chart
  • Bar Chart
  • Heat maps

Data Visualization using Plotly

  • 3D-view
  • Geo Maps

Misc. functions

  • Null Handling
  • Merge
  • Grep
  • Scan

Advance Topics in R

  • Text Mining
  • Exploratory Data Analysis
  • Machine Learning with R (concept)

Machine Learning with SAS Training

Started Using SAS Software

  • The SAS Language
  • SAS Data Sets
  • The Two Parts of a SAS Program
  • The DATA Step’s Built-in Loop
  • Choosing a Mode for Submitting SAS Programs
  • Windows and Commands in the SAS Windowing Environment
  • Submitting a Program in the SAS Windowing Environment
  • Reading the SAS Log
  • Viewing Your Results in the Output Window
  • Creating HTML Output
  • SAS Data Libraries
  • Viewing Data Sets with SAS Explorer
  • Using SAS System Options

Getting Your Data into SAS

  • Methods for Getting Your Data into SAS
  • Entering Data with the Viewtable Window
  • Reading Files with the Import Wizard
  • Telling SAS Where to Find Your Raw Data
  • Reading Raw Data Separated by Spaces
  • Reading Raw Data Arranged in Columns
  • Reading Raw Data Not in Standard Format
  • Selected Informats
  • Mixing Input Styles
  • Reading Messy Raw Data
  • Reading Multiple Lines of Raw Data per Observation
  • Reading Multiple Observations per Line of Raw Data
  • Reading Part of a Raw Data File
  • Controlling Input with Options in the INFILE Statement
  • Reading Delimited Files with the DATA Step
  • Reading Delimited Files with the IMPORT Procedure
  • Reading PC Files with the IMPORT Procedure
  • Reading PC Files with DDE
  • Temporary versus Permanent SAS Data Sets
  • Using Permanent SAS Data Sets with LIBNAME Statements
  • Using Permanent SAS Data Sets by Direct Referencing
  • Listing the Contents of a SAS Data Set

Working with Your Data

  • Creating and Redefining Variables
  • Using SAS Functions
  • Selected SAS Functions
  • Using IF-THEN Statements
  • Grouping Observations with IF-THEN/ELSE Statements
  • Subsetting Your Data
  • Working with SAS Dates
  • Selected Date Informats, Functions, and Formats
  • Using the RETAIN and Sum Statements
  • Simplifying Programs with Arrays
  • Using Shortcuts for Lists of Variable Names

Sorting, Printing, and Summarizing

  • Using SAS Procedures
  • Subsetting in Procedures with the WHERE Statement
  • Sorting Your Data with PROC SORT
  • Printing Your Data with PROC PRINT
  • Changing the Appearance of Printed Values with Formats
  • Selected Standard Formats
  • Creating Your Own Formats Using PROC FORMAT
  • Writing Simple Custom Reports
  • Summarizing Your Data Using PROC MEANS
  • Writing Summary Statistics to a SAS Data Set
  • Counting Your Data with PROC FREQ
  • Producing Tabular Reports with PROC TABULATE
  • Adding Statistics to PROC TABULATE Output
  • Enhancing the Appearance of PROC TABULATE Output
  • Changing Headers in PROC TABULATE Output
  • Specifying Multiple Formats for Data Cells in PROC TABULATE Output
  • Producing Simple Output with PROC REPORT
  • Using DEFINE Statements in PROC REPORT
  • Creating Summary Reports with PROC REPORT
  • Adding Summary Breaks to PROC REPORT Output
  • Adding Statistics to PROC REPORT Output

Enhancing Your Output with ODS

  • Concepts of the Output Delivery System
  • Tracing and Selecting Procedure Output
  • Creating SAS Data Sets from Procedure Output
  • Using ODS Statements to Create HTML Output
  • Using ODS Statements to Create RTF Output
  • Using ODS Statements to Create PRINTER Output
  • Customizing Titles and Footnotes
  • Customizing PROC PRINT Output with the STYLE= Option
  • Customizing PROC REPORT Output with the STYLE= Option
  • Customizing PROC TABULATE Output with the STYLE= Option
  • Adding Traffic-Lighting to Your Output
  • Selected Style Attributes

Modifying and Combining SAS Data Sets

  • Modifying a Data Set Using the SET Statement
  • Stacking Data Sets Using the SET Statement
  • Interleaving Data Sets Using the SET Statement
  • Combining Data Sets Using a One-to- One Match Merge
  • Combining Data Sets Using a One-to- Many Match Merge
  • Merging Summary Statistics with the Original Data
  • Combining a Grand Total with the Original Data
  • Updating a Master Data Set with Transactions
  • Using SAS Data Set Options
  • Tracking and Selecting Observations with the IN= Option
  • Writing Multiple Data Sets Using the OUTPUT Statement
  • Making Several Observations from One Using the OUTPUT Statement
  • Changing Observations to Variables Using PROC TRANSPOSE
  • Using SAS Automatic Variables

Writing Flexible Code with the SAS Macro Facility

  • Macro Concepts
  • Substituting Text with Macro Variables
  • Creating Modular Code with Macros
  • Adding Parameters to Macros
  • Writing Macros with Conditional Logic
  • Writing Data-Driven Programs with CALL SYMPUT
  • Debugging Macro Errors

Basic Statistical Procedures

  • Examining the Distribution of Data with PROC UNIVARIATE
  • Producing Statistics with PROC MEANS
  • Testing Categorical Data with PROC FREQ
  • Examining Correlations with PROC CORR
  • Using PROC REG for Simple Regression Analysis
  • Reading the Output of PROC REG
  • Using PROC ANOVA for One-Way Analysis of Variance
  • Reading the Output of PROC ANOVA
  • Graphical Interfaces for Statistical Analysis

Exporting Your Data

  • Methods for Exporting Your Data
  • Writing Files Using the Export Wizard
  • Writing Delimited Files with the EXPORT Procedure
  • Writing PC Files with the EXPORT Procedure
  • Writing Raw Data Files with the DATA Step
  • Writing Delimited and HTML Files using ODS
  • Sharing SAS Data Sets with Other Types of Computers

Debugging Your SAS Programs

  • Writing SAS Programs That Work
  • Fixing Programs That Don’t Work
  • Searching for the Missing Semicolon
  • Note: INPUT Statement Reached Past the End of the Line
  • Note: Lost Card
  • Note: Invalid Data
  • Note: Missing Values Were Generated
  • Note: Numeric Values Have Been Converted to Character (or Vice Versa)
  • DATA Step Produces Wrong Results but No Error Message


  • Introduction To SAS/ SQL
  • Features
  • Uses
  • Terminology
  • Data Types, Key Words, & Operators
  • Functions, Predicates
  • Formatting Output
  • Group By Clause
  • Order By Clause
  • Having Clause
  • Case Expression And Conditional Logic
  • Creating ,Populating & Deleting Tables
  • Alter Table Statement
  • Changing Column’s Length
  • Joins
  • Constraints
  • Renaming A Table & Columns
  • Views

Are you keen on building a career in the domain of programming?
Then enroll in the best Machine Learning training center in Chennai.

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