Machine Learning Training in Chennai

Machine Learning Training Institute in Chennai

Machine Training in Chennai

Want to become a specialist in Machine learning? Well, you can learn this form of artificial intelligence from the expert trainers in SLA. Our Machine Learning Training in Chennai is a highly interactive session with a keen focus on quality.

What is Machine Learning?

Data science is mainly concerned with getting better insight from the data. Here then, machine learning is a main area of focus. Algorithms form the essence of machine learning and the software applications turn out more accurate in forecasting results. The highlight is that there is no need for explicit programming with machine learning.

Are you contemplating on a machine learning project? Python, R and SAS are powerful solutions that assist in performing analytics work in a simple manner. Get trained in SLA and gain from the complete machine learning course with Python Training, SAS Training and R Training.

Machine Learning with Python Training in Chennai

You might be pondering over implementing ML algorithms using Python. Then Machine Learning Course in Chennai from SLA is the best place to start with. The trainer is very proficient in the ML concept and will help you to become an expert in the same. He teaches in a very interesting manner and makes you understand the essentials of machine learning with ease. With the help of our labs, get an enriching experience in dealing with Python functions and libraries.

Who are the Right Candidates for Python Machine Learning Training?

  • Software Developers
  • Software Testers
  • Projects Leads/ Managers
  • Business Data/Analyst
  • Freshers from any stream including BE/B. TECH/MCA/BCA/ B Sc
  • Job seekers in the role of Data Science Developer
  • Business Intelligence & Data Visualisation Professionals

Prior knowledge Required

  • Those who have the keen interest to take up data science can opt for Python with Machine Learning.
  • Fundamental programming knowledge is advantageous
  • Fundamental statistics knowledge is essential.

Machine Learning with R Training in Chennai

In the last few years, machine learning has evolved as a mainstream domain and the career opportunities are also on the rise. When you take up the Machine Learning Course with R in Chennai you will be gaining knowledge of practical coding with machine learning algorithms.

SLA is the Best Machine Learning with R Training Center in Chennai. Our expert trainer will assist you in implementing ML concepts and techniques in the R framework. The trainers focus on giving individual attention to the students.

Who can gain from Machine Learning with R course?

  • Analytics professionals keen on taking up R
  • Software professionals wanting to migrate to the analytics field
  • Graduates who want to test waters in Analytics.
  • Professionals with relevant experience and who want to get the full value of data science
  • Any aspiring individual with a passion for data science

Course Prerequisites:

  • Fundamental knowledge of programming languages including Python or Java
  • Students with mathematical background will gain from this course
  • Knowledge of simple linear algebra and differential calculus.

Machine Learning with SAS Training in Chennai

In this Machine Learning with SAS Training in Chennai you will gain proficiency in analytics strategies with the help of SAS. SLA lays a holistic foundation for success in the domain of Machine Learning.

Who the Course is for?

  • Analytics professionals who are interested in SAS.
  • IT professionals who want to migrate their career in the analytics field
  • Software developers inclined in taking up a career in analytics
  • Graduates keen on developing career in analytics and data science
  • Experienced professionals who want to make the utmost application of data science.

Prerequisites

  1. No earlier programming skills required.
  2. Even B.com, M.com, B.A. M.B.A graduates can learn SAS.

Machine Learning Training Course Duration in SLA

Machine Learning Course Duration
TrackRegular TrackWeekend TrackFast Track
Course Duration30 – 40 Days5 WeekendsAccording to your Convenience
Hours2 hours a day6 hours a dayFits your Requirements
Training ModeLive ClassroomLive ClassroomLive Classroom

Note: SLA give importance to the convenience of the candidates and hence makes sure that the timings are accommodative.

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.

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
  • SETTING PATH IN Windows
  • Python Sub Packages
  • Uses of Python in Data Science
  • USES OF PYTHON IN IOT
  • 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 HASHING?
  • 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

Multi-Threading

  • 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 (re.search())
  • 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

Introduction

  • 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

Functions

  • 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)

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

SAS/SQL

  • 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
Future Scope of Machine Learning

Machine learning can be a good domain for any company whether it is a top MNC or a startup organization. Today the things that are concentrated on manually will be performed tomorrow by machines. There are a plethora of ways in which machine learning is useful to us. For example, the pricing in Uber/Ola, Google self-driving car, product recommendations, spam filtering are all the results of machine learning. So it is obvious that machine learning has turned out to be an integral part of lives and become more so in the near future.