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Data Science with Machine Learning Course Syllabus

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In Our Data Science with Machine Learning Course Syllabus, you’ll dive into data preparation, data exploration, and feature creation. The syllabus covers various machine learning techniques for tasks such as data classification, prediction, clustering, and simplification. You’ll also learn to evaluate models, adjust parameters, and deploy them.

Download our Data Science with Machine Learning course syllabus PDF for the best Data Science with Machine Learning Training Institute in Chennai.

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Breakdown of Data Science with Machine Learning Course Fee and Batches

Hands On Training
3-5 Real Time Projects
60-100 Practical Assignments
3+ Assessments / Mock Interviews
April 2024
Week days
(Mon-Fri)
Online/Offline

2 Hours Real Time Interactive Technical Training 

1 Hour Aptitude 

1 Hour Communication & Soft Skills

(Suitable for Fresh Jobseekers / Non IT to IT transition)

Course Fee
April 2024
Week ends
(Sat-Sun)
Online/Offline

4 Hours Real Time Interactive Technical Training

(Suitable for working IT Professionals)

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Syllabus for The Data Science with Machine Learning Course

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Module 1 – Core Java Fundamentals

1

  • Java Programming Language Keywords
  • Literals and Ranges of All Primitive
  • Data Types
  • Array Declaration, Construction, and Initialization
Module 2 – Declarations and Access Control

2

  • Declarations and Modifiers
  • Declaration Rules
  • Interface Implementation
Module 3 – Object Orientation, Overloading and Overriding, Constructors

3

  • Benefits of Encapsulation
  • Overridden and Overloaded Methods
  • Constructors and Instantiation
  • Legal Return Types
Module 4 – Flow Control, Exceptions, and Assertions

4

  • Writing Code Using if and switch statements
  • Writing Code Using Loops
  • Handling Exceptions
  • Working with the Assertion Mechanism
  • Write Java Programs
Module 5 – TestNG

5

  • Setting up TestNG
  • Testing with TestNG
  • Composing test and test suites
  • Generating and analyzing HTML test reports
  • Troubleshooting
Module 6 – Machine Learning

6

  • Introducing Machine Learning
  • To Automate or Not to Automate?
  • Test Automation for Web Applications
  • Machine Learning Components
  • Supported Browsers
  • Flexibility and Extensibility
Module 7 – Machine Learning -IDE

7

  • Introduction
  • Installing the IDE
  • Opening the IDE
  • IDE Features
  • Building Test Cases
  • Running Test Cases
  • Debugging
  • Writing a Test Suite
  • Executing Machine Learning -IDE Tests on Different Browsers
Module 8 – XPATH

8

  • Understanding of Source files and Target
  • XPATH and different techniques
  • Using attribute
  • Text ()
  • Following
Module 9 – Machine Learning

9

  • Introduction
  • How Machine Learning Works
  • Installation
  • Configuring Machine Learning With Eclipse
  • Machine Learning RC Vs Machine Learning
  • Programming your tests in WebDriver
  • Debugging WebDriver test cases
  • Troubleshooting
  • Handling HTTPS and Security Pop-ups
  • Running tests in different browsers
  • Handle Alerts / Pop-ups and Multiple Windows using WebDriver
Module 10 – Automation Test Design Considerations

10

  • Introducing Test Design
  • What to Test
  • Verifying Results
  • Choosing a Location Strategy
  • UI Mapping
  • Handling Errors
  • Testing Ajax Applications
  • How to debug the test scripts
Module 11 – Handling Test Data

11

  • Reading test data from excel file
  • Writing data to excel file
  • Reading test configuration data from text file
  • Test logging
  • Machine Learning Grid Overview
Module 12 – Building Automation Frameworks Using Machine Learning

12

  • What is a Framework
  • Types of Frameworks
  • Modular framework
  • Data Driven framework
  • Keyword driven framework
  • Hybrid framework
  • Use of Framework
  • Develop a framework using TestNG/WebDriver
Python – Overview

13

  • A brief history of python
  • Application and trends in python
  • Available python versions
Python – Environment Setup

14

  • Getting and installing python
  • Environmental variables and idle
  • Executing python from command line
Fundamentals

15

  • I/o
  • Naming conventions
  • Datatypes:
  • Numbers
  • String
  • List
  • Tuple
  • Dictionary
  • Set
Python Operators

16

  • List, Tuple, Dictionary, Set Methods
  • Statements: If, elif, Break, Continue
  • Loops: For loop, while loop
  • Functions
Oops Concepts:

17

  • Class and objects
  • Getters and setters
  • Properties
  • Inheritance
  • Polymorphism
  • Special Functions of Python: Lambda, Map, Reduce, Filter
Modules in Python:

18

  • Math
  • Arrow
  • Geopy
  • Beautiful soup
  • Numpy
  • Sys
  • Os
Multithreading:

19

  • Introducing threads and life cycles
  • Priorities
  • Dead Locks
Exceptional Handling

20

  • Errors
  • Runtime errors
  • Exceptional model
  • Exceptional hierarchy
  • Handling multiple exception
  • Raise exceptions

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