Machine Learning Course Syllabus

Machine Learning Course Syllabus

The Machine Learning Course Syllabus is prepared keeping in mind the advancements in this trending technology. The candidate will get a clear idea about machine learning and will also be industry ready. This is because the syllabus is framed keeping the industry standards in mind. The content of the syllabus is also the fresh and best. If you want to have a clear understanding of what topics will be covered in the syllabus, then look at our syllabus below. This will help you to know about what we teach.

Module 1 – Core Java Fundamentals

  • Java Programming Language Keywords
  • Literals and Ranges of All Primitive
  • Data Types
  • Array Declaration, Construction, and Initialization

Module 2 – Declarations and Access Control

  • Declarations and Modifiers
  • Declaration Rules
  • Interface Implementation

Module 3 – Object Orientation, Overloading and Overriding, Constructors

  • Benefits of Encapsulation
  • Overridden and Overloaded Methods
  • Constructors and Instantiation
  • Legal Return Types

Module 4 – Flow Control, Exceptions, and Assertions

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

Module 5 – TestNG

  • Setting up TestNG
  • Testing with TestNG
  • Composing test and test suites
  • Generating and analyzing HTML test reports
  • Troubleshooting

Module 6 – Machine Learning 

  • 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

  • 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

  • Understanding of Source files and Target
  • XPATH and different techniques
  • Using attribute
  • Text ()
  • Following

Module 9 – Machine Learning 

  • 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

  • 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

  • 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 

  • 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

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