Course Information
- Course Price $250
- Total Students 800+
- Course Duration 4 Weeks
Description
Machine learning and artificial intelligence are creating waves in every industry. There is no better time for you to get trained in one of the most demanding skills nowadays.
ML scientists develop methods for forecasting product suggestions and product demand and dive into big data to automatically extract patterns. Organizations hire positions for Machine Learning Engineer, NLP Data Scientist, Machine Learning Analyst, Machine Learning Scientist, Data Sciences Lead etc.
Benefits
- Machine learning profiles are available in every industry. Data and machine learning let companies make smart products and offer customers amazing products.
- ML scientists develop methods for forecasting product suggestions and product demand and dive into big data to automatically extract patterns.
- The data scientists would be in a position to bring huge facets and functionalities only with the model designing and deployment performed along with MLEs. So we can say that both these roles are an integral part of an efficient data science team.
Syllabus
Module 1-Core Java Fundamentals
- Java Programming Language Keywords
- Literals and Ranges of all Primitive
- Data Types
- Arrays Declaration,Construction,and initilization
Module 2-Declarations and Access Control
- Declaration and Modification
- 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-IDE
- 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 Targets
- 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 Consideration
- 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 Framework Using Machine Learning
- what is a Framework
- Types of Framework
- Modular framework
- Data Driven framework
- Keyword Driven Framework
- Hybrid framework
- Use of Framework
- Develop a framework using testNG/WebDriver
