Data Science with ML Course Syllabus
duration
2-3 Months
EMI
0% Interest
Mode
Live Online & Classroom Training
We meticulously prepared Data Science with ML Course Syllabus at SLA by industry experts that can be customized by freshers and working professionals according to their skill level and experience. We cover from scratch advanced methods such as fundamental machine learning concepts, supervised learning, unsupervised learning, neural networks, artificial neural networks, back-propagation, deep neural network, deep learning methods, linear regression, and logistic regression. SLA provides the simplified learning process with hands-on exposure and project practices in our Data Science with ML Training Institute in Chennai with 100% Job Assistance.
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Course Syllabus
Download SyllabusModule 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 – Testing
- 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 Web Driver
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/ Web Driver
The Data Science with ML Course Syllabus at SLA Institute is really good. It teaches you a lot about data analysis and machine learning techniques. You get to work on projects and use what you learn in life. This helps you become confident in making models that can predict things and solve problems. The Data Science with ML Course Syllabus at SLA Institute gets you ready for jobs, in data science, machine learning and artificial intelligence.
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