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      Data Science with Machine Learning Course Syllabus

      Data Science with ML Training Syllabus

      Our Data Science with Machine Learning Course Syllabus is enhancing the students to analyze the large chunks of data automatically using ML algorithms. We have framed our Data Science with ML Course Curriculum with key important concepts such as machine learning introduction, IDEs, X Path, automation testing design consideration, automation framework development, test data handling, Tkinter, Python, and data visualization.

      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 – 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
      Fundamental Python – Overview
      • A brief history of python
      • Application and trends in python
      • Available python versions
      Python – Environment Setup
      • Getting and installing python
      • Environmental variables and idle
      • Executing python from command line
      Fundamentals
      • I/o
      • Naming conventions
      • Datatypes:
      • Numbers
      • String
      • List
      • Tuple
      • Dictionary
      • Set
      Python Operators
      • List, Tuple, Dictionary, Set Methods
      • Statements: If, elif, Break, Continue
      • Loops: For loop, while loop
      • Functions
      • Python Operators
      • List, Tuple, Dictionary, Set Methods
      • Statements: If, elif, Break, Continue
      • Loops: For Loop, While Loop
      • Functions
      Oops Concepts:
      • Class and objects
      • Getters and setters
      • Properties
      • Inheritance
      • Polymorphism
      • Special Functions of Python: Lambda, Map, Reduce, Filter
      Accordion Title
      Modules in Python :
      • Math
      • Arrow
      • Geopy
      • Beautiful soup
      • Numpy
      • Sys
      • Os
      Multithreading
      • Introducing threads and life cycles
      • Priorities
      • Dead Locks
      Exceptional Handling
      • Errors
      • Runtime errors
      • Exceptional model
      • Exceptional hierarchy
      • Handling multiple exception
      • Raise exceptions
      File Handling
      • Text files
      • Csv files
      Regular Expressions
      • Simple character matches
      • Flags, quantifers, greedy matches
      • Grouping and matching objects
      • Matching at beginning or end
      • Substituting and splitting a string
      • Compiling regular expressions
      • Generators Iterators
      • Decorators
      • Closures
      Gui Interfacing: Tkinter
      • Widgets
      • Integrated application
      • Mysql/with application
      • Converting .exe
      Analytical Python Syllabus – Introduction
      Datascience Modules:
      • pandas
      • numpy
      • scipy
      • matplotlib
      Python Data Processing
      • Python Data Operations
      • Python Data cleansing
      • Python Processing CSV Data
      • Python Processing JSON Data
      • Python Processing XLS Data
      • Python Relational databases
      • Python NoSQL Databases
      • Python Date and Time
      • Python Data Wrangling
      • Python Data Aggregation
      • Python Reading HTML Pages
      • Python Processing Unstructured Data
      • Python word tokenization
      • Python Stemming and Lemmatization
      Python Data Visualization
      • Python Chart Properties
      • Python Chart Styling
      • Python Box Plots
      • Python Heat Maps
      • Python Scatter Plots
      • Python Bubble Charts
      • Python 3D Charts
      • Python Time Series
      • Python Geographical Data
      • Python Graph Data
      Statistical Data Analysis
      • Python Measuring Central Tendency
      • Python Measuring Variance
      • Python Normal Distribution
      • Python Binomial Distribution
      • Python Poisson Distribution
      • Python Bernoulli Distribution
      • Python P-Value
      • Python Correlation
      • Python Chi-square Test
      • Python Linear Regression
      Conclusion

      Join us to discover the wide range of opportunities by learning through our Data Science with ML Course Syllabus. SLA is the leading Data Science with Machine Learning Training Institute in Chennai.

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