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    Machine Learning Course Syllabus

    Machine Learning Training Syllabus

    Enhance your software development skills with our industry-relevant Machine Learning Course Syllabus and prepare yourself for new-gen tech jobs. Learn how to predict outcomes accurately through software apps and ML algorithms by our Machine Learning Course Curriculum as it covers the ML environment, fundamentals of ML, OOPs, classes for ML, packages and exception handling, machine learning app developments, utility packages, and framework developments, and generics.

    Machine Learning Language Environment

    • Object Oriented
    • Platform Independent
    • Automatic Memory Management
    • Compiled / Interpreted Approach
    • Robust
    • Secure
    • Dynamic Linking
    • Multi Threaded
    • Built-In Networking

    Machine Learning Fundamentals

    • Data Types
    • Operators
    • Control Statements
    • Arrays
    • Enhanced For-Loop
    • Enumerated Types
    • Static Import
    • Auto Boxing
    • C-Style Formatted I/O
    • Variable Arguments
    Essentials Of Object-Oriented Programming
    • Object And Class Definition
    • Using Encapsulation To Combine Methods And Data In A Single Class
    • Inheritance And Polymorphism
    Writing Machine Learning Classes
    • Encapsulation
    • Polymorphism
    • Inheritance
    • OOP In Machine Learning
    • Class Fundamentals
    • Using Objects
    • Constructor
    • Garbage Collection
    • Method Overloading
    • Method Overriding
    • Static Members
    • Understanding Interface
    • Using Interfaces Class
    • Why Packages
    • Understanding Classpath
    • Access Modifiers And Their Scope
    Exception Handling
    • Importance Of Exception Handling
    • Exception Propagation
    • Exception Types
    • Using Try And Catch
    • Throw, Throws, Finally
    • Writing User Defined Exceptions
    I/O Operations In Machine Learning
    • Byte Oriented Streams
    • File Handling
    • Readers And Writers
    Multi Threaded Programming
    • Introduction To Multi-Threading
    • Understanding Threads And Its States
    • Machine Learning Threading Model
    • Thread Class And Runnable Interface
    • Thread Priorities
    • Thread Synchronization
    • Inter Thread Communication
    • Preventing Deadlocks
    Developing Machine Learning APPS
    • Defining A Solution Without Writing Code
    • Organizing A Concept Solution
    • Creating A Program Skeleton
    • Defining Error Checking Requirements
    • Introduction To Application Security
    Network Programming
    • Introduction To Networking
    • Inet Address
    • URL
    • TCP Socket And Server Socket
    • UDP Socket
    • Developing A Chat Application
    Machine Learning Util Package / Collections Framework
    • Collection And Iterator Interface
    • Enumeration
    • List And ArrayList
    • Vector
    • Comparator
    • Set Interface And SortedSet
    • Hashtable
    • Properties
    • Introduction To Generics
    • Using Built-In Generics Collections
    • Writing Simple Generic Class
    • Bounded Generics
    • Wild Card Generics
    Inner Classes
    • Nested Top Level Classes
    • Member Classes
    • Local Classes
    • Anonymous Classes
    Abstract Window Toolkit
    • Graphics
    • Color And Font
    • AWT Components/Controls
    • Event Handling And Layouts
    Swing Programming
    • Introduction To Swing And MVC Architecture
    • Light Weight Component
    • Swing Hierarchy
    • Atomic Components E.G. JButton, JList And More
    • Intermediate Container E.G. JPanel, JSplitPane And More
    • Top-Level Container E.G. JFrame And JApplet
    • Swing Related Events
    Regular Expressions
    • Introduction
    • Match function
    • Search function
    • Grouping
    • Matching at Beginning or End
    • Match Objects
    • Flags
    • Exercise

    Become a master in ML algorithm and software developments to utilize historical data for predicting new output values through our Machine Learning Training in Chennai. Book a free demo class to check out our Machine Learning Course Curriculum and Institute infrastructure.

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