Artifical Intelligence

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5 Months - 120 Hrs

3 Alternative days/ week

09:30 pm-11:30 pm

Eligibility :- Any Science or Engineering graduate (preferably from CS/EC/IT) with a computer background.

Course Overview

This Artificial Intelligence (AI) course is designed to fulfill industry requirements and is focused on job readiness and skill development. The course provides a comprehensive understanding of AI concepts, programming, and hands-on projects in various domains like Machine Learning, Deep Learning, and DevOps.

  • Module A: Introduction
  • Basics of Industries – Learn the current industry standards and trends.
  • Programming Requirements – Overview of required programming languages and basics.
  • Introduction to Python – Why Python is important, installation, first Python program, comments, and indentation.
  • Data Types – Explore sequence types, special types, operators, operands, and I/O.
  • Control Flow & Command Line – Command-line basics and control flow structures.
  • Module B: Modules and Functions
  • Functions – Different types of functions, functions inside functions, passing functions as parameters, and recursion.
  • Lambdas – Map, reduce functions, decorators, generators, and keyword use in Python.
  • Modules – Creating and using Python modules, along with sample modules.
  • Module C: Object-Oriented Programming (OOP)
  • Encapsulation – Implementing private fields and name mangling in Python.
  • Inheritance – Using inheritance, method overriding, and the super() function.
  • Polymorphism – Concepts like duck typing, operator overloading, and runtime polymorphism.
  • Abstraction – Working with abstract classes and interfaces.
  • Module D: Exception Handling
  • Exception Hierarchy – Understanding the hierarchy and handling exceptions using try, except, else, and finally.
  • Custom Exceptions – Creating and raising custom exceptions.
  • Logging – Using logging for error tracking and configuration.
  • Module E: File Systems
  • File Operations – Reading, writing, and appending files in Python.
  • Pickle and Unpickle – Introduction to serialization and deserialization.
  • Logging and Assertions – Configuring logging and assertions in Python code.
  • Module F: Support Tools
  • Regular Expressions – Understanding sequence characters, quantifiers, and special characters.
  • Date and Time – Working with current dates, sorting, combining date and time, and introducing sleep functionality.
  • Module G: Data Structures
  • Algorithms Analysis – Time and space complexity.
  • Data Structures – Arrays, strings, and different types of data structures.
  • Sorting & Searching – Theory and implementation of sorting and searching algorithms.
  • Advanced Structures – Stack, queues, linked lists, and trees.
  • Dynamic Programming & Recursion – Solving problems with recursion and backtracking.
  • Module H: Tools and Libraries
  • Computer Vision & Deep Learning – Hands-on experience with OpenCV, TensorFlow, PyTorch, and Tesseract.
    • Project: Real-time project in Computer Vision.
  • Machine Learning – Using popular libraries like NumPy, Matplotlib, Pandas, and SciKit Learn.
    • Project: Real-time project in Machine Learning.
  • Module I: DevOps and Cloud
  • Version Control & CI/CD – Git, Jenkins, and BitBucket.
  • Cloud Technologies – Introduction to Azure and Docker.