PYTHON AI BootCAMP

Prerequisites:

• Need a Laptop/Desktop with an internet access
• Any web browser (Preferably Google chrome web browser)
• Valid Email ID (Preferably Gmail ID)
• Notebook and Pen (mandatory) 
• Basic programming language is good to have (Not mandatory)

Session 1: Introduction

• Why do we need Python?
• Development Environment setup (python and IDE Installation)
• Program structure and Execution steps
• Interactive Shell and Executable or script files
• User Interface or IDE walk-through, project setup
• Virtual environment setup

Session 2: Data Types and Operations

• Numbers
• Strings
• List
• Tuple
• Dictionary
• Object creation and deletion
• Object properties
• Other Core Types
• operators (assignment, comparison, bitwise and logical operations)

Session 3: Conditional Statements and Loops

• Assignments, Expressions, and prints
• Conditional statement and Syntax Rules (Indentation)
• While and For Loops
• Iterations and Comprehension

Session 4: File Operations

• Opening a file (from and with keyword)
• Using Files and File Modes
• context manager

Session 5: Functions

• Function definition and argument passing
• Function Scope
• Function arguments
• Function Objects
• Anonymous Functions

Session 6: Classes and object oriented programming (oops)

• Introduction to object oriented programming
• Classes and instances
• Classes method calls
• Inheritance
• Polymorphism

Session 7: Exception Handling

• Default Exception Handler
• Catching Exceptions
• Raise an exception
• User defined exception

Session 8: Introduction to Artificial Intelligence

• Introduction to AI
• Types of AI and its other categories of AI
• Computer Vision
• Machine Learning
• Deep learning
• Reinforcement learning
• chatGPT & Prompting
• Gen AI - Large language models (LLM)
• Generate the code using chatGPT

Session 9: Data science

• Introdcution to data science
• cleaning and preparing data
• Statistics in python 
• Understanding data visualization
• EDA steps
• Data formatting
• Data frames
• Scaling & Data scaling
• univariate, variable and multivariate analysis

Session 10: Regression,Classification, Clustering modeling

• What is regression
• Multivariate regression
• Regression evaluation matrix, linear regression
• Classification modeling and logistic regression

Session 11: NLP, Data visualization, Feature selection and data fine tuning

• What is NLP
• Data visualization
• Feature selection and data fine tuning

Session 12: Industry Talk

• About tech industry and what do they expect from Engineer like you
• Tech Industry Professional Standards

Session 13: Practical Projects (Activity Happens Throughout The Course)

• Real time project (Design to implementation)