Python with GenAi– Zero to Advance Training
Learn Python from Basics to Advanced and build real-world Generative AI applications through hands-on practical training.
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About this Course
## Course Overview – Python with Generative AI
A Complete **Basic to Advanced Level Training Program** designed for students, freshers, job seekers and working professionals. No prior Python or AI knowledge is required.
Start with Python programming fundamentals and gradually progress to advanced Python concepts, Object-Oriented Programming, Artificial Intelligence, Generative AI, Large Language Models and Prompt Engineering.
Learn to integrate Python with Google Gemini and OpenAI APIs, develop AI-powered applications using Streamlit, and explore document processing, Embeddings and RAG fundamentals.
This 2-month practical training program includes hands-on coding, real-world use cases and 7 practical projects to build your own Generative AI applications.
**Course Highlights:**
- Complete Basic to Advanced Practical Training
- Duration: 2 Months | Daily 1 Hour
- Python Programming from Scratch
- Python Intermediate and OOP Concepts
- Generative AI Fundamentals
- Advanced Prompt Engineering
- Gemini and OpenAI API Integration
- AI Application Development using Streamlit
- Embeddings and RAG Fundamentals
- 7 Hands-on Projects
- Suitable for Students, Freshers and Working Professionals
What you'll learn
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Course Curriculum
- Introduction to Python, Features and Applications
- Python Installation, VS Code Setup and First Program
- Variables, Data Types and Type Casting
- Input, Output and Operators
- Conditional Statements – if, elif, else
- Loops – for and while
- Loop Control Statements – break, continue, pass
- Strings and String Methods
- Lists, Tuples, Sets and Dictionaries
- Functions, Arguments and Return Values
- Built-in Functions
- Modules, Packages and Import Statements
- File Handling – Read, Write and Append
- Exception Handling
- Practical Python Coding Exercises
- Introduction to OOP, Classes and Objects
- Constructors – __init__() and self
- Instance Variables and Class Variables
- Inheritance and its Types
- Polymorphism and Encapsulation
- Mini Project using Python OOP
- Introduction to Artificial Intelligence
- Machine Learning vs Deep Learning vs Generative AI
- Understanding Generative AI and Large Language Models
- How ChatGPT, Gemini and Other AI Tools Work
- Tokens, Context Windows and AI Limitations
- Generative AI Applications in Different Industries
- Introduction to Prompt Engineering
- Zero-Shot and Few-Shot Prompting
- Role-Based and Context-Based Prompting
- Chain-of-Thought and Step-by-Step Prompting
- Structured Output – JSON and Tables
- Practical Prompt Engineering Tasks
- Introduction to AI APIs
- API Keys and API Integration
- HTTP Requests and JSON Handling
- Integrating Google Gemini API with Python
- Integrating OpenAI API with Python
- Creating AI Responses using Python
- System Instructions and Conversation Memory
- Building Reusable AI Functions and Error Handling
- Introduction to Streamlit
- Creating AI Web Applications using Python
- Building an AI Chatbot
- Adding Conversation History to Chatbot
- Text Summarization and Content Generation
- PDF Upload and Text Extraction
- AI-Based PDF Question Answering
- Introduction to Embeddings and Vector Databases
- Introduction to RAG (Retrieval-Augmented Generation)
- Student Management System – Python, Functions, OOP, File Handling.
- Expense Tracker Application – Python, Data Structures, File Handling.
- AI Chatbot – Python, Gemini API, Prompt Engineering.
- AI Resume Analyzer – Python, Generative AI, Streamlit.
- AI Interview Preparation Assistant – Python, Gemini API, Prompt Engineering.
- AI Interview Preparation Assistant – Python, Gemini API, Prompt Engineering.
- AI-Powered Data Analysis Assistant
Facilities Provided
Frequently Asked Questions
Quick Info
- LevelAdvanced
- Duration60+ hours
- Learners2800+
- Rating★ 4.9
- Certificate✅ Yes
- Internship❌ No
- Placement❌ No