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Advance Course in Generative AI with Python

Advance Course in Generative AI with Python

Go from Python and machine-learning foundations to building, fine-tuning and deploying real Generative AI applications — LLMs, prompt engineering, RAG, AI agents and multimodal generation across text, image, audio and video.

(22 reviews)
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Description

Welcome to Generative AI with Python — a complete, hands-on program that takes you from Python foundations all the way to production-ready Generative AI applications.

The course is structured into 4 learning phases and 29 modules, covering 30+ tools and APIs, and finishes with a live, deployed capstone project. Here's what you'll get:

  • Phase 1 — Foundations – Python programming, data handling with NumPy & Pandas, data visualization, machine learning, deep learning with TensorFlow/Keras/PyTorch, and NLP with Hugging Face.
  • Phase 2 — Generative AI & LLMs – How LLMs work, working with the OpenAI, Anthropic Claude & Google Gemini APIs, prompt engineering, embeddings & semantic search, vector databases, RAG, LangChain, LlamaIndex, AI agents, multi-agent systems, fine-tuning (LoRA/PEFT) and chatbots.
  • Phase 3 — Multimodal AI – Image generation with Stable Diffusion, DALL·E & Midjourney; audio & video generation with ElevenLabs, Whisper, Runway & HeyGen; and vision-language models like GPT-4o, Gemini & Claude.
  • Phase 4 — Productionizing & Responsible AI – Build apps with Streamlit, Gradio & FastAPI, deploy with Docker & Hugging Face Spaces, LLM evaluation & observability, and AI safety, ethics & governance.
  • Live Capstone & Portfolio – Ship an end-to-end deployed GenAI application, a RAG knowledge assistant, an AI agent with tool use and a multimodal project — plus a certificate of completion.

Courses Curriculum

  • Module 1: Python Programming Foundations
    • 01. Setup with Jupyter / Google Colab
    • 02. Variables, Data Types & Operators
    • 03. Lists, Tuples, Sets & Dictionaries
    • 04. Conditional Statements & Loops
    • 05. Functions, *args & **kwargs
    • 06. Lambda, Map, Filter & Comprehensions
    • 07. Object-Oriented Programming
    • 08. Exception Handling
    • 09. Modules, Packages & File Handling
  • Module 2: Python for Data & Automation
    • 01. Calling APIs with requests
    • 02. GET & POST Requests
    • 03. Parsing JSON Data
    • 04. Web Scraping with BeautifulSoup
    • 05. Regular Expressions
    • 06. Automating Tasks with Python
    • 07. Virtual Environments & pip
    • 08. Reading/Writing CSV, JSON & Pickle
  • Module 3: Data Handling with NumPy & Pandas
    • 01. NumPy Arrays & Operations
    • 02. Reshaping, Indexing & Slicing
    • 03. Universal Functions & Random Data
    • 04. Pandas DataFrames & Series
    • 05. Read/Write Excel, CSV & Databases
    • 06. Handling Missing Data
    • 07. Group By, Merge & Pivot Tables
    • 08. Data Cleaning & Transformation
    • 09. Time Series Basics
  • Module 4: Data Visualization
    • 01. Matplotlib Fundamentals
    • 02. Line, Bar & Scatter Plots
    • 03. Histograms & Pie Charts
    • 04. Box & Stack Plots
    • 05. Customizing Plots & Subplots
    • 06. Seaborn for Statistical Plots
    • 07. Visualizing Model Results
    • 08. Dynamic Time Series Plots
  • Module 5: Machine Learning Foundations
    • 01. What is Machine Learning
    • 02. Supervised vs Unsupervised Learning
    • 03. Linear & Logistic Regression
    • 04. Train/Test Split & Cross-Validation
    • 05. Decision Trees & Random Forests
    • 06. Support Vector Machines
    • 07. K-Means Clustering & KNN
    • 08. Naive Bayes Classifier
    • 09. Hyperparameter Tuning & Regularization
    • 10. Model Evaluation & Deployment
  • Module 6: Deep Learning & Neural Networks (TensorFlow, Keras, PyTorch)
    • 01. Introduction to Neural Networks
    • 02. Perceptron & Activation Functions
    • 03. Forward & Backpropagation
    • 04. Building Networks with Keras
    • 05. PyTorch Basics
    • 06. CNNs for Images
    • 07. RNNs & LSTMs for Sequences
    • 08. Transfer Learning
    • 09. Training on GPUs
  • Module 7: Natural Language Processing (NLP) (NLTK, spaCy, Hugging Face)
    • 01. Text Preprocessing & Tokenization
    • 02. Bag of Words & TF-IDF
    • 03. Word Embeddings (Word2Vec, GloVe)
    • 04. Sequence Models
    • 05. The Attention Mechanism
    • 06. Introduction to Transformers
    • 07. Hugging Face Transformers
    • 08. Text Classification & Sentiment Analysis
  • Module 8: Introduction to Generative AI
    • 01. What is Generative AI
    • 02. Generative vs Discriminative Models
    • 03. Evolution: GANs, VAEs, Diffusion, LLMs
    • 04. The GenAI Landscape & Use Cases
    • 05. Foundation Models Explained
    • 06. Open-Source vs Proprietary Models
    • 07. Business & Ethical Impact
    • 08. Setting Up Your GenAI Toolkit
  • Module 9: How Large Language Models Work
    • 01. The Transformer Architecture
    • 02. Self-Attention & Multi-Head Attention
    • 03. Tokenization & Tokens
    • 04. Pretraining & Next-Token Prediction
    • 05. Context Windows
    • 06. Temperature, Top-p & Sampling
    • 07. Model Sizes & Parameters
    • 08. Capabilities, Limits & Hallucinations
  • Module 10: Working with LLM APIs (OpenAI, Anthropic Claude, Google Gemini)
    • 01. Introduction to LLM APIs
    • 02. OpenAI GPT API
    • 03. Anthropic Claude API
    • 04. Google Gemini API
    • 05. API Keys & Authentication
    • 06. Chat Completions & Messages
    • 07. Streaming Responses
    • 08. Tokens, Pricing & Rate Limits
    • 09. Handling Responses in Python
  • Module 11: Prompt Engineering
    • 01. Anatomy of a Prompt
    • 02. System, User & Assistant Roles
    • 03. Zero-Shot & Few-Shot Prompting
    • 04. Instructions, Context & Constraints
    • 05. Output Formatting (JSON, Markdown)
    • 06. Role & Persona Prompting
    • 07. Reusable Prompt Templates
    • 08. Pitfalls & Best Practices
  • Module 12: Advanced Prompting & Reasoning
    • 01. Chain-of-Thought Prompting
    • 02. Self-Consistency
    • 03. ReAct (Reason + Act)
    • 04. Tree-of-Thought
    • 05. Prompt Chaining
    • 06. Structured Outputs & Schemas
    • 07. Guardrails & Output Validation
    • 08. Prompt Optimization & Testing
  • Module 13: Embeddings & Semantic Search
    • 01. What are Embeddings
    • 02. Generating Text Embeddings
    • 03. Measuring Similarity (Cosine)
    • 04. Semantic vs Keyword Search
    • 05. Chunking Strategies
    • 06. Choosing Embedding Models
    • 07. Building a Semantic Search Engine
  • Module 14: Vector Databases (ChromaDB, Pinecone, FAISS, Weaviate)
    • 01. Why Vector Databases
    • 02. Storing & Indexing Embeddings
    • 03. ChromaDB
    • 04. Pinecone
    • 05. FAISS
    • 06. Metadata Filtering
    • 07. Similarity Search at Scale
    • 08. Choosing a Vector Store
  • Module 15: Retrieval-Augmented Generation (RAG)
    • 01. Why RAG
    • 02. RAG Architecture
    • 03. Document Loading & Chunking
    • 04. Embedding & Indexing
    • 05. Retrieval + Generation Pipeline
    • 06. Reducing Hallucinations
    • 07. Evaluating RAG Quality
    • 08. Advanced RAG Techniques
  • Module 16: LangChain Framework (LangChain, LangSmith)
    • 01. Introduction to LangChain
    • 02. Models, Prompts & Output Parsers
    • 03. Chains & Memory
    • 04. Document Loaders & Retrievers
    • 05. Building RAG with LangChain
    • 06. LangChain Expression Language (LCEL)
    • 07. Debugging with LangSmith
  • Module 17: LlamaIndex for Data-Aware Apps
    • 01. Introduction to LlamaIndex
    • 02. Data Connectors & Loaders
    • 03. Building Indexes
    • 04. Query Engines
    • 05. Combining LlamaIndex with LLMs
    • 06. Knowledge-Base Applications
  • Module 18: AI Agents & Tool Use (LangChain, MCP)
    • 01. What are AI Agents
    • 02. Tool / Function Calling
    • 03. The ReAct Pattern
    • 04. Building Agents with LangChain
    • 05. Model Context Protocol (MCP)
    • 06. Agent Memory & Planning
    • 07. Connecting Agents to APIs & Databases
    • 08. Building an Autonomous Task Agent
  • Module 19: Multi-Agent Systems (LangGraph, CrewAI, AutoGen)
    • 01. Multi-Agent Architectures
    • 02. Agent Collaboration & Roles
    • 03. Orchestration & Workflows
    • 04. Human-in-the-Loop
    • 05. Building Agent Crews
    • 06. Real-World Agent Use Cases
  • Module 20: Fine-Tuning Large Language Models (Hugging Face, LoRA, PEFT)
    • 01. Fine-Tune vs Prompt vs RAG
    • 02. Preparing Training Data
    • 03. Supervised Fine-Tuning
    • 04. LoRA & QLoRA
    • 05. PEFT (Parameter-Efficient Fine-Tuning)
    • 06. Fine-Tuning with Hugging Face
    • 07. Evaluating Fine-Tuned Models
    • 08. Deploying Custom Models
  • Module 21: Building Chatbots & Assistants
    • 01. Designing Conversational Flows
    • 02. Context & Conversation Memory
    • 03. Custom-Knowledge Chatbots (RAG)
    • 04. Designing a Shopping Assistant
    • 05. Domain-Specific Assistants
    • 06. Streaming Chat UIs
    • 07. End-to-End Chatbot Project
  • Module 22: Image Generation Models (Stable Diffusion, DALL·E, Midjourney)
    • 01. How Diffusion Models Work
    • 02. GANs Overview
    • 03. Stable Diffusion
    • 04. DALL·E & Midjourney
    • 05. Text-to-Image Prompting
    • 06. Image-to-Image & Inpainting
    • 07. ControlNet & Image LoRA
    • 08. Generating Images via API
  • Module 23: Audio & Video Generation (ElevenLabs, Whisper, Runway, HeyGen)
    • 01. Text-to-Speech (TTS)
    • 02. Voice Cloning
    • 03. Speech-to-Text (Whisper)
    • 04. Music Generation
    • 05. Text-to-Video Models
    • 06. AI Avatars & Talking Heads
    • 07. Building Audio/Video Pipelines
  • Module 24: Multimodal AI & Vision-Language Models (GPT-4o, Gemini, Claude)
    • 01. What is Multimodal AI
    • 02. Vision-Language Models
    • 03. Image Understanding & Captioning
    • 04. Document & Chart Analysis
    • 05. Combining Text, Image & Audio
    • 06. Multimodal RAG
    • 07. Building Multimodal Applications
  • Module 25: Building GenAI Apps with Python (Streamlit, Gradio, FastAPI)
    • 01. App Architecture for GenAI
    • 02. Building UIs with Streamlit
    • 03. Building UIs with Gradio
    • 04. REST APIs with FastAPI
    • 05. Managing Secrets & Config
    • 06. Caching & Cost Optimization
    • 07. Logging & Error Handling
  • Module 26: Deploying GenAI Applications (Docker, HF Spaces, Cloud)
    • 01. Containerizing with Docker
    • 02. Deploying to the Cloud
    • 03. Serverless Deployment
    • 04. Hugging Face Spaces
    • 05. Scaling & Load Handling
    • 06. Monitoring in Production
    • 07. CI/CD for AI Apps
  • Module 27: LLM Evaluation & Observability
    • 01. Why Evaluation Matters
    • 02. Evaluation Metrics for LLMs
    • 03. Building Test Sets
    • 04. LLM-as-a-Judge
    • 05. Tracing & Observability
    • 06. A/B Testing Prompts
    • 07. Cost & Latency Monitoring
  • Module 28: Responsible AI & Safety
    • 01. AI Ethics & Bias
    • 02. Hallucination Mitigation
    • 03. Prompt Injection & Jailbreaks
    • 04. Guardrails & Content Moderation
    • 05. Data Privacy & PII Handling
    • 06. Copyright & Licensing
    • 07. Governance & Compliance
    • 08. Responsible Deployment
  • Module 29: Capstone Project & Certification
    • 01. End-to-End GenAI Application
    • 02. Custom RAG Knowledge Assistant
    • 03. AI Agent with Tool Use
    • 04. Fine-Tuned Domain Model
    • 05. Multimodal AI Project
    • 06. Deployed Live Application
    • 07. Prompt Engineering Portfolio
    • 08. Portfolio Development
    • 09. Interview Preparation
    • 10. Certification Assessment
    • 11. Certificate of Completion

What you'll learn

  • Basics of Python Language
  • Data handling and visualization
  • Visualizing Data
  • Downloading data from APIs
  • I/O with Python
  • Data Analysis APIs
  • Machine Learning Models
  • Web scraping API
  • ChatGPT API
  • Open source LLMs

Requirements

  • Zeal to Code.
  • Basic of Programming Languages.
  • Python, Pycharm/Anaconda, and other APIs

Who this course is for:

  • Beginners to programming languages
  • Students eager to learn about Data Analytics
  • Working on real time data and filtering data
  • Your ultimate goal for learning AI

Reviews

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Pawandeep Singh
Best Institute for Data Science

I am a student of btech IT currently doing six month training at 9i technology i am doing python with data science staff and all the faculty member are very friendly and supportive they helped me gain all the industrial exposure.

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Rahul Yadav
Exceptional!

I am a student of dav college, I am doing Python data science course from here, and the teaching here is very good. And teachers are very helpful.

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Sukhman Saran
Perfect Institute!

I am student of B.Tech. IT currently on industrial training at 9i Technologies. I am doing course of Python with Data Science. This institute is best for learning new skills. Staff and all the faculty members are very friendly and supportive. They helped me gain all the industrial exposure.

  • Classes : Monday - Friday
  • Doubt Session : Saturday
  • Daily Class: 2 Hours
  • Practice Time: Min 3-4 hours
  • Assessment: Online
  • Project Work: Live App
  • Language: English/Hindi
  • Video Recording: Available
  • Certificate: ISO Certified

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