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Generative AI

Generative AI Course

Master Generative AI with Python, Machine Learning, Deep Learning, Transformers, LangChain, and RAG. Build real-world AI projects and become job-ready.

54 Hours

Duration

Generative AI Course

Master Generative AI with Industry-Focused Training

This Generative AI curriculum is designed to provide structured, hands-on learning covering Python essentials, Machine Learning foundations, Deep Learning with PyTorch, NLP fundamentals, Transformers, LangChain, RAG systems, and deployment strategies. The course emphasizes practical implementation and real-world project development.

Why Learn Generative AI?

Generative AI is transforming industries by enabling intelligent content generation, conversational systems, and context-aware AI applications. This course provides foundational to advanced knowledge required to build production-ready AI systems.

By enrolling in this course, you will learn to:

  • Work with Python fundamentals including variables, control structures, functions, and modules
  • Build machine learning models using Scikit-learn
  • Understand neural networks and deep learning using PyTorch
  • Apply NLP techniques including text preprocessing and vectorization
  • Use Transformers like BERT and GPT-2
  • Implement LangChain for building conversational systems
  • Build Retrieval-Augmented Generation (RAG) applications
  • Deploy and optimize LLM applications

Who Should Enroll?

  • Students and graduates interested in AI careers
  • Developers wanting to transition into Generative AI
  • Professionals looking to build LLM-based applications

What Makes This Course Practical?

  • Hands-on implementation in every module
  • Model building and evaluation
  • Capstone project using Streamlit, LangChain, and RAG
  • Deployment using Streamlit Cloud and Hugging Face Spaces

Course Curriculum

1

Module 1: Streamlit + Python Essentials

  • Introduction to Python: variables, datatypes, control structures
  • Functions, modules, error handling
  • Numpy and Pandas basics for data manipulation
  • Getting started with Streamlit
  • Using widgets, layout, and components
  • Creating interactive dashboards and LLM UI mockups
2

Module 2: Machine Learning Foundations

  • Understanding supervised and unsupervised learning
  • Scikit-learn model building: Linear & Logistic Regression
  • Train-test split and cross-validation
  • Hyperparameter tuning and evaluation metrics
  • Use case: spam detection / customer churn
3

Module 3: Deep Learning with PyTorch

  • Neural network basics: perceptron, multi-layer networks
  • Introduction to PyTorch tensors and autograd
  • Building neural networks using nn.Module
  • Training loop with loss functions and optimizers
  • GPU acceleration and model evaluation
4

Module 4: NLP Fundamentals

  • Text preprocessing: cleaning, stemming, lemmatization
  • Vectorization: Bag of Words, TF-IDF, Word2Vec
  • Sequence models: RNN, LSTM concepts
  • NER and sentiment analysis hands-on
  • Limitations of traditional NLP techniques
5

Module 5: Transformers & Hugging Face

  • Transformer architecture: self-attention, encoder-decoder
  • BERT and GPT-2 overview and use cases
  • Using Hugging Face transformers pipeline
  • Downloading and using pre-trained models
  • Hands-on: sentiment classifier, text summarizer
6

Module 6: LangChain Essentials

  • LangChain architecture: Chains, Agents, Memory
  • Building LLM chains using prompts and tools
  • Creating a conversational bot with LangChain
  • Integration with OpenAI API
  • LangChain toolkits: calculator, Python REPL
  • Prompt engineering patterns and anti-patterns
7

Module 7: RAG with Hugging Face & LangChain

  • Understanding RAG and how it enhances LLMs
  • Vector databases: FAISS, Pinecone basics
  • Document loading and chunking for retrieval
  • Creating context-aware Q&A systems
  • Building a private chatbot using LangChain + RAG
8

Module 8: Deployment & Optimization

  • Using Streamlit Cloud and Hugging Face Spaces for deployment
  • Monitoring LLM behavior and optimization techniques
  • Caching, truncation, rate limits and usage metrics
  • Addressing hallucinations, bias, and safety in LLMs
9

Module 9: Capstone Project

  • Plan and design a complete Generative AI solution
  • Possible projects: Resume screener, PDF-based chatbot, AI tutor
  • Incorporate Streamlit + LangChain + RAG
  • Model evaluation and improvement
  • Presentation and deployment

Frequently Asked Questions

The course duration is 54 hours as per the detailed curriculum.
Yes, the course includes hands-on exercises and a capstone project using Streamlit, LangChain, and RAG.
Basic programming knowledge is helpful, but the course begins with Python essentials.
Yes, deployment using Streamlit Cloud and Hugging Face Spaces is included.

Build Practical Expertise in Generative AI

Gain hands-on training in Python, Machine Learning, Deep Learning, NLP, Transformers, LangChain, and Retrieval-Augmented Generation (RAG). This course includes real-world projects and deployment strategies to prepare you for industry-level Generative AI roles.

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