Build the Future with Generative AI – From Fundamentals to Real-World LLM Apps

Build the Future with Generative AI – From Fundamentals to Real-World LLM Apps

 Course Overview

Master the future of technology with this hands-on course in Generative AI, Machine Learning, and Natural Language Processing (NLP). Learn how to build intelligent systems powered by Large Language Models (LLMs) like GPT, use cutting-edge tools like LangChain, Hugging Face, and OpenAI, and apply AI to real-world tasks like document search, content creation, chatbots, and more.

Designed for students, developers, tech professionals, and AI enthusiasts — no advanced math required!

 Learning Outcomes

By the end of this course, you’ll be able to:

• Understand the foundations of AI, ML, and Deep Learning
• Explain and apply Generative AI vs. traditional ML techniques
• Build NLP-powered apps using LLMs like GPT-4, BERT, and others
• Use frameworks like LangChain, Hugging Face, and TensorFlow
• Develop RAG (Retrieval-Augmented Generation) search systems
• Address key AI ethics: bias, privacy, and misinformation

Key Benefits

• Learn the most in-demand AI skills of today and tomorrow
• Build real-world apps like chatbots, AI writers, and AI search engines
• Hands-on with leading tools: OpenAI, LangChain, ChromaDB, PyTorch
• Boost your resume with AI & NLP project experience
• Stay competitive in fields like data science, software development, and UX

Module 1: Introduction to AI, ML & Generative AI

Objectives: Discover the basics of AI, ML, and Generative AI with real-world tools like ChatGPT and Copilot. Learn how LLMs work and why ethics matter in AI.

Outline

• What is AI, Machine Learning & Deep Learning?
• Traditional vs Generative AI
• Use cases of Generative AI (ChatGPT, Midjourney, GitHub Copilot)
• Overview of LLMs (Large Language Models)
• Ethics in AI (bias, hallucination, privacy, regulation)

 Outcomes: Build a strong foundation in modern AI, understand its real-life impact, and get ready to explore powerful generative tools confidently.

Module 2: Python for Machine Learning

Objectives: Learn Python tools like NumPy, Pandas, and Matplotlib for ML, plus data cleaning and EDA basics.

Outline

• Python basics for ML (NumPy, Pandas, Matplotlib)
• Data preprocessing techniques
• Exploratory Data Analysis (EDA)
• Introduction to Scikit-learn

 Outcomes: Gain practical skills to process, visualize, and analyze data—preparing you to build ML models with confidence.

Module 3: Fundamentals of Machine Learning

Objectives: Master Supervised vs Unsupervised Learning, including Regression, Classification, and Clustering, with model evaluation using Accuracy, F1 Score, and Cross-Validation.

 Outline

• Supervised vs Unsupervised Learning
• Regression, Classification, Clustering
• Evaluation Metrics (Accuracy, Precision, Recall, F1, ROC-AUC)
• Model tuning and cross-validation

Outcomes: Build, evaluate, and fine-tune ML models to solve real-world, data-driven problems with confidence.

Module 4: Deep Learning with TensorFlow & PyTorch

Objectives: Explore Deep Learning with TensorFlow and PyTorch, covering Neural Networks, CNNs, RNNs, and LSTMs.

 Outline

• Neural Networks basics
• Building and training models in TensorFlow
• PyTorch basics: Tensors, autograd, model building
• CNNs, RNNs, LSTMs overview

Outcomes: Build and train deep learning models using TensorFlow and PyTorch for real-world AI projects.

Module 5: Natural Language Processing (NLP) Essentials

Objectives: Master key NLP techniques such as tokenization, vectorization, and text classification. Explore tools for sentiment analysis, NER, and POS tagging using real-world data to unlock NLP’s power.

 Outline

• Text preprocessing (tokenization, stemming, lemmatization)
• Vectorization (TF-IDF, Word2Vec, GloVe)
• Sentiment analysis, text classification
• Named Entity Recognition (NER), POS tagging

Outcomes: By the end of this module, you’ll be able to process and analyze human language with machine learning, building NLP applications like chatbots and sentiment analyzers.

Module 6: Large Language Models (LLMs)

Objectives: Learn how Transformers, BERT, and GPT work. Explore model fine-tuning using Hugging Face tools.

 Outline

• What are Transformers? (Attention mechanism, self-attention)
• BERT vs GPT architectures
• Fine-tuning LLMs with Hugging Face Transformers
• Hands-on: Fine-tune a BERT or GPT model on custom data

Outcomes: Gain hands-on skills to fine-tune LLMs for custom tasks and build smart, AI-driven language applications.

Module 7: Working with Hugging Face

Objectives: Master Hugging Face tools to accelerate NLP development—learn pre-trained models, tokenizers, datasets, pipelines, and model deployment in one powerful module.

 Outline

• Overview of Hugging Face ecosystem
• Using pre-trained models with transformers
• Datasets, Tokenizers, Pipelines
• Model deployment on Hugging Face Hub

Outcomes: Build and deploy real-world NLP apps with ease. By the end, you’ll confidently use Hugging Face to create and share production-ready AI models.

Module 8: LangChain & Generative AI Applications

Objectives: Build smart GenAI apps with LangChain, vector databases, RAG, and integrations like OpenAI & Hugging Face.

 Outline

• What is LangChain?
• Building LLM-powered apps with LangChain
• Connecting to vector databases (e.g., FAISS, ChromaDB)
• Retrieval-Augmented Generation (RAG)
• LangChain + OpenAI + Hugging Face integration

Outcomes: Create and deploy real-world LLM apps that are fast, flexible, and production-ready.

Module 9: Generative AI Use Cases & Projects

Objectives: Learn to build real-world GenAI projects—chatbots with GPT-4, text summarizers, Q&A systems, and auto content pipelines. Get a quick dive into AI art with Midjourney.

 Outline

• AI Chatbot with GPT-3.5/4 using LangChain
• Text summarization and Q&A system
• AI art generation with Midjourney (overview only)
• Auto-content generation pipeline (blogs, code, etc.)

Outcomes: Finish with a portfolio of practical GenAI apps, ready to deploy, showcase, or scale for real-world impact.

Module 10: Capstone Project

Objectives: Create a domain-specific AI chatbot with LangChain & Hugging Face, fine-tune an LLM for custom applications like e-commerce, and build an AI-powered document search system using Retrieval-Augmented Generation (RAG).

 Outline

• Build a domain-specific AI chatbot using LangChain & Hugging Face
• Fine-tune an LLM for custom use (e.g., e-commerce)
• AI-powered document search using RAG

Outcomes: By the end, you’ll have a fully-functional AI chatbot, a fine-tuned LLM, and an advanced document search system—perfect for showcasing your skills and completing your AI journey.

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