GENERATIVE AI COURSE

Day 01 introduces Artificial Intelligence (AI), Machine Learning (ML), and Generative AI, explaining their differences, relationships, and real-world applications. Learners understand how AI systems mimic human intelligence, how ML enables learning from data, and how Generative AI creates new content like text, images, audio, and code.

Day 02 explores the history of Artificial Intelligence, covering its origins, major milestones, AI winters, the rise of machine learning, deep learning breakthroughs, and the emergence of Generative AI. Learners understand how AI evolved from rule-based systems to modern intelligent models.

Day 03 introduces major generative models including GANs, VAEs, and Transformers. Learners understand how these models generate new data such as images, text, and audio using neural networks, probability distributions, and attention mechanisms, forming the foundation of modern Generative AI systems.

Day 04 explores real-world applications of Generative AI across industries such as healthcare, marketing, finance, education, entertainment, and software development. Learners understand how generative models create text, images, code, audio, and video to automate tasks, enhance creativity, and improve business efficiency.

Day 05 introduces Python basics essential for AI and Generative AI development. Learners understand variables, data types, operators, conditional statements, loops, functions, and basic data structures, building a strong programming foundation required for machine learning and generative model implementation.

Day 06 introduces Neural Networks, the foundation of deep learning and Generative AI. Learners understand neurons, layers, activation functions, forward propagation, backpropagation, and how neural networks learn patterns from data to perform tasks like classification, regression, and content generation.

Day 07 focuses on Backpropagation, the core algorithm used to train neural networks. Learners understand how errors are calculated, how gradients are computed using the chain rule, and how weights are updated through gradient descent to minimize loss and improve model accuracy.

Day 08 introduces Convolutional Neural Networks (CNNs), specialized deep learning models designed for image and visual data processing. Learners understand convolution layers, filters, pooling, feature extraction, and how CNNs automatically detect patterns like edges, shapes, and objects in images.

Day 09 introduces Recurrent Neural Networks (RNNs), deep learning models designed for sequential data processing. Learners understand how RNNs use hidden states and memory to process time-series, text, and speech data, making them suitable for language modeling, translation, and sequence prediction tasks.

Day 10 introduces TensorFlow and PyTorch, two leading deep learning frameworks used to build, train, and deploy neural networks. Learners understand their architecture, key features, differences, and how they support model development for AI, deep learning, and generative AI applications.

Day 11 introduces Tokenization, the process of converting raw text into smaller units called tokens. Learners understand word-level, character-level, and subword tokenization methods, their importance in NLP and Generative AI, and how tokenization prepares text data for model training and language understanding.

Day 12 introduces the Attention Mechanism, a powerful concept in deep learning that allows models to focus on important parts of input data. Learners understand how attention improves sequence modeling, enhances context understanding, and forms the foundation of modern Transformer-based Generative AI systems.

Day 13 introduces Transformers, a revolutionary deep learning architecture that powers modern Generative AI systems. Unlike RNNs and CNNs, Transformers rely entirely on attention mechanisms to process sequences efficiently. Learners explore encoder-decoder architecture, self-attention, multi-head attention, positional encoding, and why Transformers dominate NLP and large-scale AI models.

Day 14 explains the key differences between BERT and GPT, two powerful Transformer-based language models. Learners understand bidirectional vs autoregressive training, encoder vs decoder architecture, use cases, strengths, and limitations. This session clarifies how BERT excels at understanding tasks while GPT specializes in text generation and conversational AI systems.

Day 15 introduces Hugging Face, a leading open-source platform for Natural Language Processing and Generative AI. Learners explore pretrained models, Transformers library, datasets, tokenizers, and model deployment. This session helps students understand how to download, fine-tune, and use state-of-the-art AI models efficiently for real-world applications.

Day 16 focuses on GPT-based text generation, explaining how autoregressive language models generate human-like text. Learners explore next-word prediction, temperature, top-k and top-p sampling, prompt design, and real-world applications. This session builds practical understanding of how GPT models generate essays, stories, code, and conversational responses.

Day 17 introduces Prompt Engineering, the skill of designing effective inputs to guide AI models toward accurate and high-quality outputs. Learners explore prompt structure, zero-shot and few-shot prompting, role prompting, chain-of-thought reasoning, and best practices to improve responses from large language models.

Day 18 introduces Diffusion Models, a powerful generative technique that creates high-quality images and data by learning to reverse a gradual noise process. Learners explore forward and reverse diffusion, denoising training, mathematical intuition, and real-world applications in modern image generation systems.

Day 19 introduces Generative Adversarial Networks (GANs), a powerful generative model where two neural networks compete to produce realistic data. Learners explore the generator–discriminator framework, adversarial training, loss functions, challenges like mode collapse, and real-world applications in image synthesis and data generation.

Day 20 focuses on a hands-on mini project where learners build a small Generative AI application using pretrained models. Students apply concepts like prompt engineering, text generation, or image generation to create a working prototype, strengthening practical understanding and real-world implementation skills.

Day 21 focuses on fine-tuning Large Language Models (LLMs) to improve performance on domain-specific tasks. Learners explore supervised fine-tuning, instruction tuning, parameter-efficient methods like LoRA, dataset preparation, and evaluation techniques used to adapt powerful pretrained models for real-world applications.

Day 22 introduces LangChain, a framework for building applications powered by Large Language Models. Learners explore chains, agents, memory, prompt templates, and integrations with external tools, enabling the creation of intelligent AI applications like chatbots, question-answering systems, and document-based assistants.

Day 23 introduces Retrieval-Augmented Generation (RAG), a technique that enhances Large Language Models by retrieving relevant external information before generating responses. Learners understand embeddings, document retrieval, vector search, and how RAG reduces hallucinations while improving factual accuracy in AI systems.

Day 24 introduces Vector Databases, specialized systems designed to store and search embeddings efficiently. Learners explore similarity search, cosine similarity, indexing methods, and how vector databases power modern AI systems like RAG and semantic search applications.

Day 25 covers Chatbot development using Large Language Models. Learners explore conversational design, memory handling, context management, RAG integration, and deployment strategies to build intelligent, context-aware AI chat systems for real-world applications.

Day 26 introduces FastAPI, a modern Python framework for building high-performance APIs. Learners explore REST API creation, request handling, model serving, integration with machine learning models, and deployment strategies for scalable AI applications.

Day 27 covers Cloud Deployment for AI applications. Learners understand how to deploy FastAPI or ML models to cloud platforms like AWS, Google Cloud, and Azure. The session explains cloud basics, hosting options, Docker deployment, scalability, monitoring, and security practices required to move AI applications from local development to production environments.

Day 29 focuses on designing interactive and user-friendly interfaces using Stream lit. Learners will build clean dashboards, add input widgets, display charts, integrate ML predictions, and improve layout styling to create professional AI web applications without using HTML, CSS, or JavaScript.

Day 30 focuses on building and deploying a complete end-to-end AI or Generative AI project. Learners will integrate data processing, model training, evaluation, UI development (Streamlit/FastAPI), and cloud deployment into a production-ready application that demonstrates real-world problem-solving skills.