Generative AI & Large Language Model Engineering using Python

Natural language Processing: Methods, Models & Applications
(In Association with iHUB Divyasampark IIT Roorkee)

About this Course: 

• To make the participants learn Python programming from scratch with a focus on its applications in the fields of Artificial Intelligence (AI) & Generative AI.
• To introduce participants to core AI concepts, including Natural vs Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI fundamentals.
• To provide a strong foundation in Python programming concepts such as objects, functions, methods, error handling, regex, and object-oriented programming with practical illustrations.
• To enable learners to work with Python libraries (OS, Requests, Pandas) for automation, data handling, and API integration (including OpenAI API).
• To introduce participants to Natural Language Processing (NLP) and equip them to build text-processing pipelines including tokenization, sentiment analysis, and custom text classifiers.
• To make learners proficient in working with Large Language Models (LLMs), covering transformer architecture, GPT, BERT, Hugging Face, and LangChain for chatbot development and text generation.
• To equip learners with the knowledge of LangChain framework, LangGraph, and Retrieval Augmented Generation (RAG) for building advanced conversational agents and memory-enabled systems.
• To familiarize learners with Vector Databases (e.g., Pinecone) and their applications in semantic search, recommendation engines, and biomedical research.
• To introduce learners to Speech Recognition and Speech-to-Text systems using traditional ML, Deep Learning, and transformer-based approaches such as Whisper AI.
• To prepare learners for LLM Engineering, including prompt engineering, hosting models vs APIs, cost optimization, scaling strategies, and deploying AI-powered applications with Streamlit.

Course Overview:

Module 1

• Building an AI tool
• Natural vs Artificial Intelligence, Brief history of AI, Weak vs Strong AI
• AI vs Data Science vs Machine Learning vs Deep Learning
• Data: Collection, Labelled vs Unlabeled, Structured vs Unstructured, Metadata
• Overview of Machine Learning (Supervised, Unsupervised and Reinforcement Learning)
• Overview of Deep Learning, Robotics, Computer Vision, Traditional ML, Generative AI
• Generative AI: Introducing ChatGPT, Natural Language, Processing (NLP)
• Large Language Models (LLMs): Training, N-Grams, RNNs, Transformers
• Building LLMs: Prompt Engineering, Fine Tuning, RAG
• Foundation Models vs Private Models
• Inconsistency and Hallucination in Gen AI, Budgeting, Latency, Running out of Data
• AI stack: Python, Working with APIs, Vector Databases, Hugging Face, LangChain

Module 2 

• Basics of Python Language
• Python objects with details of Numbers/Variables/Strings/Lists/Dictionaries/Tuples etc. 
• Variable Assignment, Indexing & Slicing
• Comparison & Logical operators
• Range, List Comprehension, Functions, Lambda expressions.
• Scope, with statement, Working with various Files types, Error Handling
• Regex: Anchors and Groupings, Range Expressions, Non-Greedy Matching, Substitutions.
• Object Oriented Programming, -class, Attributes, Inheritance, Polymorphism

Module 3 

• Understanding Python Module: OS module, Import Techniques and Best Practices
• Requests and HTTPZ Libraries: Getting Started, Handling Errors and Managing Authentication and Headers (Open AI API)
• Introduction and working with Pandas
• Series, DataFrames, working with various data types, Group-By operation
• Selecting a single column, important series methods
• Indexing & Sorting; loc & iloc with series; Inspecting DataFrames, filtering with conditional operators

Module 4 

• Introduction, Supervised vs Unsupervised NLP
• Data preparation, Handling Stop words, Regular Expressions 
• Tokenization, Stemming, Lemmatization, N-grams
• Text tagging, Parts of Speech (POS) tagging, Name Entity Recognition (NER)
• Sentiment Analysis, Rule-based Sentiment Analysis, Pre-trained Transformers
• Numerical Representation of text, Bag of Words, TF-IDF
• Topic Modelling, Latent Dirichlet Allocation (LDA), Latent Semantic Analysis (LSA)
• Custom text classifier using various ML models 

Module 5 

• Understanding LLMs, General Purpose Models, Pre-training and Fine Tuning
• Deep- Learning recap, Transformer Architecture, Input Embeddings
• Multi-headed Attention, Feed-Forward Layer, Masked multihaed Attention
• Understanding GPT, Open AI API, Generating Text, Customizing GPT output
• Key word text summarization, Coding a simple chatbot using LangChain in Python
• Hugging Face, Transformer Pipeline, Pre-trained tokenizers, Special tokens
• Q&A models: BERT architecture, Tokenizer, Embeddings, Calculating response
• Creating QA bot, BERT, RoBERTa, DistilBERT, GPT vs BERT vs XLNET, XLNET Embeddings and Fine Tuning

Module 6

• LangChain Introduction, Tokens and Models, Setting up Environment, Open API key
• System, User and Assistant roles, Creating chatbot, Temperature, Max Tokens, Streaming
• LangChain Framework, ChatOpenAI, System, AI and Human messages
• Prompt Templates and Prompt Values, Few-shot chat message prompt templates
• String output parser, Comma-separated list output parser, Datetime output parser
• Piping a prompt, model and parser, Batching, Streaming, Runnable Sequence class
• Piping chains and Runnable Passthrough, Runnable Parallel, Runnable Lambda
• Retrieval Augmented Generation (RAG): Document Loading, Splitting and Embedding
• Document storing, retrieval and generation, loading with PyPDFLoader, Docx2txtLoader
• Splitting with character text splitter, markdown header text splitter
• QA bot, BERT, RoBERTa, DistilBERT, GPT vs BERT vs XLNET, XLNET Embeddings and Fine Tuning
• Text embedding with OpenAI, Chroma vectorstore (Inspecting and Managing docs)
• Retrieval: Similarity search, Maximal Marginal Relevance (MMR) search, Vectorstore-backed retriever
• Generation: Stuffing documents and Generating a response 

Module 7

• States, nodes and Edges, First graph: Importing relevant classes, Building graph
• Conditional edges: Defining nodes, routing function, Building the graph
• Annotated construct and reducer functions, MessagesState, RemoveMessages
• Checkpointers and threads, Short-term memory with the InMemorySaver class
• The StateSnapshot class, Long-term memory with SQLite  

Module 8 

• Database comparison: SQL, NoSQL and Vector, Understanding Vector databases,
• Vector space: Introduction, Distance Metrics, Vector Embeddings
• Vector database, comparison, Pinecone registration, walkthrough and creating index
• Pinecone with Python: Connection, Pinecone Index, Upsetting data and using embedding
• Vector database for Recommendation Engines, Biomedical research, Semantic search

Module 9

• Development and Evolution, Formants, Harmonics and Phonemes
• Sound and Sound waves: Fundamentals and properties
• Sample Rate, bit depth, bit rate, Audio signal processing for Machine Learning and AI
• Audio Features: Time-domain, Frequency-domain, time-frequency-domain, Fourier transform
• Acoustic and language modeling, Hidden Markov Models (HMMs)
• Traditional Neural Networks: CNNs, RNNs and LSTMs
• Advanced speech recognition systems: Transformers
• Building a Speech Recognition Model
• Audio file formats for speech recognition, Importing audio files in python
• Google Web Speech API, Evaluation metrics: WER and CER
• Dealing with background, Noise Creating spectrogram
• Whisper AI: Transformer-based speech-to-text, Transcribing multiple audio files
• Reversing the process: AI-powered text-to-speech

Module 10

• Hosting an LLM vs Using an API, Open-Source vs Closed-source, Tokens
• Pricing: Hosting an LLM vs Pay-by-Token, Initial Prompt Development
• Database Design and Schema Development, Activity Diagram
• OpenAI Playground, Optimizing Temperature, Top P for Different Use Cases
• Prompt Engineering for Software Lifecycle
• Streamlit: Introduction, Pros and Cons, Text Methods, Chat Elements, Session State
• Initializing an OpenAI Client, Implementing the Chat Functionality, Building the Setup Page
• Enhancing Chatbot Interaction with Session State, Feedback Functionality, Deployment
• Application Structure, Prompt Structure, Hallucinations, Prompt Injection
• Counting Tokens, Cost reduction and Scaling

Prerequisites and eligibility:

• No coding experience in any programming language required. We’ll start from scratch.
• This course can be taken up by any undergraduate/postgraduate student of Basic & Applied Sciences, Engineering, Management and Computer Applications and also by Research Scholars/Faculties/Working Professionals who want to upskill themselves.
• Participants need to have a laptop/PC (with a minimum of 4 GB RAM, 100 GB HDD, Intel i3 processor) and proper internet/Wi-Fi connection.

Contact Person: Dr. Subrat Kotoky

Email: [email protected] / [email protected]

Phone: 9085317465 / 8473874389

Expert Profile:  Mr. Shreyas Shukla

Professional Corporate Trainer & Microsoft Azure Certified Data Engineer

 M.Tech-IIT Kharagpur & BE- The Aeronautical Society of India, New Delhi

 (Total Experience in conducting Professional Courses: 4+ Years)

  Certifications:

• DP-203: Microsoft Certified: Azure Data Engineer Associate

• DP-900: Microsoft Certified: Azure Data Fundamentals

• AZ-900: Microsoft Certified: Azure Fundamentals

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