Large Language Model(LLM)

Data Science in Tamil > Basic > Large Language Model(LLM)
llm
Why Large Language Model is Important
in Data Science and AI

LLMs power today’s most advanced AI systems, including chatbots, copilots, search engines, and creative tools.

They understand and generate human-like text, enabling applications in customer support, content creation, summarization, and more.

  • LLMs like GPT, LLaMA, Claude, and Gemini perform zero-shot and few-shot learning, allowing flexible use without retraining.

  • They support natural language interfaces to databases, APIs, and applications—transforming how users interact with software.

  • LLMs have revolutionized data preprocessing, analysis, and automation across data science workflows.

  • They are foundational to generative AI, used in text-to-image, text-to-code, and multimodal applications.

  • LLMs can be fine-tuned or prompted to specialize in domain-specific tasks such as legal advice, education, or programming.

  • Their underlying architecture—Transformers—is now a standard in NLP and even in computer vision and audio processing.

  • Open-source LLMs are fueling innovation in enterprise and research, making cutting-edge AI accessible and modifiable.

  • Mastering LLMs empowers learners to build, evaluate, and deploy modern AI applications responsibly and efficiently.

llm
01

Module 1: Introduction to LLMs

  1. What are Large Language Models?

  2. LLMs vs traditional NLP pipelines

  3. Real-world applications of LLMs

  4. Evolution: BERT → GPT → LLaMA → GPT-4 / Claude / Gemini

  5. Generative AI and LLMs

02

Module 2: NLP Foundations for LLMs

  1. Text preprocessing and tokenization

  2. Embeddings and vector space models

  3. Language modeling: N-grams vs neural models

  4. Attention mechanism: motivation and benefits

03

Module 3: Transformer Architecture

  1. Self-attention and multi-head attention

  2. Encoder-decoder vs decoder-only models

  3. Positional encoding

  4. Layer normalization, residual connections

  5. Pre-training and fine-tuning process

04

Module 4: Types of LLMs

  1. Autoregressive Models: GPT, Mistral, LLaMA

  2. Masked Language Models: BERT, RoBERTa

  3. Instruction-Tuned Models: ChatGPT, Claude

  4. Multimodal Models: Gemini, GPT-4o

  5. Open-Source Models: Falcon, Mistral, LLaMA-3

05

Module 5: Tokenization and Embedding

  1. Byte Pair Encoding (BPE), SentencePiece

  2. Vocabulary size and trade-offs

  3. Embedding layers in LLMs

  4. Role of context length

06

Module 6: Training and Scaling Laws

  1. Pre-training objective (causal, masked, contrastive)

  2. Datasets used for pretraining (Common Crawl, Wikipedia, etc.)

  3. Scaling laws for compute, data, and model size

  4. Training infrastructure and parallelism

07

Module 7: Prompt Engineering

  1. What is prompting?

  2. Zero-shot, one-shot, and few-shot examples

  3. Chain-of-thought (CoT) prompting

  4. Role prompting, system prompts

  5. Prompt injection and security risks

  6. Tools: LangChain, Guidance, PromptFlow

08

Module 8: Fine-Tuning and Adaptation

  1. Full fine-tuning vs parameter-efficient tuning (LoRA, QLoRA, PEFT)

  2. Instruction tuning and supervised fine-tuning (SFT)

  3. Reinforcement Learning with Human Feedback (RLHF)

  4. Dataset design for custom LLMs

  5. Evaluation and safety tuning

09

Module 9: Inference and Deployment

  1. GPU/TPU requirements and inference strategies

  2. Quantization and model compression (8-bit, 4-bit)

  3. Using LLMs via API (OpenAI, Hugging Face, Cohere)

  4. Local deployment of open-source models

  5. Streaming and low-latency response tuning

10

Module 10: Evaluation of LLMs

  1. Accuracy, coherence, hallucination rate

  2. BLEU, ROUGE, perplexity, BERTScore

  3. Human evaluation methods

  4. Benchmarks: HELM, MMLU, TruthfulQA

11

Module 11: Building Applications with LLMs

  1. Text summarization, rewriting, translation

  2. Code generation and explanation (Code LLMs)

  3. Semantic search with vector databases (FAISS, Chroma)

  4. Chatbots and virtual assistants

  5. Agent frameworks: LangChain Agents, AutoGPT, CrewAI

12

Module 12: Tools & Ecosystem

  1. Hugging Face Transformers and Datasets

  2. OpenAI Assistants API

  3. LangChain, LlamaIndex, Vector DBs

  4. Gradio, Streamlit, FastAPI

  5. MLflow and Weights & Biases for tracking

13

Module 13: Multimodal and Vision-Language LLMs

  1. Introduction to multimodal transformers

  2. Image captioning (BLIP, Flamingo)

  3. Text-to-image (DALL·E, Stable Diffusion)

  4. Vision-Language models (GPT-4o, Gemini)

  5. Speech and audio with Whisper, Bark

14

Module 14: Ethical, Legal, and Social Implications

  1. Bias and fairness in LLMs

  2. Hallucinations and misinformation

  3. Copyright and licensing issues

  4. Open-weight vs closed-weight models

  5. Regulatory frameworks (EU AI Act, NIST)

15

Module 15: Capstone Projects and Case Studies

  1. Build a custom Q&A chatbot using OpenAI API

  2. Deploy a local open-source LLM using Hugging Face

  3. Fine-tune a model using PEFT on a domain-specific dataset

  4. Integrate LLM with search using RAG (Retrieval Augmented Generation)

  5. Build a generative agent with memory and context

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