Artificial Intelligence
0 To 1 LLM
Enter course description
25 hours
10 Modules
Beginner
₹1,50,000₹2,50,00040% OFF
Course Curriculum
01
1. Introducion
15 Lessons
1.1 Definition of AI: An Overview
1.2 What is Machine Learning
1.3 What is Deep Learning?
1.4 What Is NLP?
1.5 What are LLMs?
1.6 Real-World AI Applications: An Introduction
1.7 Introduction to AI & LLMs: Difference Between AI, ML, DL, and LLMs
1.8 Introduction to AI & LLMs: Common LLM Examples (GPT, LLaMA, etc.)
1.9 Generative AI vs Classical AI
1.10 Language model use cases
1.11 Text generation basics
1.12 Understanding Chatbots and Assistants in the AI Landscape
1.13 Risks and benefits of AI
1.14 AI in industry today
1.15 How LLMs Impact Jobs & the Future
02
2. Python Foundations for AI
15 Lessons
2.1 Variables & Data Types: A Foundation for AI
2.2 Lists, Tuples, Dictionaries: Essential Data Structures for Python and AI
2.3 Loops & Iterations: A Comprehensive Guide for Python Foundations in AI
2.4 Functions & Parameters
2.5 Conditional Statements
2.6 Input/Output operations
2.7 Error handling
2.8 File reading/writing
2.9 Modules & Packages
2.10 Virtual environments
2.11 Working in Jupyter Notebook
2.12 Python libraries install pip
2.13 Using notebooks efficiently
2.14 Basic scripts for automation
2.15 Project structure basics
03
3. Math & ML Fundamentals
15 Lessons
3.1 Linear algebra basics
3.2 Matrices & vectors
3.3 Probability basics
3.4 Statistics essentials
3.5 Data distribution
3.6 Mean/median/mode
3.7 Gradients & derivatives
3.8 Loss functions what/why
3.9 Overfitting vs underfitting
3.8 Evaluation methods
3.9 Train/Test split
3.10 ML pipeline overview
3.11 Bias/variance concept
3.12 Feature engineering idea
3.13 Intro to optimization
04
4. Data Handling & Preprocessing
15 Lessons
4.1 Using NumPy arrays
4.2 Basic Pandas dataframe usage
4.3 CSV/JSON reading
4.4 Missing value handling
4.4 Text cleaning basics
4.5 Removing stopwords
4.6 Lowercasing text
4.7 Punctuation removal
4.8 Tokenization intro
4.9 Lemmatization
4.10 Stemming
4.11 Word frequency count
4.12 Simple EDA
4.13 Visualization basics
4.14 Preparing data for NLP
05
5. NLP Foundations
15 Lessons
5.1 What is NLP
5.2 Bag-of-Words model
5.3 TF-IDF basics
5.4 N-grams understanding
5.5 POS tagging
5.6 Named Entity Recognition
5.7 Sentence segmentation
5.8 Language modeling intro
5.9 Perplexity basics
5.10 Text similarity concept
5.11 Stopwords usage
5.12 Feature extraction idea
5.13 Introduction to corpora
5.14 Basic sentiment analysis
5.15 Intro classification task
06
6. Embeddings
15 Lessons
6.1 What are embeddings
6.2 Word2Vec concept
6.3 GloVe concept
6.4 Vector meaning
6.5 Vector space visual
6.6 Cosine similarity
6.7 High-dimensional representation
6.8 Context-based embeddings
6.9 Static vs dynamic embeddings
6.10 Visualization with PCA
6.11 Similarity search
6.12 Sentence embeddings
6.13 Token embeddings
6.14 Embedding evaluation
6.15 Embedding in LLMs
07
7. Transformer Basics
15 Lessons
7.1 From RNN → LSTM → Transformer
7.2 Why transformers dominate
7.3 Attention overview
7.4 Encoder & Decoder idea
7.5 Position encoding intro
7.6 Multi-head attention concept
7.7 Feed-forward layers intro
7.8 Token representation flow
7.9 Residual connections meaning
7.10 Layer normalization
7.11 Masking basics
7.12 Transformer use in LLMs
7.13 Basic architecture diagram
7.14 Intuition building
7.15 Paper reference "Attention is All You Need"
08
8. Working with Hugging Face
15 Lessons
8.1 Installing transformers library
8.2 Using pipeline API
8.3 Loading pre-trained models
8.4 GPT2 text generation
8.5 Tokenizer basics
8.6 Setting parameters (max_length etc.)
8.7 Saving outputs
8.8 Using models offline
8.9 Downloading datasets
8.10 Model card reading
8.11 Using auto classes
8.12 Text classification pipeline
8.13 Summarization pipeline
8.14 Translation pipeline
8.15 Hands-on mini coding
09
9. Prompting for Beginners
15 Lessons
9.1 What is prompting
9.2 Zero-shot prompting
9.3 Simple instruction prompts
9.4 Continuation prompts
9.5 Creative writing prompt
9.6 Q&A prompt examples
9.7 Limitation understanding
9.8 Prompt templates
9.9 Formatting prompts
9.10 Using context
9.11 Avoid ambiguous prompts
9.12 Getting consistent outputs
9.13 Chain prompts
9.14 Role-based prompting
9.15 Beginner mini project
10
10. Project & Practice
15 Lessons
10.1 Build simple chatbot
10.2 Text summarization small app
10.3 Sentiment classifier toy project
10.4 Write prompt experiments
10.5 Compare models outputs
10.6 Tune basic generation settings
10.7 Build UI using Gradio
10.8 Implement text cleaning pipeline
10.9 Evaluate generated results
10.10 Build script text generator
10.11 Explore datasets on HF
10.12 Document learnings
10.13 Demo presentation
10.14 GitHub upload
10.15 Beginner milestone achieved
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