Master AI,
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Build real understanding across 598 structured levels and 13 end-to-end modules from the foundations of intelligence to modern generative AI, agents and responsible adoption.
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“Can a human judge tell a machine's writing from a person's?”
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Every level combines explanation, recall, application and checkpoints so concepts are understood before the next layer unlocks.
Artificial Intelligence (AI)
When people say a machine is intelligent, they usually mean it can do something we associate with human thinking: recognize a face, translate a sentence or recommend a song you like. AI is the umbrella field that studies how to build such systems.
One path. Every step builds on the last.
Learn freely
Sign in with Google and start at level one every module and level is free.
Complete structured levels
Work through concept lessons and quizzes, unlocked in sequence as you go.
Pass module checkpoints
Each module closes with a checkpoint that confirms what you've actually retained.
Complete all 13 modules
598 levels across every module, from foundations to responsible adoption.
Claim optional verified proof
Finish the program and claim a publicly verifiable certificate for ₹49.
13 modules, 598 levels, one sequence.
Start with fundamentals, move through modern generative AI and finish with agents, safety and responsible adoption 46 levels in every module.
Foundations of Intelligence
Understand what intelligence, reasoning and machine learning actually mean and how the field of AI took shape.
Key concepts
- What is artificial intelligence
- Narrow AI vs general AI vs superintelligence
- A brief history of AI
- The Turing Test
- Symbolic AI vs machine learning
Sample level titles
- Symbolic AI vs Machine Learning
- Why AI Is Booming Now
- AI vs ML vs DL vs Data Science
- The AI Winters
- Where AI Already Lives in Daily Life
01 Foundations of Intelligence
Foundations of Intelligence
Understand what intelligence, reasoning and machine learning actually mean and how the field of AI took shape.
Key concepts
- What is artificial intelligence
- Narrow AI vs general AI vs superintelligence
- A brief history of AI
- The Turing Test
- Symbolic AI vs machine learning
Sample level titles
- Symbolic AI vs Machine Learning
- Why AI Is Booming Now
- AI vs ML vs DL vs Data Science
- The AI Winters
- Where AI Already Lives in Daily Life
02 Data & Machine Learning
Data & Machine Learning
Learn how machines learn from data features, training, evaluation and the difference between learning paradigms.
Key concepts
- What is data to a machine
- Features and labels
- Supervised learning
- Unsupervised learning
- Reinforcement learning: a first look
Sample level titles
- Reinforcement Learning: A First Look
- Training, Validation and Test Sets
- Overfitting and Underfitting
- Loss Functions
- Evaluation Metrics: Accuracy, Precision, Recall
03 Neural Networks
Neural Networks
See how neurons, weights and gradients combine into a network that can learn from example.
Key concepts
- The biological inspiration: neurons
- The perceptron
- Weights and bias
- Activation functions
- Layers: input, hidden and output
Sample level titles
- Layers: Input, Hidden and Output
- Forward Propagation
- Backpropagation
- Gradient Descent and Learning Rate
- Epochs, Batches and Iterations
04 Deep Learning Architectures
Deep Learning Architectures
Understand the architectures CNNs, RNNs, attention and transformers that power modern AI systems.
Key concepts
- What makes learning ‘deep’
- Convolutional neural networks (CNNs)
- Recurrent neural networks (RNNs)
- The long-term memory problem (LSTM)
- The attention mechanism
Sample level titles
- The Attention Mechanism
- The Transformer Architecture
- Embeddings: Turning Meaning Into Numbers
- Tokenization
- Transfer Learning and Fine-Tuning
05 Language & NLP
Language & NLP
Learn how machines process, represent and generate human language, from n-grams to embeddings.
Key concepts
- What is natural language processing
- Language as prediction
- N-grams: the old way of predicting text
- Word embeddings and Word2Vec
- Sentiment analysis
Sample level titles
- Sentiment Analysis
- Named Entity Recognition
- Machine Translation
- Speech Recognition and Synthesis
- Why Language Is Hard for Machines
06 Large Language Models
Large Language Models
Understand how large language models are trained, tuned and steered and where they go wrong.
Key concepts
- What makes a language model ‘large’
- Pretraining: learning from the internet
- Fine-tuning and instruction tuning
- RLHF: learning from human feedback
- The context window
Sample level titles
- The Context Window
- Tokens, Temperature and Sampling
- Hallucination: Why Models Make Things Up
- Emergent Abilities in Large Models
- Multimodal Models
07 Generative AI
Generative AI
Explore how models generate images, text and media and the creative and legal questions that follow.
Key concepts
- What is generative AI
- Generative adversarial networks (GANs)
- Diffusion models
- Autoregressive text generation
- Text-to-image generation
Sample level titles
- Text-to-Image Generation
- Latent Space
- Creativity vs Memorization
- Deepfakes
- Copyright and Generative AI
08 Computer Vision
Computer Vision
Learn how machines perceive images, from raw pixels to detection, segmentation and recognition.
Key concepts
- How a machine sees an image
- Pooling and downsampling
- Image classification
- Object detection
- Image segmentation
Sample level titles
- Image Segmentation
- Facial Recognition
- Optical Character Recognition (OCR)
- Self-Driving Car Perception
- Medical Imaging AI
09 Reinforcement Learning
Reinforcement Learning
Understand how agents learn through reward, from Q-learning to RLHF.
Key concepts
- Agent, environment and reward
- The policy
- Exploration vs exploitation
- Q-learning
- Reward shaping
Sample level titles
- Reward Shaping
- Reinforcement Learning in Games: AlphaGo
- Reinforcement Learning in Robotics
- Policy Optimization in RLHF
- The Credit Assignment Problem
10 AI Agents & Tools
AI Agents & Tools
Learn how AI systems plan, use tools and act autonomously toward a goal.
Key concepts
- What is an AI agent
- Tool use and function calling
- Assistant vs agent
- Multi-agent systems
- Memory in AI agents
Sample level titles
- Memory in AI Agents
- Planning and Task Decomposition
- Autonomous Agents: Promise and Risk
- Retrieval-Augmented Generation (RAG)
- Orchestration: Chaining AI Steps Together
11 Prompt Engineering
Prompt Engineering
Master the skill of instructing AI systems clearly, specifically and iteratively.
Key concepts
- What is a prompt
- Zero-shot vs few-shot prompting
- Chain-of-thought prompting
- System prompts vs user prompts
- Prompt injection
Sample level titles
- Prompt Injection
- Being Specific: The Core Skill
- Giving the Model a Role and Format
- Common Prompting Mistakes
- Iterating Like a Scientist
12 AI Ethics, Safety & Bias
AI Ethics, Safety & Bias
Understand where bias, opacity and misuse enter AI systems and how to build responsibly.
Key concepts
- Where bias in AI comes from
- Fairness in machine learning
- The black box problem
- Explainable AI (XAI)
- The alignment problem
Sample level titles
- The Alignment Problem
- Privacy and Surveillance
- AI and Jobs
- Misinformation and Manipulation
- Responsible AI Principles
13 AI in Society & Future
AI in Society & Future
See where AI is already reshaping healthcare, finance, education and work and what comes next.
Key concepts
- AI in healthcare
- AI in finance
- AI in education
- AI regulation basics
- Careers in AI: tech and non-tech
Sample level titles
- Careers in AI: Tech and Non-Tech
- AI Literacy for Leaders
- Human-AI Collaboration
- Realistic Expectations About AGI
- How to Keep Learning About AI
Move beyond AI terminology into working understanding.
Understand
Build accurate mental models of machine learning, neural networks, LLMs, generative AI and agents.
Apply
Recognise where AI creates value, where it fails and how to use it effectively.
Build
Understand prompts, retrieval, tools, evaluation and the architecture behind modern AI products.
Lead
Make better technical, product and organisational decisions around AI adoption.
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Learning is completely free. The certificate is optional and costs ₹49, available once all 13 modules are complete.
Built for people who need more than AI hype.
Students
Build foundations before tools and trends.
Professionals
Use AI more effectively in real workplace contexts.
Developers and builders
Understand the complete modern AI product stack.
Leaders
Evaluate opportunities, risks and adoption decisions.
598 levels. One clear path.
No credit card. No trial deadline. Learn the entire program free and claim the optional ₹49 certificate only after completing all 13 modules.
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