INTRO TO GENERATIVE AI · END-SEMESTER

Your complete GenAI exam study companion

Every topic — from Machine Learning basics to AI Agents — explained from first principles with worked examples, runnable code, exam tips and 200+ practice questions with solutions. Study only from here and walk into the exam confident.

Start with the first topic → Try the Mock Exam

Study Topics

Syllabus Topics

200+

Practice Questions

Mock Exam

How to use this site

The exam is MCQ + coding. This site is built so that reading it cover-to-cover is enough to score top marks. Here is the most effective way to revise:

📖

1. Work through the topics

The topics are ordered to build on each other, but each one is self-contained — jump straight to any concept you need. Every page opens with an "On this page" map and explains the concept with intuition, examples and the exact terms examiners use.

💻

2. Study the code blocks and their output

Coding questions reuse these exact patterns. Every code block shows what it prints — learn to predict the output before you read it.

✅

3. Attempt every practice question

Each topic ends with MCQs and coding questions. Pick your answer first, then press Check Answer — the solution explains the reasoning, not just the result.

⏱️

4. Finish with the Mock Exam

The full mock exam mixes every topic, auto-grades your MCQs and reveals every coding solution. Aim for 85%+ before exam day.

💡 Exam-day strategy For MCQs, read every option before choosing — examiners love "all of the above" and near-miss distractors. For coding questions, write the import lines first, then the logic; partial code still earns partial marks. Manage time: roughly 1 minute per MCQ, leave the longest coding question for last.

Exam Toolkit

Four focused study aids built on top of the topics — use them in the final days of revision.

≡

Cheat Sheet

Every formula, key fact and decision rule for every topic, condensed onto one print-friendly page.

▦

Interactive Flashcards

120 flip cards for active recall — shuffle, filter by part and track what you've mastered.

✦

Exam Tips & Traps

An MCQ + coding playbook, plus 18 examiner traps and the misconceptions that quietly cost marks.

</>

Code Patterns Library

Every reusable Python template — sklearn, Keras, NLP, RAG, agents — in one quick-copy reference.

How it all connects

The 18 topics build on one another across four parts. Follow the numbered path for the recommended study order, or hover over any topic to highlight what it leads into — then click to jump straight in.

Suggested study order 1 → 18 Hover a topic to reveal what it unlocks Click any topic to open it

All 18 topics

The whole syllabus, reorganised into 18 self-contained concepts across 4 parts. Each topic stands on its own — jump straight to whatever you need to revise.

Part 1 · Machine Learning Foundations

Foundations of ML

ML vs traditional programming, the AI/ML/DL hierarchy, the four learning types and core ML tasks.

SupervisedUnsupervisedClassification

Data Preprocessing

Missing values, outliers, feature scaling and encoding categorical data the right way.

ImputationScalingEncoding

Linear Regression

Model formulation, OLS, the seven assumptions and the limitations of a straight line.

OLSAssumptions

Logistic Regression

The sigmoid function, log-odds, decision thresholds and why classification needs an S-curve.

SigmoidClassification

Model Evaluation & Validation

Confusion matrix, precision/recall, F1, regression metrics, bias-variance and cross-validation.

F1RMSEBias-Variance

Decision Trees

Splitting, Gini & Information Gain, regression trees, overfitting and hyperparameter tuning.

GiniInfo Gain

Part 2 · Deep Learning & Language

Neural Networks

The perceptron, gradient descent, backpropagation, multi-layer perceptrons, activations and RNNs.

PerceptronBackpropMLPRNN

Natural Language Processing

Text preprocessing, tokenization, word embeddings and sequential modelling.

TokenizationEmbeddings

Transformers & LLM Architecture

The Transformer, self-attention (Q/K/V), responsible AI and bias mitigation.

TransformerAttention

Part 3 · Generative AI in Practice

Generative AI Modalities

Text, image, code, audio and multimodal models, plus ethics in generative AI.

DiffusionMultimodal

Working with LLM APIs

OpenAI & Google APIs, API-key management, costs, rate limits and embeddings.

API KeysHTTP 429

Prompt Engineering

Prompt design, zero/one/few-shot, chain-of-thought, role prompting and refinement.

Few-ShotCoT

Fine-Tuning LLMs

Fine-tuning vs prompting vs RAG, when to fine-tune, instruction-tuning and LoRA/PEFT.

Instruction-TuningLoRA

Chatbots & Conversation Memory

Memory challenges, multi-turn strategies, context management and history passing.

MemoryContext

Part 4 · Building AI Applications

Retrieval-Augmented Generation

Grounding LLMs, the retrieve-augment-generate pipeline, vector databases and embeddings.

ChromaFAISS

Rapid Prototyping

Why HTML/CSS slows you down, Streamlit, Gradio and building ML UIs in pure Python.

StreamlitGradio

AI Agents

LLM brain, tools, memory, planning, the ReAct framework, state management and LangGraph.

ToolsReActLangGraph

Low-Code Automation

n8n workflows, nodes, credentials, triggers and replicating LangChain logic visually.

n8nTriggers

🎯 Ready to test yourself? Once you have worked through the topics, take the full Mock Exam — 40 MCQs and 8 coding questions covering the entire syllabus, with instant scoring.

GenAI Speedrun · Built from the full course material, reorganised into 18 concept-based topics. Comprehensive and self-contained — covering, but not limited to, the uploaded material.

Made by Abhinav Bajpai