AI / ML — 8 Modules · Beginner to Advanced

Learn Artificial Intelligence
& Machine Learning.

From the fundamentals of AI to cutting-edge Generative AI — this track covers everything you need: ML algorithms, deep learning, NLP, computer vision, reinforcement learning, and a complete DS & AI cheat sheet.

Start with Machine Learning AI/ML Interview →
⬡8 topics covered ⬡Beginner to advanced ⬡Interview-focused ⬡Includes cheat sheet
8 Modules AI → Cheat Sheet
25+ Interview Qs In the AI/ML track
6 Core Fields ML · DL · NLP · CV · RL · GenAI
1 Cheat Sheet DS & AI reference
Curriculum

All 8 AI / ML modules

Follow the path top-to-bottom or jump to any topic directly.

Module 01
Machine Learning
Supervised Unsupervised Algorithms Model Evaluation
Core ML algorithms — linear regression, decision trees, SVMs, clustering, and how to train, evaluate, and tune models effectively.
Intro level →
Module 02
Deep Learning
Neural Networks CNNs RNNs Backpropagation
Neural networks from perceptrons to deep architectures — feedforward, convolutional, and recurrent networks with activation functions and optimizers.
Mid level →
Module 03
NLP & LLMs
Tokenization Transformers BERT LLMs
Natural language processing — from text preprocessing to transformers, attention mechanisms, BERT, GPT, and how modern LLMs work.
Mid level →
Module 04
Computer Vision
Image Processing Object Detection YOLO Segmentation
Teaching machines to see — image classification, object detection with YOLO, image segmentation, and real-world CV pipelines.
Mid level →
Module 05
Reinforcement Learning
Agents & Rewards Q-Learning Policy Gradient
Learning through interaction — agents, environments, rewards, Q-learning, and policy-based methods that power game AI and robotics.
Advanced →
Module 06
Generative AI
GANs Diffusion Models Prompt Eng. RAG
The frontier — GANs, VAEs, diffusion models, prompt engineering, RAG pipelines, and how tools like ChatGPT and Stable Diffusion are built.
Advanced →
Module 07
DS & AI Cheat Sheet
Quick Reference Formulas Interview Ready
One-page reference for data science and AI — key formulas, algorithm comparisons, complexity cheat sheet, and interview quick-prep.
Reference →
Live Algorithm Visualizations
Step through algorithms interactively — no installs, runs entirely in your browser
6 Algorithms
K-Means Clustering Unsupervised
K-Means iteratively assigns points to the nearest centroid (★), then recomputes centroids as cluster means. Converges when assignments stop changing. Sensitive to initial placement — hit New Data for different starting positions. Uses k = 3 clusters.
Linear Regression Supervised
Ordinary Least Squares finds the line y = mx + b minimising the sum of squared residuals (shown as red vertical lines). The best-fit line minimises Σ(yᵢ − ŷᵢ)². Click the canvas to add new points, then re-fit.
Neural Network Forward Pass Deep Learning
Forward Pass — activations flow left → right. Each node computes a weighted sum then applies a non-linearity (ReLU in hidden layers, Sigmoid on output). Edge thickness encodes weight magnitude. Bright nodes = high activation. Architecture: [3 → 5 → 4 → 2].
Decision Tree Supervised
Decision Trees recursively split data by the feature giving the best Gini impurity or Information Gain. Each node is a yes/no question; leaves are predictions. Green = Accept, Yellow = Review, Red = Reject.
PCA — Principal Component Analysis Dimensionality Reduction
PCA finds orthogonal directions of maximum variance. PC1 captures the most variance; PC2 is orthogonal to it. Points can be projected onto fewer dimensions — compressing data while retaining structure. Hit New Data for a fresh dataset.
Gradient Descent Optimization
LR 0.04
Gradient Descent updates parameters by moving in the direction of steepest descent: θ ← θ − α · ∇L. Each dot is one step. Adjust LR — too high causes overshooting; too low causes painfully slow convergence. The pink dot is the current position on the loss surface.
← Home