AI & ML Track

AI & ML

Path: From Zero to LLM

Completed Modules16
Coming Soon1
Total Reading Time186 mins
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01

What is a Neuron?

Learn the foundational math of a perceptron, including weights, biases, and activation thresholds through an interactive demo.

Quick8 minSim
02

Neural Networks

Deconstruct feedforward neural networks, backpropagation, and how information flows through layers in a step-by-step visual animation.

Deep12 minSim
03

How Training Works

Visualize gradient descent, loss functions, and learning rates in real time with an interactive optimization graph.

Quick10 minSim
04

Word Embeddings

Explore the vector space where words capture meaning. Map Word2Vec intuitions in an interactive 2D spatial coordinate visualization.

Quick9 min
05

Attention Mechanism

Trace Query, Key, and Value interactions that power modern LLMs. Run step-by-step self-attention calculations on live token inputs.

Quick8 minSim
06

The Transformer

Unpack the multi-head attention and encoder-decoder architecture that revolutionized modern Generative AI systems.

Deep14 min
07

How LLMs Work

Learn what happens during pre-training, instruction fine-tuning, RLHF alignment, and active token generation cycles.

Quick11 min
08

RAG Pipelines

Connect document chunking, semantic vector database retrieval, and model generation into an enterprise-ready pipeline.

Deep13 min
09

Building AI Agents

Design autonomous AI agents utilizing tool execution, conversational memory buffers, planning steps, and orchestration routines.

Quick12 min
10

Prompt Engineering

Master techniques like Chain-of-Thought, few-shot prompting, and structured schemas to generate highly reliable outputs.

Quick10 min
11

Linear Regression

Understand the foundation of predictive algorithms. Fit lines of best fit and optimize slopes with gradient descent.

Deep11 min
12

Logistic Regression

Learn binary classification models by mapping data to class probabilities using the mathematical sigmoid function.

Deep10 min
13

Decision Trees

Walk through recursive binary splitting, calculating node entropy and Gini impurity metrics dynamically.

Deep12 minSim
14

Random Forest

Construct ensemble classifiers that aggregate decision trees through bootstrap sampling and feature bagging.

Deep10 min
15

K-Means Clustering

Cluster unlabeled data iteratively. Visualize centroid migration and class assignment steps in real-time.

Deep11 minSim
16

Cosine Similarity

Calculate vector direction similarity to run semantic keyword search, context matching, and document rank algorithms.

Quick7 min
17

Types of AI & AI Agents

A taxonomy of AI systems by capability and functionality, and a deep-dive into the six types of AI agents — from simple reflex to multi-agent systems.

Quick18 min