AI Prerequisites
Core concepts in Python, Probability, Neural Networks, and Vectors you need before starting.
Covers only what AI Engineering needs — not full ML theory
Python Fundamentals for AI
beginner~4hMaster variables, loops, lists, list comprehensions, and basic NumPy arrays used in AI pipelines.
Probability Intuition for Language Models
beginner~4hLearn probabilities, expectation, and conditional probability $P(A|B)$ which dictates next-token generation.
Introduction to Neural Networks
beginner~6hLearn neurons, connection weights, biases, layers, activation functions (ReLU), and how signals propagate forward.
Deep Learning & Gradient Descent
beginner~6hLearn loss functions, gradients, training iterations, and how models adjust weights to reduce errors.
Text Tokenization Basics
beginner~4hLearn how text characters are converted into subwords and mapped to unique number IDs for AI processing.
Vector Embeddings Explained
beginner~5hLearn how text tokens are mapped to dense vector arrays, representing semantic meanings in multi-dimensional space.
Vector Search & Similarity Mathematics
beginner~5hLearn Cosine Similarity, Dot Product, Euclidean distance, and Nearest Neighbors indexing used in RAG systems.
Web APIs & JSON payloads
beginner~4hLearn HTTP requests, endpoints, methods, headers, status codes, and formatting JSON payloads for AI services.
Client-Server Architecture
beginner~4hLearn the relationship between clients (hosts requesting data) and servers (data processors) over networks and pipes.
Reinforcement Learning Foundations
beginner~6hLearn agents, states, actions, policy optimization, and reward signals used to align models in RLHF and GRPO.