Foundations · Guide
AI and Machine Learning Foundations
The groundwork under LLMs — ML types, neural networks, backprop, gradient descent, embeddings.
Before large language models there is machine learning: models that learn patterns from data rather than following hand-written rules. This guide covers AI vs machine learning vs deep learning, supervised, unsupervised and reinforcement learning, neural networks and backpropagation, gradient descent, embeddings, and the bias-variance tradeoff.
Generate your own lesson →What you'll learn
- Train, Validation, and Test Splits
- AI vs ML vs Deep Learning
- Embeddings as Learned Representations
- Overfitting and Bias-Variance
- Supervised, Unsupervised, and Reinforcement Learning
- Feature Engineering and Pipelines
- Gradient Descent and Loss Functions
- Classic ML Model Zoo
- Clustering and Dimensionality Reduction
- Neural Networks and Backpropagation
Lessons in this guide (10)
Train, Validation, and Test Splits
Learn why clean evaluation starts before model training.
AI vs ML vs Deep Learning
Understand the nested map of AI, machine learning, deep learning, and where agents fit.
Embeddings as Learned Representations
Understand how vectors encode meaning for search, clustering, and recommendations.
Overfitting and Bias-Variance
Diagnose when a model memorizes, underfits, or generalizes.
Supervised, Unsupervised, and Reinforcement Learning
Pick the right learning paradigm for labels, hidden structure, or reward-driven behavior.
Feature Engineering and Pipelines
Turn raw data into reliable model inputs without leaking validation data.
Gradient Descent and Loss Functions
See how models turn errors into weight updates.
Classic ML Model Zoo
Compare linear models, trees, support-vector machines, neighbors, and clustering.
Clustering and Dimensionality Reduction
Use k-means, DBSCAN, PCA, and UMAP to explore unlabeled data.
Neural Networks and Backpropagation
Build the mental model for layers, activations, gradients, and optimizers.
Frequently asked questions
What's the difference between AI, machine learning, and deep learning?
AI is the broad goal of machines doing intelligent tasks. Machine learning is the subset that learns from data. Deep learning is the subset of ML using many-layered neural networks — the basis of modern LLMs.
What is a neural network?
A neural network is layers of simple units ('neurons') with weighted connections that transform inputs into outputs. Training adjusts the weights so the network maps inputs to the right outputs.
What is supervised learning?
Supervised learning trains a model on labeled examples (input → known answer) so it learns to predict the answer for new inputs. Classification and regression are the two main kinds.