Supervised Learning
The algorithm learns from labeled training data, learning to map inputs to known outputs. Used for classification (spam detection) and regression (price prediction) tasks.
Labeled DataMachine Learning
The science of teaching computers to learn from experience
What is Machine Learning?
Machine Learning (ML) is a subset of artificial intelligence that enables systems to automatically learn and improve from experience without being explicitly programmed. Instead of following rigid rules, ML algorithms build mathematical models based on sample data, known as "training data," to make predictions or decisions.
The field has evolved dramatically since its inception, moving from simple pattern recognition to complex deep learning systems capable of understanding images, translating languages, and even generating creative content.
Paradigms
Types of Machine Learning
Unsupervised Learning
Discovers hidden patterns in unlabeled data. Clustering algorithms group similar items together, while dimensionality reduction simplifies complex datasets for analysis.
Pattern DiscoveryReinforcement Learning
An agent learns optimal behavior through trial and error, receiving rewards or penalties for its actions. Powers game-playing AI and robotic control systems.
Reward-BasedArchitecture
Neural Networks
Inspired by the human brain, neural networks form the backbone of modern AI
Deep Neural Networks
Multiple layers of interconnected nodes that progressively extract higher-level features from raw input. Deep learning has revolutionized image recognition, speech processing, and natural language understanding.
Convolutional Networks (CNN)
Specialized for processing grid-like data such as images. CNNs use convolution operations to detect features like edges, textures, and complex patterns automatically.
Recurrent Networks (RNN)
Designed for sequential data like text and time series. RNNs maintain memory of previous inputs, making them ideal for language modeling and speech recognition.
Transformers
The architecture behind GPT and modern language models. Transformers use attention mechanisms to process entire sequences in parallel, enabling unprecedented scale and capability.
Fundamentals
Key Concepts
Training & Validation
Models learn from training data and are evaluated on separate validation sets to ensure they generalize well to new, unseen examples.
Bias-Variance Tradeoff
Balancing model complexity: too simple underfits the data, too complex overfits. Finding the sweet spot is crucial.
Hyperparameter Tuning
Optimizing learning rate, batch size, and architecture choices to achieve the best possible model performance.
Feature Engineering
Transforming raw data into meaningful representations that make patterns more accessible to learning algorithms.