Machine Learning (ML)

Definition

Machine Learning (ML) is a field of artificial intelligence (AI) where computers learn patterns from data and improve performance without being explicitly programmed.

• Instead of writing rules by hand, we give the system examples (data).
• The model learns relationships between inputs (features) and outputs (labels).
• Once trained, it can make predictions on new, unseen data.

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Core Idea

$\text{Model: } y = f(X) + \epsilon$

• $X$ = input features
• $y$ = output (prediction)
• $f(\cdot)$ = function learned from data
• $\epsilon$ = error/noise

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Types of Machine Learning

1. Supervised Learning
   • Learn from labeled data (input → correct output).
   • Tasks:
     • Regression → predict continuous value (house price, temperature).
     • Classification → predict categories (spam/not spam, disease/healthy).
2. Unsupervised Learning
   • Learn from unlabeled data (no given output).
   • Tasks:
     • Clustering → group similar data (customer segmentation).
     • Dimensionality Reduction → compress features (PCA, embeddings).
3. Semi-Supervised Learning
   • Mix of labeled + unlabeled data.
   • Common when labels are expensive (medical images).
4. Reinforcement Learning (RL)
   • Agent learns by interacting with environment and receiving rewards.
   • Example: AlphaGo, robotics, recommendation systems.
5. Self-Supervised Learning (modern deep learning trend)
   • Model learns from raw data by predicting part of the input from other parts (e.g., masked words in BERT).
   • Core idea behind LLMs like GPT.

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Applications

• Computer Vision: image classification, object detection, medical imaging.
• Natural Language Processing (NLP): chatbots, translation, summarization.
• Healthcare: disease prediction, drug discovery.
• Finance: fraud detection, algorithmic trading.
• E-commerce: recommendation systems.
• Autonomous Systems: self-driving cars, robotics.

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Workflow in ML

1. Collect data
2. Clean & preprocess
3. Choose model (linear regression, decision tree, neural net, etc.)
4. Train model (optimize weights)
5. Evaluate on test data
6. Deploy to production
7. Monitor & retrain (continuous learning)

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Example

• Input: house size (1000 sqft), number of rooms (3).
• Output: price = $250,000.
• Train ML model on thousands of such examples.
• Model learns: price increases with size & rooms.
• Later → given new input (1200 sqft, 4 rooms), model predicts price ≈ $300,000.

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Why It Matters

• Automates pattern discovery.
• Handles massive, complex datasets humans can’t.
• Powers modern AI systems (LLMs, vision models, recommender systems).

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Summary
Machine Learning = teaching computers to learn patterns from data and make predictions without explicit programming.

• Main types: supervised, unsupervised, reinforcement.
• Applications: vision, NLP, healthcare, finance, e-commerce.