Machine Learning
Algorithms that learn patterns from data rather than following fixed rules.
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Overview
Machine learning is the broad field of algorithms that improve at a task by learning from data, rather than following manually programmed rules. Deep learning (neural networks) is one family of ML techniques among several (others include decision trees and linear models).
- What it is
- A field of algorithms that learn patterns from data to make predictions or decisions.
- Why it's used
- For tasks where the 'rules' are too complex or numerous to hand-write, but plenty of example data exists to learn from.
- Where it fits
- The layer between 'AI' (the broad umbrella) and 'deep learning' (one specific ML technique family).
Core concepts
- Training data and labels
- Model, parameters, and training
- Overfitting
- Evaluation metrics
Example
Training is iterative: the model's parameters are nudged repeatedly to reduce error on example data, not solved for in one step.
// Simplified idea of "training":
// Show the model thousands of (email, is_spam) pairs.
// It adjusts internal parameters to reduce prediction error
// on those examples -- then generalizes to new emails.Common use cases
- Spam and fraud detection
- Recommendation systems
- Forecasting
Project ideas
- Explain, in plain language, how you'd design training data for a model that predicts whether a customer will churn