Machine Learning Fundamentals
Machine learning is a set of methods that let computers learn patterns from data and make predictions without being explicitly programmed for each case. It powers recommendations, spam filters, image recognition, and forecasting systems.
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Intro
Machine Learning Fundamentals
Machine learning (ML) is the study of algorithms that improve their performance on a task by learning from data. Instead of writing rules to solve a problem, you provide examples and let the algorithm discover the rules.
Why machine learning exists
Some problems are too complex for hand-written rules:
- Recognizing objects in images (millions of pixel patterns).
- Filtering spam (language evolves faster than rules can follow).
- Recommending products (preferences vary per user and change over time).
- Predicting equipment failure (hundreds of sensor readings interact in non-obvious ways).
ML handles these by finding patterns in historical data and applying them to new inputs. The patterns are learned, not programmed.
The core idea
Every ML system does the same thing at a high level:
- Collect data with known inputs and (usually) known outputs.
- Choose a model — a mathematical structure that can represent relationships between inputs and outputs.
- Train — adjust the model's parameters to minimize error on the training data.
- Evaluate — measure performance on held-out data the model has not seen.
- Deploy — use the trained model to make predictions on new inputs.
The quality of the model depends on the data, the choice of algorithm, and how well you evaluate it — not just on training accuracy.
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Sources
- https://scikit-learn.org/stable/user_guide.html
Supports
- Algorithm implementations and API documentation
- Classification and regression metrics
- Cross-validation and model selection
- Feature preprocessing and engineering
- Regularization methods (L1, L2)
- Ensemble methods (random forest, gradient boosting)
- https://www.statlearning.com/
Supports
- Bias-variance tradeoff theory
- Linear and logistic regression foundations
- Decision trees and ensemble methods
- Regularization (lasso, ridge, elastic net)
- Cross-validation methodology
- Feature selection principles
- https://developers.google.com/machine-learning/crash-course
Supports
- Supervised learning workflow
- Training/test split rationale
- Overfitting causes and prevention
- Feature engineering practices
- Neural network introduction
- Data drift and model degradation
- https://ocw.mit.edu/courses/6-036-introduction-to-machine-learning-spring-2020/
Supports
- Mathematical foundations of ML algorithms
- Linear classifiers and margin-based methods
- Neural network architectures
- Reinforcement learning fundamentals
- https://www.khanacademy.org/math/statistics-probability
Supports
- Statistical foundations for ML evaluation
- Probability distributions underlying models
- Regression as a statistical method