Machine Learning (ML) is a field of artificial intelligence that gives computers the ability to learn from data without being explicitly programmed. Instead of writing step-by-step instructions, we provide algorithms with data and let them find patterns.
1. Types of Machine Learning
- Supervised Learning: Models learn from labeled examples (e.g., classifying emails as spam or not).
- Unsupervised Learning: Models find structure in unlabeled data (e.g., clustering customer segments).
- Reinforcement Learning: Agents learn by interacting with an environment and receiving rewards or penalties (e.g., game-playing AIs).
2. Core Concepts
- Features & Labels: Features are the inputs (e.g., pixel values), labels are the desired outputs (e.g., “cat” vs. “dog”).
- Training vs. Inference: Training is when the model learns patterns; inference is when the trained model makes predictions on new data.
- Overfitting & Underfitting: Overfitting happens when a model learns training noise instead of the underlying pattern; underfitting occurs when it can’t capture the pattern at all.
3. Common Algorithms
- Linear Regression for predicting continuous values.
- Logistic Regression and Decision Trees for classification.
- k-Means for clustering.
- Neural Networks for complex, high-dimensional tasks.
4. Workflow
- Data Collection
- Data Cleaning & Preprocessing
- Feature Engineering
- Model Selection & Training
- Evaluation (e.g., accuracy, precision/recall)
- Deployment & Monitoring
Machine Learning powers countless applications—from recommendation systems to medical diagnosis. In future posts, we’ll dive deeper into specific algorithms and real-world case studies.