Machine Learning

‍

What It Is:
Machine Learning (ML) involves algorithms that learn from historical data and adapt to new patterns without explicit reprogramming. It underpins predictive analytics, recommendation engines, anomaly detection, and more.

Technical Breakdown:
       •        Supervised learning: Models (e.g., regression, decision trees, gradient boosting, neural nets) learn from labeled datasets.
       •        Unsupervised learning: Clustering (K-means, DBSCAN), dimensionality reduction (PCA, t-SNE) discover hidden structures.
       •        Reinforcement learning: Agents learn via trial and error to maximize rewards (e.g., robotics, supply-chain optimization).

Pipeline:

Data collection → Feature engineering → Model training (scikit-learn, TensorFlow, PyTorch) → Evaluation → Deployment (MLflow, Docker, cloud APIs).

Applications: Demand forecasting, credit scoring, personalized marketing, image/speech recognition.

ML is like teaching computers to “see” patterns humans miss—turning oceans of data into maps of opportunity.

‍