Machine Learning

Machine Learning

Introduction to Machine Learning

Machine Learning (ML) is a subset of artificial intelligence enabling systems to learn from data and improve tasks without explicit programming. ML algorithms identify patterns in data, build predictive models, and can be applied to diverse domains—from image recognition to language translation and autonomous systems.

Learning Paradigms

Supervised Learning

  • Trains on labeled data (features + known outcomes).
  • Tasks: classification, regression.
  • Examples: spam detection, price prediction.

Unsupervised Learning

  • Trains on unlabeled data to find structure.
  • Tasks: clustering, dimensionality reduction.
  • Examples: customer segmentation, anomaly detection.

Reinforcement Learning

  • Agents learn by interacting with environment.
  • Tasks: control, game playing.
  • Examples: game AI, robotics.
ML Workflow Diagram

Core Algorithms

  • Linear Regression: Predict continuous values.
  • Decision Trees: Rule-based classification.
  • Neural Networks: Deep models for complex patterns.

Evaluation Metrics

  • Accuracy, Precision, Recall, F1 Score
  • Mean Squared Error (MSE), R²
  • ROC-AUC for binary classification

Applications & Challenges

Applications:

  • Computer Vision
  • Natural Language Processing
  • Recommendation Systems

Challenges:

  • Data Quality & Quantity
  • Model Interpretability
  • Bias & Ethical Concerns
▶ Video Source

Conclusion

By understanding paradigms, algorithms, and challenges, you can leverage machine learning to extract insights and build intelligent systems, while responsibly managing data and model biases.