Fundamentals of Machine Learning
Machine Learning (ML) is a subset of artificial intelligence (AI) that enables systems to learn from data and improve performance over time without explicit programming. ML models identify patterns in data and use these patterns to make predictions and automate decision-making processes.
Types of Machine Learning:
- Supervised Learning – The model is trained on labeled data, where input-output pairs are provided. Algorithms include:
- Linear Regression
- Logistic Regression
- Support Vector Machines (SVM)
- Random Forest
- Unsupervised Learning – The model discovers hidden patterns in unlabeled data. Techniques include:
- K-Means Clustering
- Hierarchical Clustering
- Principal Component Analysis (PCA)
- Reinforcement Learning – The model learns by interacting with an environment and receiving rewards or penalties based on actions taken. Examples:
- Q-Learning
- Deep Q Networks (DQN)
- Policy Gradient Methods
Machine Learning is widely used in recommendation systems, fraud detection, image recognition, and predictive analytics.
