Machine Learning Algorithms Explained
Machine Learning Algorithms Explained
Supervised vs Unsupervised Learning with Examples
What is Machine Learning?
What is Machine Learning?
Machine Learning (ML) is a branch of Artificial Intelligence that enables systems to learn and improve from experience automatically without being explicitly programmed.
Types of Machine Learning
Types of Machine Learning
1. Supervised Learning
- Model learns from labeled data.
- Example: Linear Regression, Decision Trees.
2. Unsupervised Learning
- Model finds hidden patterns in unlabeled data.
- Example: K-Means Clustering, PCA.
3. Reinforcement Learning
- Model learns by interacting with an environment and receiving feedback.
Supervised Learning
Supervised Learning
Supervised learning uses input-output pairs to train models.
Common algorithms include:
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forest
- Support Vector Machines (SVM)
Unsupervised Learning
Unsupervised Learning
Unsupervised learning deals with unlabeled data to find hidden patterns. Common algorithms include:
- K-Means Clustering
- Hierarchical Clustering
- Principal Component Analysis (PCA)
- Association Rule Learning
Real-World Applications
Real-World Applications
- Email Spam Filtering
- Fraud Detection
- Customer Segmentation
- Image and Speech Recognition
- Product Recommendation Systems
Summary
Summary
Machine Learning enables computers to identify patterns, make predictions, and improve automatically with experience. Understanding supervised and unsupervised learning forms the foundation for building intelligent systems.
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