Machine Learning Algorithms are the core components of artificial intelligence that enable computers to learn from data and improve their performance on a specific task without being explicitly programmed. They can be categorized into two main types:
Supervised Learning
- Regression: Predicting a continuous numerical value (e.g., house prices, stock prices).
- Linear Regression
- Decision Trees
- Random Forests
- Support Vector Machines (SVMs)
- Neural Networks
- Classification: Predicting a categorical label (e.g., spam or not spam, cat or dog).
- Logistic Regression
- Decision Trees
- Random Forests
- Support Vector Machines (SVMs)
- Naive Bayes
- Neural Networks
Unsupervised Learning
- Clustering: Grouping similar data points together.
- K-means clustering
- Hierarchical clustering
- DBSCAN
- Dimensionality Reduction: Simplifying data by reducing the number of features.
- Principal Component Analysis (PCA)
- t-SNE
How do they work? Machine learning algorithms learn patterns from data and use these patterns to make predictions or decisions on new, unseen data. They can be trained using labeled data (supervised learning) or unlabeled data (unsupervised learning).
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