The Fundamentals of Machine Learning
Types of Machine Learning
Supervised learning
Trained Data with label and predict the data example : classification,
- Regression algorithms
- k-Nearest Neighbors
- Linear Regression
- Logistic Regression
- Support Vector Machines (SVMs)
- Decision Trees and Random Forests
- Neural networks
Unsupervised learning
The training data is unlabeled
Clustering
- K-Means
- DBSCAN
- Hierarchical Cluster Analysis (HCA)
Anomaly detection and novelty detection
- One-class SVM
- Isolation Forest
Visualization and dimensionality reduction
- Principal Component Analysis (PCA)
- Kernel PCA
- Locally Linear Embedding (LLE)
- t-Distributed Stochastic Neighbor Embedding (t-SNE)
Association rule learning
- Apriori
- Eclat
Reinforcement Learning
The learning system, called an agent in this context, can observe the environment, select and perform actions, and get rewards in return