Types of Machine Learning in Data Science
1. Supervised Learning (Labeled Data)
- Definition: The model learns from labeled data where the outcome is known.
- Examples:
- Spam detection (Emails labeled as spam or not spam).
- Stock price prediction (Using past stock data).
- Algorithms Used: Linear Regression, Decision Trees, Random Forest, Support Vector Machines (SVM).Data Science Course in Pune
2. Unsupervised Learning (Unlabeled Data)
- Definition: The model identifies patterns and structures in data without labeled outcomes.
- Examples:
- Customer segmentation (Grouping customers based on behavior).
- Anomaly detection (Detecting fraudulent transactions).
- Algorithms Used: K-Means Clustering, Hierarchical Clustering, Principal Component Analysis (PCA).Data Science Training in Pune
3. Reinforcement Learning (Learning from Actions)
- Definition: The model learns by interacting with its environment and receiving rewards or penalties.
- Examples:
- Self-driving cars learning to navigate roads.
- Game-playing AI like DeepMind’s AlphaGo.
- Algorithms Used: Q-Learning, Deep Q Networks (DQN), Policy Gradient Methods.