Machine learning, a key part of AI, lets machines learn from experience and improve their performance without needing explicit programming. It falls into three main categories: supervised, unsupervised, and reinforcement learning. Each one works differently and tackles specific kinds of problems. Machine Learning can be categorized into three types :
1 Supervised Machine Learning :
In Supervised Machine , the model is trained on “Labeled Data” . Labeled data in machine learnig is raw data that has given labels to provide meaning and context for machine learning model. The main goal of Suspervised machine learning is to learn mapping from inputs to outputs.
- Supervised Machine Learning Algorithms :
- Logistic Regression
- Linear Regression
- K-Nearest Neighbors ( K-NN )
- Decision Tree
- Random Forest
- Artificial Neural Networks ( ANN )
2 Unsupervised Machine Learning :
Unsupervised learning involves feeding a computer program input data without labeled responses. The system tries to find patterns, groupings, or structures in the data all by itself. This technique is commonly applied in clustering—like when we group customers in a “segmenting” fashion—and association problems—like when we use “market basket” analysis. The most common algorithms that we use for such unsupervised learning are k-means clustering and principal component analysis (PCA).
- Unsupervised Machine Learning Algorithms :
- K-Means
- Hierarchical Clustering
- Mean Shift
- Self-OrganizingMaps
- Generative Adversarial Networks ( GANs )
- Linear Discriminant Analysis ( LDA )
3 Reinforcement Machine Learning :
With reinforcement learning, we use trial and error to teach a computer program how to perform a task. We have an “agent” learn by having it interact with an “environment.” The agent takes actions in the environment, and we give it “rewards” or “penalties” in response to its actions. At the end of the process, the agent has learned a policy—i.e., it knows what to do in what situations. We commonly use appreciation or “reward” signals to guide the agent’s learning.
- Reinforcement Machine Learning Algorithms :
- Q-Learning
- Deep Q-Network ( DQN )
- Double Q-Learning
- Dueling DQN
- Reinforce
- Deep Deterministic Policy ( DDPG )
These are types of machine learning .
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