Machine Learning is a branch of artificial intelligence that enables systems to learn from data and improve their performance without explicit programming. In machine learning, models extract patterns from past data and use these patterns to make predictions or decisions on new data.
Types of Machine Learning:
1. Supervised Learning
The model is trained on labeled data. Example: Spam email classification (with labels "spam" and "not spam").
Algorithms: Linear Regression, Decision Trees, Neural Networks.
2. Unsupervised Learning
The model discovers hidden patterns and structures in data without labels.
Example: Customer segmentation based on purchasing behavior.
Algorithms: K-Means, Principal Component Analysis (PCA).
3. Semi-Supervised Learning
A combination of the above two methods, where some data points are labeled, and others are not.
Example: Disease detection from medical images, where only a limited number of images are labeled.
4. Reinforcement Learning
The model learns by interacting with the environment and receiving rewards or penalties.
Example: Training a robot to play chess or drive autonomously.
Algorithms: Q-Learning, Deep Q-Networks (DQN).
Applications of Machine Learning:
Natural Language Processing (Machine translation, chatbots)
Image and Video Recognition (Facial recognition, self-driving cars)
Time Series Forecasting (Stock price prediction, weather forecasting)
Anomaly Detection (Fraud detection in credit cards)
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Machine Learning
├── Supervised Learning
│ ├── Regression (e.g., Linear Regression)
│ ├── Classification (e.g., Decision Trees, Neural Networks)
│
├── Unsupervised Learning
│ ├── Clustering (e.g., K-Means)
│ ├── Dimensionality Reduction (e.g., PCA)
│
├── Semi-Supervised Learning
│ ├── Combination of Labeled & Unlabeled Data
│
├── Reinforcement Learning
| ├── Reward-Based Learning (e.g., Q-Learning, DQN)