Machine Learning (ML) is a subset of Artificial Intelligence where computers learn from data and improve their performance over time without being explicitly programmed.
Instead of giving rules manually, you provide data → the machine identifies patterns → and makes predictions or decisions.
How Machine Learning Works
- Provide Data (images, text, numbers, etc.)
- Train the Model using algorithms
- Model Learns Patterns from the data
- Model Makes Predictions on new, unseen data
This process allows ML systems to become more accurate with more data and training.
Types of Machine Learning
1. Supervised Learning
The model is trained on labeled data.
Example:
- Email Spam Detection
- Predicting house prices
2. Unsupervised Learning
The model finds hidden patterns without labels.
Example:
- Customer segmentation
- Grouping similar images
3. Reinforcement Learning
The model learns through trial and reward/punishment.
Example:
- Game-playing AI
- Self-driving car navigation
Popular ML Algorithms
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forest
- K-Means Clustering
- Neural Networks
These algorithms form the foundation for building intelligent systems.
Real-World Uses of Machine Learning
- Movie recommendations on Netflix
- Fraud detection in banking
- Product suggestions on Amazon
- Disease prediction in healthcare
- Speech recognition on smartphones
Machine Learning is everywhere — silently improving your daily digital life.
Conclusion
Machine Learning powers most modern AI applications. Understanding its types and working process will help you build intelligent systems in future lessons.
Citations
https://savanka.com/category/learn/ai-and-ml/
https://www.w3schools.com/ai/