Machine Learning Guide
Understanding machine learning concepts and applications
What is Machine Learning?
Machine Learning (ML) is a subset of AI that enables systems to learn and improve from experience without being explicitly programmed. It uses algorithms to analyze data, identify patterns, and make predictions or decisions.
Types of Machine Learning
- Supervised Learning: Learning from labeled training data (e.g., image classification)
- Unsupervised Learning: Finding patterns in data without labels (e.g., clustering)
- Reinforcement Learning: Learning through trial and error with rewards (e.g., game AI)
Common Algorithms
- Linear Regression
- Decision Trees
- Neural Networks
- Support Vector Machines
- Random Forests
- K-Means Clustering
ML in Mobile Apps
- Personalized recommendations
- Image and text recognition
- Predictive text and autocorrect
- Voice assistants
- Fraud detection
Tip: Start with supervised learning using popular frameworks like TensorFlow or PyTorch. Practice with real datasets to understand ML concepts better.