Welcome to the Machine Learning Basics section of our community documentation! This guide provides foundational knowledge for beginners exploring AI and ML. Let's dive in!
🔍 What is Machine Learning?
Machine Learning (ML) is a subset of Artificial Intelligence (AI) that enables systems to learn patterns from data without explicit programming.
Key concepts include:
- Training Data: The dataset used to teach the model.
- Model: A mathematical representation of patterns.
- Inference: Applying the trained model to new data.
📊 Types of Machine Learning
Supervised Learning
- Uses labeled data (e.g., classification, regression).
- Example: Predicting house prices based on features.
Unsupervised Learning
- Works with unlabeled data (e.g., clustering, dimensionality reduction).
- Example: Customer segmentation.
Reinforcement Learning
- Learns by interacting with an environment through rewards/punishments.
- Example: Training robots for navigation.
🧠 Core Steps in ML
- Data Collection
- Data Preprocessing
- Feature Engineering
- Model Training
- Evaluation & Tuning
- Deployment
For a deeper dive into any of these steps, check our ML入门指南!
🌍 Applications of Machine Learning
- Healthcare: Disease prediction, medical imaging analysis.
- Finance: Fraud detection, algorithmic trading.
- Recommendation Systems: Netflix, Spotify, etc.
- Natural Language Processing (NLP): Chatbots, translation.
Explore more about NLP in our NLP专题文档.
📚 Resources for Learning
Let us know if you'd like to contribute to this documentation or ask questions! 🌟