What is Machine Learning?
Machine learning is a field of artificial intelligence that enables computers to learn from data without explicit programming. It empowers computers to make predictions, classify data, and make informed decisions.
Applications of Machine Learning:
- Image and speech recognition
- Prediction (e.g., weather, stock prices)
- Fraud detection
- Recommendation systems (e.g., Netflix, Amazon)
Types of Machine Learning:
- Supervised learning: Involves training a model on labeled data (i.e., data with known outcomes).
- Unsupervised learning: Trained on unlabeled data to find patterns and structures.
- Reinforcement learning: The model learns through interactions with its environment and receives feedback on its actions.
Basic Concepts and Definitions:
- Data: The input used to train machine learning models.
- Model: A mathematical representation of the relationship between input and output data.
- Features: The individual elements of the input data.
- Label: The known outcome associated with a data point in supervised learning.
- Training: The process of fitting a model to the data.
- Evaluation: Assessing the performance of a trained model on a separate dataset.
Predicting House Prices:
- Data: House features, prices, and location.
- Label: House price.
- Model: A regression model trained to predict the house price based on its features.
- Evaluation: The model is evaluated on a test dataset to assess its accuracy in predicting house prices.
This example illustrates how machine learning can be applied to solve real-world problems by learning from data and making data-driven predictions.