Machine LearningLesson 69Free preview
Getting Started
This lesson is part of Python · All levels
Summary
Machine learning gives computers the ability to learn from data to make predictions without explicitly programming rules. There are two main groups: supervised learning that learns from labeled data and unsupervised learning that finds patterns in unlabeled data. The overall workflow is to collect data, divide the training data and the test data, train the model, and then evaluate the results. Scikit-learn is a popular Python library that provides a complete tool for the entire thread.
Key points
- Machine learning makes models that learn from data.
- Supervised learning uses labeled data, unsupervised learning uses unlabeled data
- General course: prepare the data, model training, testing, then evaluation
- Overfitting occurs when the model is too oblivious to training data
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