This repository provides a tutorial implementation of AutoML for batch and online learning.
Open source
24 alternativespattern classification
This repository collects tutorials and examples for learning machine learning and pattern classification.
About pattern classification
This GitHub repository collects tutorials and examples on machine learning and pattern classification. Its homepage lists them by topic, including pre-processing, model evaluation, parameter estimation, machine learning algorithms, clustering, data collection, and data visualization.
Many entries are linked as IPython notebooks, with some also available as Markdown or PDF files. Several examples use scikit-learn. The homepage excerpt does not state a license name, a required skill level, or a publication date for the materials.
Key features
- Pre-processing tutorials on feature scaling, encoding categorical features, and feature selection
- Model evaluation material on performance metrics, cross-validation, and model selection
- Parameter estimation notes on maximum likelihood and kernel density estimation
- Algorithm tutorials on naive Bayes, neural networks, ensemble methods, and decision trees
- Clustering examples on hierarchical, density-based, graph-based, and probabilistic methods
- Data collection and visualization examples, such as MNIST and Iris dataset analysis
Best for: Students and practitioners who want worked, topic-organized examples of machine learning and pattern classification methods in Python.
Good for
- Open source, with the repository and its files publicly viewable on GitHub
- Topics are organized into clear sections, from data preparation through model evaluation
- Examples are provided in several formats, including IPython notebooks, Markdown, and PDF
pattern classification at a glance
- Category
- Education & Learning › AI Learning & Guides
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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pattern classification FAQ
What formats are the tutorials available in?
The listed entries link to IPython notebooks, Markdown files, and PDF files, depending on the topic.
Which topics does the collection cover?
The homepage lists sections on pre-processing, model evaluation, parameter estimation, machine learning algorithms, clustering, collecting data, and data visualization, among others.
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