introductory machine learning education with Python/scikit-learn
Jupyter notebooks and mglearn helper code for the book 'Introduction to Machine Learning with Python', teaching practical ML with scikit-learn.
Same problem, same approach. Swapping one for another is a config change, not a rewrite.
Pick if you want an ML book-code repo that uses PyTorch/HuggingFace in addition to scikit-learn rather than the seed's scikit-learn-only examples.
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, PyTorch, and Hugging Face libraries.
Pick for a modern maintained ML book repo covering scikit-learn and PyTorch with updated APIs.
Code Repository for Machine Learning with PyTorch and Scikit-Learn
Pick for a current, widely used ML book companion covering Scikit-Learn/Keras/TensorFlow with continued updates.
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
Choose for the official, up-to-date scikit-learn MOOC notebooks, refreshed for current sklearn releases.
Machine learning in Python with scikit-learn MOOC
Solves the same problem with a different architecture or at a different layer. Expect to rewrite the integration.
Pick for learning algorithms by implementing from scratch in Python rather than applying scikit-learn.
π€ Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained
Pick for compact pure-Python implementations of core ML algorithms to see how they work internally.
Minimal and clean examples of machine learning algorithms implementations
Pick for a wide self-paced set of ML tutorials with detailed derivations rather than one book's narrative.
:earth_americas: machine learning tutorials (mainly in Python3)
Pick for the same educational algorithm walk-throughs in Octave/MATLAB instead of Python.
π€ MatLab/Octave examples of popular machine learning algorithms with code examples and mathematics being explained
An earlier or more limited way to do the job. Still the right call when you need something small, proven, or CPU-only.
Older scikit-learn teaching notebooks/video series; useful but stuck with now-dated sklearn workflows.
Jupyter notebooks from the scikit-learn video series
Ancient scikit-learn book code; choose if you need a minimal legacy-API scikit-learn tutorial, otherwise too dated.
Source code for the "Learning scikit-learn: Machine Learning in Python"
First-edition book code by Raschka; still instructive but older APIs and scope than current ML books.
The "Python Machine Learning (1st edition)" book code repository and info resource
These projects were analysed and named introduction_to_ml_with_python among their alternatives. The relationship is not symmetric β how introduction_to_ml_with_python rates them is a separate judgement, made when introduction_to_ml_with_python is analysed in its own right.
calls introduction_to_ml_with_python βchoose for the book companion notebooks covering scikit-learn and classical ML.β
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