A technical guide to Linear Discriminant Analysis (LDA) for dimensionality reduction and classification in machine learning, with comparisons to PCA.
A technical guide to Linear Discriminant Analysis (LDA) for dimensionality reduction and classification in machine learning, including a Python implementation.
A report on the 2014 scikit-learn developer sprint in Paris, covering participants, venues, achievements, and sponsors.
Highlights of the scikit-learn 0.15 release, including performance improvements, new features, and deprecations.
A guide to feature scaling and normalization in machine learning, covering standardization, Min-Max scaling, and their implementation in scikit-learn.
Explains feature scaling and normalization in machine learning, comparing standardization and Min-Max scaling, with examples using scikit-learn.
A Python tutorial covering essential tools and techniques for machine learning, including data visualization, PCA, LDA, and classification.
A tutorial on using Python tools for machine learning, covering data loading, visualization, preprocessing, and classification with scikit-learn.
Announcing the four students accepted for Google Summer of Code 2024 to work on scikit-learn projects, including neural networks and performance improvements.
A technical guide to implementing Principal Component Analysis (PCA) for dimensionality reduction, comparing it with MDA and providing code examples.
A comparison of four Python implementations for Kernel Density Estimation (KDE), analyzing their features, interfaces, and performance.
Overview of scikit-learn 0.14 release, highlighting new features like AdaBoost and performance improvements in benchmarks.
A developer details a new, enhanced Ball Tree and KD-Tree implementation for scikit-learn, comparing its performance and features to existing solutions.
Benchmarking Cython memoryviews for optimizing distance metric calculations in Python, comparing performance with NumPy and older Cython methods.
Overview of new features in scikit-learn 0.11, including non-linear models, semi-supervised learning, and sparse models for Python machine learning.
Scikit-learn and related Python libraries have students accepted for Google Summer of Code projects focused on performance and new features.
Announcement for a 2-year junior engineer position to work on the scikit-learn machine learning library at INRIA near Paris.
The scikit-learn team announces a community sprint on April 1st for improving the Python machine learning library, with in-person and remote participation.
Explains the difference between ICA and PCA using scikit-learn code, advocating for runnable examples over static visuals in scientific materials.
A humorous take on machine learning concepts like overfitting and algorithm comparisons, using Python's scikit-learn library as an example.
