An introduction to single-layer neural networks, covering the Perceptron and Adaline models, with Python implementations and gradient descent.
A tutorial explaining Principal Component Analysis (PCA), a dimensionality reduction technique used in machine learning and data analysis.
A guide to implementing a weighted majority rule ensemble classifier in scikit-learn to combine different ML models and improve prediction accuracy.
An introduction to Naive Bayes classifiers, focusing on their theory and application in text classification tasks like spam filtering.
A guide to performing nonlinear dimensionality reduction using RBF Kernel PCA, including theory, implementation, and examples.
An overview of predictive modeling, supervised machine learning, and pattern classification concepts, workflows, and applications.
A technical guide to Linear Discriminant Analysis (LDA) for dimensionality reduction and classification in machine learning, including a Python implementation.
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.
A Python tutorial covering essential tools and techniques for machine learning, including data visualization, PCA, LDA, and classification.
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.
An author critiques the overuse of PCA in data science, arguing it's not a universal solution for classification problems.
Overview of scikit-learn 0.14 release, highlighting new features like AdaBoost and performance improvements in benchmarks.
Overview of new features in scikit-learn 0.11, including non-linear models, semi-supervised learning, and sparse models for Python machine learning.
Announcement for a 2-year junior engineer position to work on the scikit-learn machine learning library at INRIA near Paris.
EuroScipy conference in Paris announces program details, keynote speakers, and a new poster session. Submission deadline is May 8th.
The scikit-learn team announces a community sprint on April 1st for improving the Python machine learning library, with in-person and remote participation.
A research group seeks a post-doc for the AzureBrain project, using Python for parallel computing and statistics on brain imaging/genetics data.
Explains the difference between ICA and PCA using scikit-learn code, advocating for runnable examples over static visuals in scientific materials.
