A tutorial explaining the internals of Principal Component Analysis (PCA) for dimensionality reduction in machine learning and data analysis.
Author's 2014 review: writing a data science book from scratch in Python and preparing for/starting a software engineering job at Google.
A guide to implementing a weighted majority rule ensemble classifier in scikit-learn to combine different ML models and improve prediction accuracy.
A guide to building a weighted majority rule ensemble classifier in scikit-learn, demonstrated using the Iris dataset.
A developer shares a data mining project that builds a machine learning model to classify songs as happy or sad based on their lyrics.
An introduction to Naive Bayes classifiers, focusing on their theory and application in text classification tasks like spam filtering.
Explores Naive Bayes classifiers for text classification, covering theory and applications like spam filtering and song lyric analysis.
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.
An overview of predictive modeling, supervised machine learning, and the core workflow for pattern classification tasks.
A technical guide to Linear Discriminant Analysis (LDA) for dimensionality reduction and classification in machine learning, including a Python implementation.
A technical guide to Linear Discriminant Analysis (LDA) for dimensionality reduction and classification in machine learning, with comparisons to PCA.
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.
A blog post sharing the author's cover letter for an internship at iHub Research, focusing on their interest in automating hate speech detection using AI and NLP.
Explores how personas, data science, and k-means clustering can be used together to analyze user data and gain actionable business insights.
Announcing the four students accepted for Google Summer of Code 2024 to work on scikit-learn projects, including neural networks and performance improvements.
