A post-doc position in computational neuroscience using Python and machine learning to find biomarkers from fMRI brain connectivity data.
Summary of the 2015 MLOSS workshop on open-source machine learning software, covering key talks and the maturing community.
A tutorial on building an Outlook add-in that uses Azure Text Analytics to classify email sentiment as positive, neutral, or negative.
Author shares the journey and process of writing 'Python Machine Learning,' a technical book for aspiring machine learning practitioners.
Author shares the journey and process of writing a book on Python Machine Learning, including productivity tips and the book's focus.
A scientist explains why Python is their preferred language for machine learning and data analysis, arguing for productivity over language wars.
A scientist explains why Python is their preferred tool for machine learning and data analysis, emphasizing productivity over language wars.
A summary of the second Nilearn sprint, highlighting new features and improvements for this neuroimaging machine learning library.
A summary of a talk on achieving top 3% in a Kaggle competition, covering validation, feature engineering, and ensemble techniques.
Interview with data scientist Jeroen Janssens about his background, work on data science at the command line, and his Data Science Toolbox project.
Announcing the MLOSS workshop at ICML 2015, focusing on open-source software and ecosystems for machine learning.
An introduction to single-layer neural networks, covering the Perceptron and Adaline models, with Python implementations and gradient descent.
An introduction to single-layer neural networks, covering the history, perceptrons, adaptive linear neurons, and the gradient descent algorithm with Python implementations.
A tutorial explaining Principal Component Analysis (PCA), a dimensionality reduction technique used in machine learning and data analysis.
A tutorial explaining the internals of Principal Component Analysis (PCA) for dimensionality reduction 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.
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.
