Python and Machine Learning
Python, Machine Learning, and Language Wars. A Highly Subjective Point of View Oh god, another one of those subjective, pointedly opinionated click-bait headlines? Yes! Why did I bother writing this? Well, here is one of the most trivial yet life-changing insights and worldly wisdoms from my former professor that has become my mantra ever since: “If you have to do this task more than 3 times just
Turn Your Twitter Timeline into a Word Cloud
Nov 28, 2014 by Sebastian Raschka Turn Your Twitter Timeline into a Word Cloud Using Python Last week, I posted some visualizations in context of “Happy Rock Song” data mining project, and some people were curious about how I created the word clouds. I thought it might be interesting to use a different dataset for this tutorial: Your personal twitter timeline. Sections Turn Your Twitter Timeline i
Naive Bayes and Text Classification
Naive Bayes classifiers, a family of classifiers that are based on the popular Bayes’ probability theorem, are known for creating simple yet well performing models, especially in the fields of document classification and disease prediction. In this first part of a series, we will take a look at the theory of naive Bayes classifiers and introduce the basic concepts of text classification. In follow
Kernel tricks and nonlinear dimensionality reduction via RBF kernel PCA
Sections Sections Principal Component Analysis PCA and linear dimensionality reduction Nonlinear dimensionality reduction Kernel functions and the kernel trick Gaussian radial basis function (RBF) Kernel PCA Implementing the RBF kernel PCA step-by-step 1. Computation of the kernel (similarity) matrix. 2. Eigendecomposition of the kernel matrix. Examples of RBF Kernel PCA Half-moon shapes Linear PC
Linear Discriminant Analysis
Aug 3, 2014 by Sebastian Raschka Sections Sections Introduction Principal Component Analysis vs. Linear Discriminant Analysis What is a “good” feature subspace? Summarizing the LDA approach in 5 steps Preparing the sample data set About the Iris dataset Reading in the dataset Histograms and feature selection Normality assumptions LDA in 5 steps Step 1: Computing the d-dimensional mean vectors Step
Parallel Programming in Python
An introduction to parallel programming using Python's multiprocessing module -- written by Sebastian Raschka June 20, 2014 Tweet CPUs with multiple cores have become the standard in the recent development of modern computer architectures and we can not only find them in supercomputer facilities but also in our desktop machines at home, and our laptops; even Apple's iPhone 5S got a 1.3 Ghz Dual-co
Entry Point Data
In this short tutorial I want to provide a short overview of some of my favorite Python tools for common procedures as entry points for general pattern classification and machine learning tasks, and various other data analyses. Sections Sections Installing Python packages About the dataset Downloading and saving CSV data files from the web Reading in a dataset from a CSV file Visualizating of a da
Moving from MATLAB matrices to NumPy arrays - A Matrix Cheatsheet
Moving from MATLAB matrices to NumPy arrays - A Matrix Cheatsheet -- written by Sebastian Raschka on January 22, 2014 Tweet Over time Python became my favorite programming language for the quick automation of tasks, such as manipulating and analyzing data. Also, I grew fond of the great matplotlib plotting library for Python. MATLAB/Octave was usually my tool of choice when my tasks involved matr
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