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Sebastian Raschka, PhD Candidate – Michigan State University

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Implementing a Principal Component Analysis (PCA) in Python, step by step

Implementing a Principal Component Analysis (PCA) in Python, step...

Sections Sections Introduction Principal Component Analysis (PCA) Vs. Multiple Discriminant Analysis (MDA) What is a “good” subspace? Summarizing the PCA approach Generating some 3-dimensional sample data Why are we chosing a 3-dimensional sample? 1. Taking the whole dataset ignoring the class labels 2. Computing the d-dimensional mean vector 3. a) Computing the Scatter Matrix 3. […]

Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning – Part 2

Model Evaluation, Model Selection, and Algorithm Selection in Mac...

Bootstrapping and Uncertainties: Introduction In the previous article (Part I), we introduced the general ideas behind model evaluation in supervised machine learning. We discussed the holdout method, which helps us to deal with real world limitations such as limited access to new, labeled data for model evaluation. Using the holdout method, we split our dataset […]

Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning – Part 1

Model Evaluation, Model Selection, and Algorithm Selection in Mac...

 Introduction Machine learning has become a central part of our life – as consumers, customers, and hopefully as researchers and practitioners! Whether we are applying predictive modeling techniques to our research or business problems, I believe we have one thing in common: We want to make “good” predictions! Fitting a model to our training data […]

Linear Discriminant Analysis – Bit by Bit

Linear Discriminant Analysis – Bit by Bit

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 2: […]

Diving Deep into Python, the not-so-obvious Language Parts

Diving Deep into Python, the not-so-obvious Language Parts

Sections Sections The C3 class resolution algorithm for multiple class inheritance Assignment operators and lists – simple-add vs. add-AND operators True and False in the datetime module Python reuses objects for small integers – use “==” for equality, “is” for identity And to illustrate the test for equality (==) vs. identity (is): Shallow vs. deep […]

Predictive Modeling, Supervised Machine Learning, and Pattern Classification

Predictive Modeling, Supervised Machine Learning, and Pattern Cla...

When I was working on my next pattern classification application, I realized that it might be worthwhile to take a step back and look at the big picture of pattern classification in order to put my previous topics into context and to provide and introduction for the future topics that are going to follow. Pattern […]

Single-Layer Neural Networks and Gradient Descent

Single-Layer Neural Networks and Gradient Descent

This article offers a brief glimpse of the history and basic concepts of machine learning. We will take a look at the first algorithmically described neural network and the gradient descent algorithm in context of adaptive linear neurons, which will not only introduce the principles of machine learning but also serve as the basis for […]

Highlight Syntax and Convert Markdown into HTML in 5 Steps

Highlight Syntax and Convert Markdown into HTML in 5 Steps

In this little tutorial, I want to show you in 5 simple steps how easy it is to add code syntax highlighting to your blog articles. There are more sophisticated approaches using static site generators, e.g., nikola, but the focus here is to give you the brief introduction of how it generally works. All the files I will […]

Principal Component Analysis in 3 Simple Steps

Principal Component Analysis in 3 Simple Steps

Principal Component Analysis (PCA) is a simple yet popular and useful linear transformation technique that is used in numerous applications, such as stock market predictions, the analysis of gene expression data, and many more. In this tutorial, we will see that PCA is not just a “black box”, and we are going to unravel its […]