The Data Science Lab


How to Train a Machine Learning Radial Basis Function Network Using C#

A radial basis function network (RBF network) is a software system that's similar to a single hidden layer neural network, explains Dr. James McCaffrey of Microsoft Research, who uses a full C# code sample and screenshots to show how to train an RBF network classifier.

How to Create a Radial Basis Function Network Using C#

Dr. James McCaffrey of Microsoft Research explains how to design a radial basis function (RBF) network -- a software system similar to a single hidden layer neural network -- and describes how an RBF network computes its output.

How to Do Machine Learning Evolutionary Optimization Using C#

Resident data scientist Dr. James McCaffrey of Microsoft Research turns his attention to evolutionary optimization, using a full code download, screenshots and graphics to explain this machine learning technique used to train many types of models by modeling the biological processes of natural selection, evolution, and mutation.

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How to Do Multi-Class Logistic Regression Using C#

Dr. James McCaffrey of Microsoft Research uses a full code program, examples and graphics to explain multi-class logistic regression, an extension technique that allows you to predict a class that can be one of three or more possible values, such as predicting the political leaning of a person (conservative, moderate, liberal) based on age, sex, annual income and so on.

How to Create a Machine Learning Decision Tree Classifier Using C#

After earlier explaining how to compute disorder and split data in his exploration of machine learning decision tree classifiers, resident data scientist Dr. James McCaffrey of Microsoft Research now shows how to use the splitting and disorder code to create a working decision tree classifier.

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How to Compute Disorder for Machine Learning Decision Trees Using C#

Using a decision tree classifier from a machine learning library is often awkward because it usually must be customized and library decision trees have many complex supporting functions, says resident data scientist Dr. James McCaffrey, so when he needs a decision tree classifier, he always creates one from scratch. Here's how.

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How to Do Machine Learning Perceptron Classification Using C#

Dr. James McCaffrey of Microsoft Research uses code samples and screen shots to explain perceptron classification, a machine learning technique that can be used for predicting if a person is male or female based on numeric predictors such as age, height, weight, and so on. It's mostly useful to provide a baseline result for comparison with more powerful ML techniques such as logistic regression and k-nearest neighbors.

How to Do Naive Bayes with Numeric Data Using C#

Dr. James McCaffrey of Microsoft Research uses a full code sample and screenshots to demonstrate how to create a naive Bayes classification system when the predictor values are numeric, using the C# language without any special code libraries.

How to Work with C# Vectors and Matrices for Machine Learning

Here's a hands-on tutorial from bona-fide data scientist Dr. James McCaffrey of Microsoft Research to get you up to speed with machine learning development using C#, complete with code listings and graphics.

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How to Do Logistic Regression Using ML.NET

Microsoft Research's Dr. James McCaffrey show how to perform binary classification with logistic regression using the Microsoft ML.NET code library. The goal of binary classification is to predict a value that can be one of just two discrete possibilities, for example, predicting if a person is male or female

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Getting Started with AutoML for ML.NET

Dr. James McCaffrey provides hands-on examples in introducing ML.NET, for machine learning prediction models, and AutoML, which automatically examines different ML algorithms, finds the best one, and creates a Visual Studio project with the C# code backing the best model, along with C# code that shows how to use the trained model to make a prediction.

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How to Do Neural Network Glorot Initialization Using Python

Microsoft Research data scientist Dr. James McCaffrey explains what neural network Glorot initialization is and why it's the default technique for weight initialization.

How To Code Modern Neural Networks Using Python and NumPy

Data scientist Dr. James McCaffrey begins a series on presenting and explaining the most common modern techniques used for neural networks, for which over the past couple of years there have been many small but significant changes in the default techniques used.

How to Do Thompson Sampling Using Python

Suppose you have three different Internet advertising strategies and you want to determine which of them is the best as quickly as possible. Or suppose you work for a medical company and you want to determine which of three new drugs is the most effective. Resident data scientist Dr. James McCaffrey shows how Thompson Sampling can help.

Naive Bayes Classification Using Python

Dr. James McCaffrey of Microsoft Research uses Python code samples and screenshots to explain naive Bayes classification, a machine learning technique used to predict the class of an item based on two or more categorical predictor variables, such as predicting the gender (0 = male, 1 = female) of a person based on occupation, eye color and nationality.

Weighted k-NN Classification Using Python

Need to predict the political party affiliation (democrat, republican, independent) of a person based on their age, annual income, gender, years of education and so on? Our resident data scientist Dr. James McCaffrey shows a technique that can help with that and much more -- with code!

Neural Anomaly Detection Using Keras

Our resident doctor of data science this month tackles anomaly detection, using code samples and screenshots to explain the process of finding rare items in a dataset, such as discovering fraudulent login events or fake news items.

Support Vector Machines Using Accord.NET

The Data Science doctor delves into supporting vector machines, software systems that can perform binary classification such as creating a model to predict the gender of a person based on their age, annual income, height and weight.

Self-Organizing Maps Using Python

Dr. James McCaffrey of Microsoft Research uses a full project code sample and screenshots to detail how to use Python to work with self-organizing maps (SOM), which let you investigate the structure of a set of data.

Image Classification Using Keras

Creating a custom image classification model is challenging, but the existence of neural network libraries like Keras has made it doable. Here's how, with many code samples and a full project download.

Sentiment Analysis Using Keras

Custom sentiment analysis is hard, but neural network libraries like Keras with built-in LSTM (long, short term memory) functionality have made it feasible. Step into the Data Science Lab with Dr. McCaffrey to find out how, with full code examples.

Q-Learning Using Python

The Data Science Doctor explains how to use the reinforcement learning branch of machine learning with the Q-learning approach, providing code on how to solve a maze problem for an easy-to-understand example.

How to Do Neural Binary Classification Using Keras

Our resident data scientist provides a hands-on example on how to make a prediction that can be one of just two possible values, which requires a different set of techniques than classification problems where the value to predict can be one of three or more possible values.

Neural Regression Using Keras

Data scientist Dr. James McCaffrey provides code, screenshots and step-by-step instructions on how to perform regression using a deep neural network with the Keras code library.

DNN Image Classification Using Keras

The Data Science Doctor provides a hands-on tutorial, complete with code samples, to explain one of the most common methods for image classification, deep neural network, used, for example, to identify a photograph of an animal as a "dog" or "cat" or "monkey."

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