The Data Science Lab


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."

Introduction to Keras with TensorFlow

Our data science doctor provides a hands-on neural networking tutorial to explain how to get started with the popular Keras library, a high-level wrapper over TensorFlow.

Clustering Non-Numeric Data Using Python

The data science doctor explains everything you need to know about clustering data, the process of grouping items so those in a group (cluster) are similar and items in different groups are dissimilar.

Data Clustering with K-Means Using Python

Our Data Science Lab guru explains how to implement the k-means technique for data clustering, or cluster analysis, which is the process of grouping data items so that similar items belong to the same group/cluster.

Neural Network Dropout Using Python

Go hands-on with data scientist Dr. James McCaffrey as he explains neural network dropout, a technique that can be used during training to reduce the likelihood of model overfitting.

Neural Network Time Series Regression Using Python

Learn how to do time series regression using a neural network, with "rolling window" data, coded from scratch, using Python.

Logistic Regression Using Python

The data doctor continues his exploration of Python-based machine learning techniques, explaining binary classification using logistic regression, which he likes for its simplicity.

Neural Network L1 Regularization Using Python

The data science doctor continues his exploration of techniques used to reduce the likelihood of model overfitting, caused by training a neural network for too many iterations.

Neural Network Batch Training Using Python

Our resident data scientist explains how to train neural networks with two popular variations of the back-propagation technique: batch and online.

Neural Network L2 Regularization Using Python

Our data science expert continues his exploration of neural network programming, explaining how regularization addresses the problem of model overfitting, caused by network overtraining.

Neural Network Momentum Using Python

With the help of Python and the NumPy add-on package, I'll explain how to implement back-propagation training using momentum.

Neural Network Cross Entropy Using Python

James McCaffrey uses cross entropy error via Python to train a neural network model for predicting a species of iris flower.

Neural Network Back-Propagation Using Python

You don't have to resort to writing C++ to work with popular machine learning libraries such as Microsoft's CNTK and Google's TensorFlow. Instead, we'll use some Python and NumPy to tackle the task of training neural networks.

Neural Networks Using Python and NumPy

With Python and NumPy getting lots of exposure lately, I'll show how to use those tools to build a simple feed-forward neural network.

This R/S4 Demo Might Take You Out of Your Comfort Zone

Let's explore factor analysis again, this time using the R ability to tap into OOP, but we won't use the RC model.

Revealing Secrets with R and Factor Analysis

Let's use this classical statistics technique -- and some R, of course -- to get to some of the latent variables hiding in your data.

Data Clustering Using R

Find the patterns in your data sets using these Clustering.R script tricks.

R Language OOP Using S3

The S3 OOP model is still widely used, so let's use write S3-style OOP code via the R language.

Logistic Regression Using R

I predict you'll find this logistic regression example with R to be helpful for gleaning useful information from common binary classification problems.

Neural Networks Using the R nnet Package

The R language simplifies the creation of neural network classifiers with an add-on that lays all the groundwork.

Results Are in -- the Sign Test Using R

The R language can be used to perform a sign test, which is handy for comparing "before and after" data.

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