### 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.

### 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.