Text Classification with Neural Networks

It’s not difficult to use Scikit-learn to build machine-learning models that analyze text for sentiment, identify spam e-mails, and classify textual data in other ways.

Binary Classification with Neural Networks

One of the common uses for machine learning is performing binary classification, which looks at an input and predicts which of two possible classes it belongs to. Practical uses include […]

Building Neural Networks with Keras and TensorFlow

Machine learning isn’t hard when you have a properly engineered dataset to work with. The reason it’s not hard is libraries such as Scikit-learn and ML.NET, which reduce complex mathematical […]

Deep Learning

Deep learning is a subset of machine learning that relies primarily on neural networks. Most of what’s considered AI today is accomplished with deep learning. From recognizing objects in photos […]

Building Machine-Learning Models with ML.NET

Scikit-learn is arguably the world’s most popular machine-learning framework. The efficacy of the library, the documentation that accompanies it, and the mindshare that surrounds it are the primary reasons more ML models are written in Python than any other language.

Operationalizing Machine-Learning Models

All of the machine-learning models presented so far in this series were written in Python. Models don’t have to be written in Python, but many are, thanks in part to […]

Recommender Systems

Another branch of machine learning that has proven its mettle in recent years is recommender systems – systems that recommend products or services to customers. Amazon’s recommender system reportedly drives […]