Tag: machine learning

While organizations are turning to Artificial Intelligence and Machine Learning (AI/ML) to better serve customers, reduce cost, and gain other competitive advantages, there are significant challenges to executing these programs.

Data Scientists need a self-service experience that allows them to build, scale, and share their machine learning (ML) modeling results across the hybrid cloud.

With Red Hat OpenShift, you’ll enable data scientists to easily enable and deploy their ML modeling without the dependency on IT to provision infrastructure.

Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to perform the task.

Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop a conventional algorithm for effectively performing the task.

How data Governance can help your AI transformation? What is the purpose of data governance in Machine Learning and Artificial Intelligence? In this video, we will describe and give you examples why data governance is critical to the success of your business, organization, and government.

Automatic Machine Learning or “AutoML” is a field of Artificial Intelligence that is gaining a lot of interest lately. The idea is that doing any kind of task related to machine learning involves a whole lot of steps like cleaning a dataset, choosing a model, deciding what the right configurations of that model should be, deciding what the most relevant features are etc. The goal of AutoML is to automate all of that up to a point where all a data scientist would need to do is tell a machine to perform some task using a dataset and wait for it to learn how by itself. In this episode, i’m going to explain several popular AutoML techniques, then compare top AutoML frameworks like AutoKeras, Auto Sklearn, h20, Ludwig, etc. to help you decide which one will be the best for your needs.

This lecture gives an overview of the main categories of machine learning, including supervised, un-supervised, and semi-supervised techniques, depending on the availability of expert labels. We also discuss the different methods to handle discrete versus continuous labels.

This lecture gives an overview of the main categories of machine learning, including supervised, un-supervised, and semi-supervised techniques, depending on the availability of expert labels. We also discuss the different methods to handle discrete versus continuous labels.