Machine learning modeling

There are a number of ways to tackle machine learning modeling. Find content on supervised and unsupervised machine learning algorithm training and modeling processes, including expert tips to help you evaluate, choose and use the right machine learning model to deliver accurate predictive outputs to solve business problems.

July 15, 2020 15 Jul'20
Care provider uses automated machine learning in healthcare

An assisted living and transitional care provider uses DataRobot to automate the process of building and deploying machine learning models, enabling it to deploy models quickly.
November 07, 2019 07 Nov'19
Booz Allen releases Modzy AI platform and marketplace

Booz Allen Hamilton introduced an AI platform and marketplace made for uploading, deploying and managing AI models across a scalable environment.
October 30, 2019 30 Oct'19
Google releases TensorFlow Enterprise for enterprise users

With the new TensorFlow for enterprises, organizations running previous versions of TensorFlow can get long-term support, including security updates and select bug fixes.
August 09, 2019 09 Aug'19
IBM interprets machine models with AI Explainability kit

IBM's open source AI Explainability 360 toolkit packages algorithms and training examples to help humans better understand the decision-making process of machine learning models.

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Associativity, graphical summary computations aid ML insights

Associativity computation and graphical summary computation allow for more complex insights, and in turn improve predictions. Explore how these ML techniques work in practice. Continue Reading
machine learning

Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input ... Continue Reading
deep learning

Deep learning is a type of machine learning and artificial intelligence (AI) that imitates the way humans gain certain types of knowledge. Continue Reading

Weigh the pros and cons of technologies, products and projects you are considering.

15 data science tools to consider using in 2021

Numerous tools are available for data science applications. Read about 15, including their features, capabilities and uses, to see if they fit your analytics needs. Continue Reading
In-depth guide to machine learning in the enterprise

Enterprises are adopting machine learning technologies at rapid rates. In this machine learning guide, we break down what you need to know about this transformative technology. Continue Reading
Synthetic data for machine learning combats privacy, bias issues

Synthetic data generation for machine learning can combat bias and privacy concerns while democratizing AI for smaller companies with data set issues. Continue Reading

Learn to apply best practices and optimize your operations.

How to build a machine learning model in 7 steps

Building a viable, reliable and agile machine learning model that streamlines operations and bolsters business planning takes patience, preparation and perseverance. Continue Reading
Unsupervised machine learning: Dealing with unknown data

Learn how machine learning works when dealing with unclassified, unlabeled data sets and how, using certain algorithms and other practices, the system can learn on its own. Continue Reading
Free machine learning course: Using ML algorithms, practices and patterns

This 13-lesson series offers an overview of machine learning and its applications for those hoping to break into the machine learning job market or learn more about this technology. Continue Reading

We’ve gathered up expert advice and tips from professionals like you so that the answers you need are always available.

Tackling the AI bias problem at the origin: Training data

Though data bias may seem like a back-end issue, the enterprise implications of an AI software using biased data can derail model implementation. Continue Reading
How to troubleshoot 8 common autoencoder limitations

Autoencoders' ability for automated feature extraction, data preparation, and denoising are complicated by their common problems and limitations in usage. Continue Reading
Data science's ongoing battle to quell bias in machine learning

Machine learning expert Ben Cox of H2O.ai discusses the problem of bias in predictive models that confronts data scientists daily and his techniques to identify and neutralize it. Continue Reading