Posted by Christian Frank, Google Research, Zürich Melodies stuck in your head, often referred to as “ earworms ,” are a well-known and s…

Join LifeWithData in Machine Learning Up-To-Date 23 for a curated, concise machine learning newsletter that points the reader to key developments.

Linear algebra concepts are key for understanding and creating machine learning algorithms, especially as applied to deep learning and neural networks. This course reviews linear algebra with applications to probability and statistics and optimization&amp…

In the field of machine learning, meta learning techniques focus on the research and development of automated learning algorithms. To get a better understanding of them, I got in touch with Nicholas Guttenberg, a senior research scientist at CrossLabs.

As the foundation of many world economies, the agricultural industry is ripe with public data to use for machine learning. We at Lionbridge AI have gathered the best publicly available agricultural datasets for machine learning projects.

Google Cloud NLP, IBM Watson, and the Lionbridge AI platform are some of the best NLP and text classification tools for ML projects.

Contribute to designing, building, evaluating, shipping, and refining Spotify’s product by hands-on ML developmentCollaborate with a multi-functional agile team spanning user research, design, data science, product management,…

Online courses are one of the best ways to study machine learning, so we’ve written an overview of the 5 best machine learning courses on Coursera.

Machine learning algorithms usually operate as black boxes and it is unclear how they derived a certain decision. This book is a guide for practitioners to make machine learning decisions interpretable.