Machine-learning algorithms find and apply patterns in data. And they pretty much run the world.

If you are an aspiring data scientist, you may have come across the terms artificial intelligence (AI), machine learning, deep learning and neural networks.

Level 2 is continual learning: ML systems that incorporate new data and update in real-time, for which she defines real-time to be in the order of minutes.

Data validation in machine learning plays a critical role in ensuring that data sets adhere to specific project criteria and affirming the effectiveness of prior cleaning and transformation efforts.

Getting machine-learning-based systems to handle edge cases is complex for several reasons: Good data is not available about these edge cases due to their infrequent occurrence. The knowledge base ...

Machine learning, a field of artificial intelligence (AI), is the idea that a computer program can adapt to new data independently of human action.

Reinforcement learning is like unsupervised ML in that the training data is also unlabelled. However, when asked a question about the data, the outcome is graded – so there is still a level of ...

You can't build, validate, or measure the success of a machine learning model without the right amounts and types of data, notes Yiwen Huang, CEO of r2.ai, an automated machine learning platform.

Explore the role of labeled data in machine learning, the challenges it presents, techniques and the future of data labeling.