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Learn how noisy crowdsourced labels and annotator disagreement affect model training, then monitor for distribution bias in ML systems.
Build semantic text search and reverse image search with embeddings, then see how to put the applications into production with Milvus.
Use KNIME’s Keras integration to build an LSTM text generator, create images with GANs, and apply neural styling to a profile picture.
Explores ethical ways to monetize company data, including direct sales, indirect monetization, and assessing data value.
Detect fraud in labeled and unlabeled data using KNIME, from random forests and autoencoders to statistical and outlier detection methods.
Explore Azure Marketplace offers, deploy applications to virtual machines with one click, and try tools running on a VM.
Learn how purpose, audience, data visualizations, and narrative can make data analysis findings clearer and more influential in business decisions.
An introduction to the Quantum Circuit Born Machine, showing how quantum circuits can generate data and how their foundations differ from classical machine learning.
A systematic way to connect business metrics and actionable levers, using machine learning to make decisions under uncertainty.
A beginner’s guide to finding data in databases with SQL, including how relational and NoSQL systems differ and how SQL fits alongside analysis tools.
A data-centric approach to computer vision focuses on collecting and annotating representative, consistent image datasets and mitigating harmful bias.
See how individual differences in discrete choice models can be estimated from aggregate sales data through simulated consumers.
An unsupervised correlation service expands enterprise search queries by uncovering implicit relationships among terms in a content corpus.
Explores why collecting security data is not the same as detecting threats, including the risks of over-collection.
Applies the four cores of credibility—integrity, intent, capabilities, and results—to building trust between people and AI systems.
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