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How to Monitor Machine Learning Models in Real-Time
We present practical methods for near real-time monitoring of machine learning systems which detect system-level or model-level faults and can see when the world changes.
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The Hundred-Page Machine Learning Book
This book covers supervised and unsupervised learning, support vector machines, neural networks, ensemble methods, gradient descent, cluster analysis and dimensionality reduction, autoencoders and transfer learning, feature engineering and hyperparameter tuning.
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Data Scientist’s Dilemma: The Cold Start Problem – Ten Machine Learning Examples
We present an array of examples showcasing the cold-start problems in data science where the algorithms and techniques of machine learning produce the good judgment in model progression toward the optimal solution.
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10 Exciting Ideas of 2018 in NLP
We outline a selection of exciting developments in NLP from the last year, and include useful recent papers and images to help further assist with your learning.
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 How to go from Zero to Employment in Data Science
We propose the quickest and surest way to go from zero experience to landing a job, either in data science generally, or specifically in a new programming language or a new technology.
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The SIAM Book Series on Data Science
SIAM is soliciting manuscripts for its new book series on the mathematical and computational foundations of data science.
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Principles of Database Management: The Practical Guide to Storing, Managing and Analyzing Big and Small Data
This comprehensive textbook teaches the fundamentals of database design, modeling, systems, data storage, and the evolving world of data warehousing, governance and more.
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Explainable Artificial Intelligence
We outline the necessity of explainable AI, discuss some of the methods in academia, take a look at explainability vs accuracy, investigate use cases, and more.
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4 Myths of Big Data and 4 Ways to Improve with Deep Data
There is a fundamental misconception that bigger data produces better machine learning results. However bigger data lakes / warehouses won’t necessarily help to discover more profound insights. It is better to focus on data quality, value and diversity not just size. "Deep Data" is better than Big Data.
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The Five Best Data Visualization Libraries
There are plenty of library options out there to make great visualizations. We outline five of the best, complete with code examples and explanations, that will enable you to create and build interactive visualizations.
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How to go from Zero to Employment in Data Science