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The Need For Mission Critical Text Analytics
“There’s simply too much textual information available for human beings to find or analyze it by themselves. Machines must help,” said Chris Biow, the senior vice president of Global Public Sector at Basis Technology. A 20-year text analytics veteran, Biow started his career in the space in 2000 working... Read more
How to Fix Data Leakage – Your Model’s Greatest Enemy
At ODSC London 2018, Yuriy Guts of DataRobot gave a talk on data leakage, including potential sources of the problem and how it can be remedied. Data leakage – also sometimes referred to as data snooping – is a phenomenon in machine learning that occurs when a model is... Read more
Reviewing Amazon’s Machine Learning University – Is it Worth All of the Hype?
As an educator in the field of data science, I’m always interested in new learning resources for machine learning. The industry needs a new crop of data scientists to fill the rising demand. This is why I was pleased to learn of the recent announcement of free access to... Read more
10 Tips to Get Started with Kaggle
Kaggle is a well-known community website for data scientists to compete in machine learning challenges. Competitive machine learning can be a great way to hone your skills, as well as demonstrate your skills. In this article, I will provide 10 useful tips to get started with Kaggle and get... Read more
The Data Scientist’s Holy Grail – Labeled Data Sets
The Holy Grail for data scientists is the ability to obtain labeled data sets for the purpose of training a supervised machine learning algorithm. An algorithm’s ability to “learn” is based on training it using a labeled training set – having known response variable values that correspond to a... Read more
Mail Processing with Deep Learning: A Case Study
Businesses increasingly delegate simple, boring, and repetitive tasks to artificial intelligence. In a case study, Alexandre Hubert — lead data scientist of software company Dataiku’s U.K. operations — worked on a team of three to automate mail processing with deep learning. At ODSC Europe 2018, Hubert detailed how his team... Read more
Thomas Wiecki of Quantopian on ‘Minding the Gap’ Between Statistics and Machine Learning at ODSC Europe 2018
Key Takeaways: It’s important for data scientists to understand the so-called “gap” between statistics and machine learning, and how there actually is a lot of commonality between the two; it’s just a matter of how you look at things. PyMC3 is a very useful probabilistic programming framework for Python.... Read more
Active Learning: Your Model’s New Personal Trainer
First, some facts. Fact: active learning is not just another name for reinforcement learning; active learning is not a model; and no, active learning is not deep learning. What active learning is and why it may be an important component of your next machine learning project was the subject... Read more
Three Machine Learning Practices That Keep Your Identity Safe
As privacy concerns escalate in the age of big data, developers constantly evolve artificial intelligence and machine learning techniques to keep your identity safe. Machine learning systems enable businesses to more effectively identify fraud and keep user information safe. These systems gather data that can provide much more powerful... Read more
An Introduction to Reinforcement Learning Concepts
Individuals interested in reinforcement learning crowded into a room at ODSC Europe 2018. There, Badoo’s lead data scientist Leonardo De Marchi hosted a four-hour workshop to guide attendees through the first steps. What is reinforcement learning? Reinforcement learning is one machine learning approach. Most people know of supervised and unsupervised learning.... Read more