Machine learning is Big Data being used at its most extreme level, processing vast and disparate data sets at a machine level to find patterns buried within, producing insights beyond human recognition.
Machine-learning is all the rage in fraud detection, with industry analysts, academics, businesses and technology media examining the advantages of algorithms and big data in the fight against e-commerce fraud. Especially for fraud analysts working in companies with small budgets , machine-learning tools are seen as a cost-effective way to
Stumbling through overwhelming aisles of wines, it can be daunting to find the perfect match to that asparagus-heavy dish. There are pairing tables, books and websites filled with descriptions and ratings, but they aren’t quite an exact science. Seeking out and buying a wine, only to realize you’ve gone the
In the future imagined by science fiction, artificial intelligence will reign supreme and take over pretty much everything humans can do. Frankly this sci-fi vision isn’t helpful when it comes to applying technology because it distracts us from thinking about what people do well and what advanced techniques such as
Deep Learning is one of the key parts of data science. As data becomes increasingly important and accessible, today’s biggest companies are rapidly investing in deep learning. In fact, it is considered to be so vital to future technologies that many are sharing their own results and discoveries with the
Looking for the perfect podcast for your morning commute or during your downtime? Here’s a list of the best podcasts in data (in alphabetic order). Data Skeptic Unusual Angles Data Skeptic takes a different take on how we review data—thanks to some healthy skepticism, listeners come out with unusual information and knowledge.
A few months ago, Airbnb ran a great post about how its trust and safety data scientists build machine learning models to protect users from fraud by predicting bad actors. As the piece illustrated using Game of Thrones, a highly nuanced model is required to determine something like whether someone
“I don’t think that you should approach big data as a solution in search of a problem”- Interview with Skimlinks Maria Mestre
I completed a PhD in signal processing at Cambridge developing models of user behaviour using brain data. After the PhD I joined Skimlinks as a data scientist, where I model online user behaviour and work on much larger datasets. My main role is implementing large-scale machine learning models processing terabytes
“I often warn data analysts not to underestimate the power of small data” – Interview with Data Mining Consultant Rosaria Silipo
Rosaria has been a researcher in applications of Data Mining and Machine Learning for over a decade. Application fields include biomedical systems and data analysis, financial time series (including risk analysis), and automatic speech processing. She is currently based in Zurich, Switzerland. What project have you worked on do you
“Open source and public cloud are the most impactful shifts I have seen.” – Interview with Google Cloud Platform’s William Vambenepe
William Vambenepe is the Lead Project Manager for Big Data at Google Cloud Platform. Dataconomy interviewed him about his career path, his current role and how he sees the industry changing. You’ve worked for some of the biggest names in the industry (HP, Oracle, Google), what stands out to you
Within the rail industry, anything which helps keep trains moving, avoiding operational delays and improves customer experience, is worth pursuing. Many OEMs are now investing significant resources into one of the most valuable and potentially rewarding currencies in business: Big Data. In rail, and specifically when it comes to rolling