We hear the term “machine learning” a lot these days (usually in the context of predictive analysis and artificial intelligence), but machine learning has actually been a field of its own for several decades. Only recently have we been able to really take advantage of machine learning on a broad

From Jeopardy winners and Go masters to infamous advertising-related racial profiling, it would seem we have entered an era in which artificial intelligence developments are rapidly accelerating. But a fully sentient being whose electronic “brain” can fully engage in complex cognitive tasks using fair moral judgement remains, for now, beyond

In the last few months, I have had several people contact me about their enthusiasm for venturing into the world of data science and using Machine Learning (ML) techniques to probe statistical regularities and build impeccable data-driven products. However, I’ve observed that some actually lack the necessary mathematical intuition and

Ajit Jaokar is a leading expert working at the intersection of Data Science, IoT, AI, Machine Learning, Big Data, Mobile, and Smart Cities. He teaches IoT and Data Science at Oxford and also is a director of Smart Cities Lab in Madrid. Ajit’s work involves applying machine learning techniques to

From Decentralized Model Exchanges to Model Audit Trails [This is based on a talk I first gave on Nov 7, 2016. Here are the slides.] In recent years, AI (artificial intelligence) researchers have finally cracked problems that they’ve worked on for decades, from Go to human-level speech recognition. A key

There’s a lot of buzz around the term “Sentiment Analysis” and the various ways of doing it. Great! So you report with reasonable accuracies what the sentiment about a particular brand or product is. Opinion Mining and Sentiment Analysis After publishing this report, your client comes back to you and

There is general agreement that language is the key to AI, but who holds the key to language – algorithms or people? According to the hype, the key to automated natural language understanding lies in vast data collections containing millions of words coupled with machine learning algorithms. If you believe

This is a follow up to an article Per wrote last year, Machine Learning in a Week, on how he kickstarted his way into machine learning (ml) by devoting five days to the subject. Follow him on Medium and check out his archive. First intro: Hacker News and Udacity My interest in ml stems

Francisco is the Founder and CEO of cortical.io, a machine learning company that develops Natural Language Processing solutions for Big Text Data. Francisco’s medical background in genetics combined with over two decade’s of experience in Information Technology, inspired him to create a groundbreaking technology, called Semantic Folding, which is based

Data Natives Speaker Dr. Stefan Kühn is Lead Data Scientist at codecentric. Together with his team he is developing robust, fast and intelligent algorithms and is applying modern machine learning methods for analyzing large datasets. Before codecentric he was a researcher in the Scientific Computing Group at the Max Planck