This is the first of a series of articles intended to make Machine Learning more approachable to those who do not have a technical training. I hope it is helpful. Advancements in computer technology over the past decades have meant that the collection of electronic data has become more commonplace in most fields of human endeavor. Many organizations now find themselves holding large amounts of data spanning many prior years. This data can relate to people, financial transactions, biological information, and much, much more.
Everybody expects to have clean drinking water. But as the lead crises in Michigan has shown, that's not always the case. Now American Water, the largest publicly traded water company in the country, is actively researching the use of machine learning and real-time streaming data technology to detect and identify potentially harmful chemical signatures in its surface drinking water supply. The company is in the early stages of building such a machine learning system. But according to American Water Senior Technologist John Kuchmek, the potential benefits of training machine learning models on real-time water quality data collected by remote sensors are too great to ignore.
What should a Machine Learning Engineer or Data Scientist understand in order to be prepared for an interview? In this episode of Big Data Big Questions I breakdown the 3 major areas to prepare for the Machine Learning or Data Scientist interview process. The interview process is won before you step in the door. In fact it's probably decided by YOU in the days leading up to the interview. How well did you research the organization or team?
Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Then this course is for you! This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way. We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.
August is a popular time for vacation, and even hard-working neural networks may want to take a few epochs off from their training and take a break! KDnuggets Cartoon below examines how it might go. Robot: Finally, I get a break from Deep Learning! This cartoon was ably drawn by Jon Carter. See also other recent KDnuggets Cartoons: Cartoon: Data Scientist was the sexiest job of the 21st century until ... Cartoon: How is Data Science Different From Religion?
Big data remains a game for the 1 percent. Or the 15 percent, as new O'Reilly survey data suggests. According to the survey, most enterprises (85 percent) still haven't cracked the code on AI and machine learning. A mere 15 percent "sophisticated" enterprises have been running models in production for more than five years. Importantly, these same companies tend to give more time and attention to critical areas like model bias and data privacy, whereas comparative newbies are still trying to find the On button.
I meet with a lot of business and tech leaders, and nearly all of them ask at some point about artificial intelligence. They're worried that their company is missing out on this coming AI revolution, and falling behind rivals, because they don't have the deep tech skills to put it to use. I tell them that getting real value from AI, and from its related discipline of machine learning, doesn't have to be that hard. The reason being they can tap into AI embedded within cloud services, which they can quickly launch and put to use. Here are four AI use cases I give as examples of how they can quickly tap the benefits of AI without much work--and without an army of data scientists.
Andrew Ng is a great fan of reading research papers as a long term investment in your own study (On Life, Creativity, And Failure about Andrew Ng). Anyone who has worked in our field (AI, Machine Learning) can attest to that. AI is a complex and a rapidly evolving field. It's a challenge to stay up to date with the latest technical details. Based on my experience, in this post, I discuss how you can stay up to date by learning from the community.
This is an eclectic collection of interesting blog posts, software announcements and data applications I've noted over the past month or so. ONNX Model Zoo is now available, providing a library of pre-trained state-of-the-art models in deep learning in the ONNX format. In the 2018 IEEE Spectrum Top Programming Language rankings, Python takes the top spot and R ranks #7. Julia 1.0 has been released, marking the stabilization of the scientific computing language and promising forwards compatibility. Google announces Cloud AutoML, a beta service to train vision, text categorization, or language translation models from provided data.