How do spam detectors work - and how do you build one? How do you make it easier to find patterns in huge amounts of data? The answer is machine learning and artificial intelligence - and you can be a part of the next great tech frontier with help from this four-course bundle. Plus you can get the Machine Learning and Artificial Intelligence Bundle for 91% off at Escapist Deals. Cluster Analysis and Unsupervised Machine Learning in Python: A 1.5-hour training in cluster analysis, used in data mining and big data.

SYS-CON Events has announced today that Roger Strukhoff has been named conference chair of Cloud Expo and @ThingsExpo 2017 New York. The 20th Cloud Expo and 7th @ThingsExpo will take place on June 6-8, 2017, at the Javits Center in New York City, NY. "The Internet of Things brings trillions of dollars of opportunity to developers and enterprise IT, no matter how you measure it," stated Roger Strukhoff. "More importantly, it leverages the power of devices and the Internet to enable us all to improve the state of the world and lives of people."

This article is a part of an evolving theme. Here, I explain the basics of Deep Learning and how Deep learning algorithms could apply to IoT and Smart city domains. Specifically, as I discuss below, I am interested in complementing Deep learning algorithms using IoT datasets. I elaborate these ideas in the Data Science for Internet of Things program which enables you to work towards being a Data Scientist for the Internet of Things (modelled on the course I teach at Oxford University and UPM – Madrid).

A team of UCLA bioengineers has demonstrated that its technology may go a long way toward overcoming the challenges of treatment for acute lymphoblastic leukemia, among the most common types of cancer in children, and has the potential to help doctors personalize drug doses. The five-year survival rate for individuals with pediatric acute lymphoblastic leukemia is about 85 percent, however those who experience a recurrence generally have a poor prognosis and a bone marrow transplant is their only option for a permanent cure. Conventional treatment for this leukemia includes a combination of drugs, which come with short- and long-term side effects. Two of these drugs, 6-mercaptopurine and methotrexate, can cause liver disease and other life-threatening infections. During the maintenance phase of treatment, which aims to keep individuals in remission, dosing for these two drugs is frequently adjusted through a system of trial and error, which is not always accurate.

Scientists say machine learning is up to 93 percent accurate in identifying a suicidal person based on their responses to interview questions. The algorithm was described in a study published in the journal Suicide and Life-Threatening Behavior. Researchers were able to use the tool to classify patients as being suicidal, mentally ill but not suicidal, or neither. "These computational approaches provide novel opportunities to apply technological innovations in suicide care and prevention, and it surely is needed," study author John Pestian said in a press release. "When you look around healthcare facilities, you see tremendous support from technology, but not so much for those who care for mental illness. Only now are our algorithms capable of supporting those caregivers."

So there you have it – 5 free eBooks (plus a bonus book) for your summer reading. It would be great if you would leave brief reviews of these books in the comments below – I'm sure all the authors would appreciate your comments and shares. Join the debate below and let me know your thoughts... About the Author Lee Baker is an award-winning software creator with a passion for turning data into a story. A proud Yorkshireman, he now lives by the sparkling shores of the East Coast of Scotland. Physicist, statistician and programmer, child of the flower-power psychedelic '60s, it's amazing he turned out so normal! Turning his back on a promising academic career to do something more satisfying, as the CEO and co-founder of Chi-Squared Innovations he now works double the hours for half the pay and 10 times the stress - but 100 times the fun! He also wanted to be rich, famous and good looking.

Sebastian Thrun smiles a little awkwardly as he explains why he no longer believes in the educational revolution he sold to the world just a few years ago. The lean, balding robotics pioneer has been instrumental in convincing investors, governments, and colleges to splurge millions on the online college education platforms dubbed MOOCs, or massive online open courses, billed as opening up quality education to anyone on Earth (see "The Crisis in Higher Education"). Thrun, a Stanford professor, helped birth the frenzy when he put his introductory artificial intelligence course online in 2011, accidentally attracting 160,000 students. Amazed by the response, he took time out from Stanford and also from a side job working on autonomous cars and other research at Google to found Udacity, a company offering MOOCs in computing, math, and physics. It attracted $160 million in venture capital investment and teamed up with San Jose State University to offer courses valid for college credit. But within two years of Udacity's launch, Thrun began to question whether MOOCs could make much of a mark on the world in their current form.

Harvard University researchers have some bad news for Pokemon Go enthusiasts: The time you spend hunting digital monsters doesn't qualify as a workout. After analyzing the movements of actual players, the researchers calculated that Pokemon Go encouraged people to take an average of 955 extra steps per day in the first week after downloading the game. Assuming that each step covered 31.5 inches and that players walked at a pace of 2.5 miles per hour, the game prompted people to walk for 11 extra minutes each day. The World Health Organization recommends that adults get at least 150 minutes of aerobic physical activity each week. If you spread that equally throughout the week, you'll need to get 21.4 minutes of exercise each day -- twice as much as Pokemon Go provides.

In our recent reviews of historical Watson and the modern Watson of today we concluded that IBM's Watson Group may have the first or at least the current strongest comprehensive AI platform. This is the first time that we know of that all three elements of AI have been brought together in a single user friendly platform: image processing, text and speech processing, and knowledge retrieval. This is not so much a platform for data scientist to use to expand the capabilities of AI as it is a platform for business users (with the aid of data scientists) to exploit the capabilities of modern AI by building new products and services. To wrap up this review of Watson, we wanted to provide some thought-starters on what new services or even new businesses you might build on Watson. Oh, and regarding new businesses, did we mention that developers who join the Watson Ecosystem are eligible to become a Watson "partner" with a shot at the $100 Million funding IBM is making available to startups plus support and access from IBM business and technology advisors.

Here I offer a few off-the-beaten-path interesting problems that you won't find in textbooks, data science camps, or in college classes. These problems range from applied maths, to statistics and computer science, and are aimed at getting the novice interested in a few core subjects that most data scientists master. The problems are described in simple English and don't require math / stats / probability knowledge beyond high school level. My goal is to attract people interested in data science, but who are somewhat concerned by the depth and volume of (in my opinion) unnecessary mathematics included in many curricula. I believe that successful data science can be engineered and deployed by scientists coming from other disciplines, who do not necessarily have a deep analytical background yet are familiar with data.