In this tutorial we will begin by laying out a problem and then proceed to show a simple solution to it using a Machine Learning technique called a Naive Bayes Classifier. This tutorial requires a little bit of programming and statistics experience, but no prior Machine Learning experience is required. You work as a software engineer at a company which provides email services to millions of people. Lately, spam has a been a major problem and has caused your customers to leave. Your current spam filter only filters out emails that have been previously marked as spam by your customers.

If you're a software engineer (or someone who's learning the craft), chances are that you've heard about deep learning (which we'll sometimes abbreviate as "DL"). It's an interesting and rapidly developing field of research that's now being used in industry to address a wide range of problems, from image classification and handwriting recognition, to machine translation and, infamously, beating the world champion Go player in four games out of five. A lot of people think you need a PhD or tons of experience to get a job in deep learning, but if you're already a decent engineer, you can pick up the requisite skills and techniques pretty quickly. Important point: You need motivation and the ability to code and problem solve well. Here at Deepgram we're using deep learning to tackle the problem of speech search.

The trend towards digital transformation of the enterprise, regardless of industry or sector, will accelerate in 2017 from the already significant levels seen last year. We identify the following seven digital technology trends as game changers for software-driven enterprises. The crumbling of the barriers to entry for machine intelligence – driven by the availability of high-quality open-source software components; cloud platforms from all major providers; and the availability of wildly popular and high-quality introductory courses on MOOC platforms – will drive growing mainstream adoption of machine intelligence as a differentiating and foundational technology layer in the digital transformation stack for identifying and closing new revenue opportunities, customizing user experience, driving operational efficiencies, and predicting failures. Also expect to see acceptance and greater adoption of advanced machine learning techniques for delivering closed-loop actionable insights in domains such as the Industrial Internet of Things and cybersecurity. The fully distributed, transparent, tamper-resistant, and auditable shared ledger technology known as blockchain is particularly powerful in settings where multiple parties need to reconcile without a central intermediary, or need to track provenance of assets across organizational boundaries, or need to establish and enforce contracts between untrusting parties and speed up reconciliation with a secure and verifiable audit trail.

How to Cut Cake Fairly.

This webinar focuses on demonstrating how the Data Science Virtual Machine (DSVM) in Microsoft Azure conveniently enables key end-to-end data analytics scenarios by providing users immediate access to a collection of the top data science and development tools of the industry, completely pre-configured, with worked out examples and sample code. We will do a detailed demonstration of some key capabilities of the DSVM by working through a selection of popular scenarios using technologies that are enabled by it. These examples encompass areas such as using a local Spark environment for easy test and development, training and scoring for deep-learning on GPU based instances of the DSVM, cross-platform data exploration and querying using Apache Drill, and in-database analytics using SQL Server 2016 R Services. Both the Windows and Linux flavors of the VMs are covered in this webinar.

March 29, 2017 -- Interop ITX, the independent conference for tech leaders, today announced new artificial intelligence (AI) components created to further the education of its IT community. This year's event will explore both the education and practical application of AI through the all new Data & Analytics track, AI Theater and Demo Showcase, and The Future of Data Summit. AI content will explore a range of areas from business strategy and case studies, to specific tasks and application usage. Interop ITX will take place May 15 – 19, 2017 at the MGM Grand in Las Vegas, NV. For more information and to register, please visit: interop.com/.

The other day, I went to meet someone in downtown Sydney, Australia. On my way, back on the local train, I looked at my mobile to check my emails and found a message asking me whether I would like to meet the person I had just connected with on my LinkedIn network. So, was this some form of artificial intelligence (AI) at play? We now live in a brave new world where AI is the next frontier. We keep hearing about bots, chatbots, teacherbots, digital assistants, machine learning, deep learning and many more such words and often wonder what do they mean.

Data science and machine learning are some of the top buzzwords in the technical world today. A resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. This book is your entry point to machine learning. This book starts with an introduction to machine learning and the Python language and shows you how to complete the setup. Moving ahead, you will learn all the important concepts such as getting data, exploratory data analysis, regression algorithms, and more with the help of various projects included in the book.

They were also discussed in 2015 in the Computing at School (CAS) framework and guide for teachers to enable teachers in the U.K. to incorporate computational thinking into their teaching work.10 CSTA/ISTE and CAS also provide pedagogical approaches to embed these capabilities across the curriculum in elementary and secondary classes. For example, CSTA/ISTE describes how the nine core computational thinking concepts and capabilities could be practiced in science classrooms by collecting and analyzing data from experiments (data collection and data analysis) and summarizing that data (data representation). Computational thinking is often mistakenly equated with using computer technology. Algorithms are central to both computer science and computational thinking.

At the start of the year, I was part of a panel that aimed to offer career guidance to graduating students at a local secondary school in Singapore. The panel dished out the usual advice about the need to have passion, work hard, and always strive to do better. A student then posed a question that struck a chord: "How can I ensure the skills I learn in school will not be obsolete by the time I enter the workforce?" These students were no older than 16 or 17 years and, if they took the typical route to university, would not begin life as working adults for at least another five to seven years. With technology changing so rapidly these days, it might very well be possible their course modules would no longer be relevant by the time they graduated.