Artificial Intelligence/Machine Learning field is getting a lot of attention right now, and knowing where to start can be a little difficult. I've been dabbling in this field, so I thought of curating the best resources in one place. All of these are curated based on if it's an inspiring read or a valuable resource. I hope this curated list help you get started on what you need to know about AI/Machine Learning on a technical level. Design intelligent agents to solve real-world problems including, search, games, machine learning, logic, and constraint satisfaction problems.

Machine learning is a type of artificial intelligence that uses algorithms to iteratively learn from data and finds hidden insights in data without being explicitly programmed where to look or how to find the answer. Here at SAS, we hear questions every day about machine learning: what it is, how it compares to other technologies, and why it matters. On the more technical end of the spectrum, we also hear questions about specific machine learning algorithms and how to use them. You will get all of your answers -- and more -- if you take the time to read the articles listed here, from definitions and comparisons to how-to tips on data science techniques. This isn't a comprehensive list, of course, but I tried to pick some favorite articles I've seen published over the last year, and I'm trying to list them here in order from introductory to more advanced.

In his regular courses at Georgia Tech, the computer science professor had at most a few dozen students. But his online class had 400 students -- students based all over the world; students who viewed his class videos at different times; students with questions. Maybe 10,000 questions over the course of a semester, Goel says. It was more than he and his small staff of teaching assistants could handle. "We were going nuts trying to answer all these questions," he says.

Teachers should use teaching methods that utilize technology in the most efficient way. During action research a motivating learning environment was developed, with a digital learning game and the flipped classroom pedagogy. The target group was first year pupils in a primary school. There were seventeen pupils in the class: nine of them were girls and eight were boys. The experiment was held during five weeks and there was one lesson per week.

What is the best way to start learning machine learning and deep learning without taking any online courses? Let me first start off by saying that there is no single "best way" to learn machine learning, and you should find a system that works well for you. Some people prefer the structure of courses, others like reading books at their own pace, and some want to dive right into code. I started with Andrew Ng's Machine Learning Coursera course in 2012, knowing almost zero linear algebra and nothing about statistics or machine learning. Note that although the class covered neural networks, it was not a course on Deep Learning.

The Fourth Industrial Revolution is upon us, even with the Third is still in progress. Big Data, Machine Learning and Artificial Intelligence are three of the driving forces behind it. While the term'Industrial Revolution' has always applied mainly to manufacturing, it now also involves service industries such as banking and insurance, who are investing heavily in Big Data to help them model credit risk, fraud, marketing success and other key data. Meanwhile manufacturing, retail, telco, pharma and many other sectors constantly need people skilled in building, analysing, monitoring and maintaining data models to gain strategic intelligence that helps them inform and adapt their key business processes. A leader in the world of Data Analytics is the SAS Institute, whose flagship product is SAS (Statistical Analysis System).

As technology grows, so do the options for gathering and sharing information. Because while finding cat gifs or funny memes on the internet is fun, there are also scholarly dissertations on a variety of subjects, public domain novels and classics, and up-to-date scientific information. This, in combination with advances in personal computing in both applications and general hardware, means that there are a host of new tools and methods that can be used to improve student learning outcomes. The open source movement is a technology in itself. The trend has already started with open-source education options available from high-profile universities and the Khan Academy.

Competition amongst universities is set to increase with institutions closely differentiating themselves to attract and retain the best quality students, academics and staff. Key to this differentiation will be an extensive technology adoption and innovation strategy, enhancing the student experience, delivery of learning content, community engagement and campus management. The education technology (Edutech) market in Australia is expected to grow significantly amidst increasing student demand for education services and technology innovation, competition amongst institutions and decreasing acquisition costs. Frost & Sullivan anticipates that as the learning experience becomes increasingly digitised, technologies and solutions incorporating big data and analytics, collaboration, Augmented / Virtual Reality technology, Artificial Intelligence and learning management systems will play a key role within universities in the coming years. Frost & Sullivan's most recent analysis, Australian Edutech Market: Key Trends, Technologies and Opportunities 2016-2022 finds that the Australian Edutech Market is expected to grow to AUD 1.7 Billion by 2022.

A few months ago I wrote a tutorial on how to classify images using Convolutional Neural Networks (specifically, VGG16) pre-trained on the ImageNet dataset with Python and the Keras deep learning library. The pre-trained networks inside of Keras are capable of recognizing 1,000 different object categories, similar to objects we encounter in our day-to-day lives with high accuracy. Back then, the pre-trained ImageNet models were separate from the core Keras library, requiring us to clone a free-standing GitHub repo and then manually copy the code into our projects. This solution worked well enough; however, since my original blog post was published, the pre-trained networks (VGG16, VGG19, ResNet50, Inception V3, and Xception) have been fully integrated into the Keras core (no need to clone down a separate repo anymore) -- these implementations can be found inside the applications sub-module. Because of this, I've decided to create a new, updated tutorial that demonstrates how to utilize these state-of-the-art networks in your own classification projects.

As a business technology professional, you need to manage your company's information resources 24x7 while juggling concurrent projects and staying up to speed on changes in the technology and in your chosen field. You're stretched thin but continue to seek out professional learning opportunities that you can fit into your crowded schedule. That's where IBM InterConnect 2017, 19-23 March 2017, at Mandalay Bay in Las Vegas, Nevada, can be just the experience for you. This annual conference is your opportunity to learn about new methodologies, tools and practices for data engineering in the era of cloud data services, cognitive computing, data science and big data analytics. Here are the top five reasons why your peers are registering for IBM InterConnect 2017. At IBM InterConnect 2017, you can learn to advance your business through cognitive technologies.