In less than five years, artificial intelligence -- AI, as it's commonly known -- has gone from the stuff of science fiction to the forefront of the news, from scientific journals to the strategic plans of the world's biggest companies. It's a coming sea change, one that will disrupt entire parts of our lives by competing with what, until now, was seen as a fundamentally human characteristic: Our intelligence. At the same time, he acknowledges that AI -- at least for now -- is "still utterly unintelligent". Recognising a cat in a picture, a sentence dictated to a smartphone or even a tumour in MRI images is an impressive feat. But this prowess is still limited to very specific uses, Alexandre argues.

Since it launched in 2009, the Kepler Space Telescope has done a good job of collecting data -- too good for human analysis alone. The telescope has produced 14 billion data points about 200,000 stars. It has also amassed 35,000 signals indicating possible planets. People alone would not have been able to keep up. Since it launched in 2009, the Kepler Space Telescope has done a good job of collecting data -- too good for human analysis alone.

This post was written by Ofir Press. Ofir is a graduate student at Tel-Aviv University's Deep Learning Lab. His main focus is on using deep learning for natural language processing. "Due to the recent achievements of artificial neural networks across many different tasks (such as face recognition, object detection and Go), deep learning has become extremely popular. This post aims to be a starting point for those interested in learning more about it. If you already have a basic understanding of linear algebra, calculus, probability and programming: I recommend starting with Stanford's CS231n. The course notes are comprehensive and well-written. The slides for each lesson are also available, and even though the accompanying videos were removed from the official site, re-uploads are quite easy to find online. If you don't have the relevant math background: There is an incredible amount of free material online that can be used to learn the required math knowledge. Gilbert Strang's course on linear algebra is a great introduction to the field. For the other subjects, edX has courses from MIT on both calculus and probability. If you are interested in learning more about machine learning: Andrew Ng's Coursera class is a popular choice as a first class in machine learning. There are other great options available such as Yaser Abu-Mostafa's machine learning course which focuses much more on theory than the Coursera class but it is still relevant for beginners. Knowledge in machine learning isn't really a prerequisite to learning deep learning, but it does help. In addition, learning classical machine learning and not only deep learning is important because it provides a theoretical background and because deep learning isn't always the correct solution. Geoffrey Hinton's Coursera class "Neural Networks for Machine Learn... covers a lot of different topics, and so does Hugo Larochelle's "Neural Networks Class".

Democratization of content: In 2018, we will see more professionals outside of the training team contributing in creation of content. The concept of crowd sourcing will evolve in a big way with solution architects, professional services, and support organizations creating the content. Customers and partners will also contribute content as they implement the cloud computing solutions and identify new use cases and design and implementation best practices. This trend will see the role of training organizations evolving. Training teams will act more as a strategy and content curation arm providing tools and templates to the subject matter experts to develop the content and then curating the content.

This online course covers big data analytics stages using machine learning and predictive analytics. Big data and predictive analytics is one of the most popular applications of machine learning and is foundational to getting deeper insights from data. Starting off, this course will cover machine learning algorithms, supervised learning, data planning, data cleaning, data visualization, models, and more. This self paced series is perfect if you are pursuing an online computer science degree, online data science degree, online artificial intelligence degree, or if you just want to get more machine learning experience. Check out the entire series here: https://www.youtube.com/playlist?list... Support me! http://www.patreon.com/calebcurry

These days, I am sure 90% of LinkedIn traffic contains one of these terms: DS, ML or DL -- acronyms for Data Science, Machine Learning or Deep Learning. Beware of the cliche though: "80% of all the statistics are made on the spot". If you blinked on these acronyms perhaps you need to google a bit and then continue reading the rest of this post. This post has 2 goals. First, it attempts to put all the fellow Data Science learners at ease.

About this course: Learn how advances in geospatial technology and analytical methods have changed how we do everything, and discover how to make maps and analyze geographic patterns using the latest tools. The past decade has seen an explosion of new mechanisms for understanding and using location information in widely-accessible technologies. This Geospatial Revolution has resulted in the development of consumer GPS tools, interactive web maps, and location-aware mobile devices. These radical advances are making it possible for people from all walks of life to use, collect, and understand spatial information like never before. This course brings together core concepts in cartography, geographic information systems, and spatial thinking with real-world examples to provide the fundamentals necessary to engage with Geography beyond the surface-level.

About the course - Cluster analysis is one of the most popular techniques used in data mining for marketing needs. The idea behind cluster analysis is to find natural groups within data in such a way that each element in the group is as similar to each other as possible. At the same time, the groups are as dissimilar to other groups as possible. Course materials- The course contains video presentations (power point presentations with voice), pdf, excel work book and sas codes. Course duration- The course should take roughly 10 hours to understand and internalize the concepts.

Getting up from the toilet after a satisfying bowel movement, you walk right over to the sink and start washing your hands. "Alexa, flush my toilet," you say while reaching for your toothbrush. Your mirror starts displaying your schedule for the day, the weather update and latest news. Suddenly, there's a ping -- your toilet has detected an anomaly in your stool and recommends you increase your fiber intake. A dispenser built into your medicine cabinet whirs and spits out a fiber supplement.

Well, if you are even remotely related to the technological field, chances are you may be aware of the world going gaga over these buzzwords. Though these different buzzwords are being used in a multitude of diverse application areas, at the core, they all mean the same thing -- making sense of vast amounts of data in a way that would give out some intelligence to act upon. Although Machine Learning has now gained prominence owing to the exponential rate of data generation and technological advancements to support it, its roots lie way back in 17th century. People have been attempting to make sense of data and processing it to gain quick insights since ages. Let me take you through an interesting journey down the history of Machine Learning -- how it all began and how did it come to what it is today.