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Which Machine Learning Algorithm Should I Use?

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This resource is designed primarily for beginner to intermediate data scientists or analysts who are interested in identifying and applying machine learning algorithms to address the problems of their interest. The machine learning algorithm cheat sheet helps you to choose from a variety of machine learning algorithms to find the appropriate algorithm for your specific problems. Since the cheat sheet is designed for beginner data scientists and analysts, we will make some simplified assumptions when talking about the algorithms. The algorithms recommended here result from compiled feedback and tips from several data scientists and machine learning experts and developers.


Artificial intelligence on Hadoop: Does it make sense? ZDNet

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This week MapR announced a new solution called Quick Start Solution (QSS), focusing on deep learning applications. MapR touts QSS as a distributed deep learning (DL) product and services offering that enables the training of complex deep learning algorithms at scale. Ted Dunning, MapR chief application architect, explains: "The best approach for pursuing AI/Deep learning is to deploy a scalable converged data platform that supports the latest deep learning technologies with an underlying enterprise data fabric with virtually limitless scale." But being able to run ML or DL on Hadoop does not really make a Hadoop vendor an AI vendor too.


Top April Stories: 10 Free Must-Read Books for Machine Learning and Data Science

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Most Viewed and Most Shared - Platinum Badge ( 20,000 UPV AND 2,000 shares) 10 Free Must-Read Books for Machine Learning and Data Science, by Matthew Mayo Most Viewed - Gold Badges ( 10,000 UPV) Forrester vs Gartner on Data Science Platforms and Machine Learning Solutions, by Gregory Piatetsky Top 20 Recent Research Papers on Machine Learning and Deep Learning, by Thuy Pham Most Viewed - Silver Badges ( 5,000 unique PV) Awesome Deep Learning: Most Cited Deep Learning Papers, by Terry Taewoong Um 5 Machine Learning Projects You Can No Longer Overlook, April, by Matthew Mayo Keep it simple! How to understand Gradient Descent algorithm, by Jahnavi Mahanta Top mistakes data scientists make when dealing with business people, by Karolis Urbonas (new) New Online Data Science Tracks for 2017, by Brendan Martin (new) Cartoon: Machine Learning - What They Think I Do, by Harrison Kinsley Data Science for the Layman (No Math Added), Annalyn Ng and Kenneth Soo Most Shared - Gold Badges ( 1,000 shares) Forrester vs Gartner on Data Science Platforms and Machine Learning Solutions, by Gregory Piatetsky Top 20 Recent Research Papers on Machine Learning and Deep Learning, by Thuy Pham Top mistakes data scientists make when dealing with business people, by Karolis Urbonas (new) Awesome Deep Learning: Most Cited Deep Learning Papers, by Terry Taewoong Um Most Shared - Silver Gold Badges ( 500 shares) Keep it simple! How to understand Gradient Descent algorithm A Brief History of Artificial Intelligence, By Francesco Corea The 42 V's of Big Data and Data Science, by Tom Shafer (new) Data Science for the Layman (No Math Added) Deep Stubborn Networks - A Breakthrough Advance Towards Adversarial Machine Intelligence, by Matthew Mayo (new) Cartoon: Machine Learning - What They Think I Do Awesome Deep Learning: Most Cited Deep Learning Papers, by Terry Taewoong Um 5 Machine Learning Projects You Can No Longer Overlook, April, by Matthew Mayo Keep it simple! How to understand Gradient Descent algorithm A Brief History of Artificial Intelligence, By Francesco Corea The 42 V's of Big Data and Data Science, by Tom Shafer (new) Data Science for the Layman (No Math Added) Deep Stubborn Networks - A Breakthrough Advance Towards Adversarial Machine Intelligence, by Matthew Mayo (new) Cartoon: Machine Learning - What They Think I Do


5 Best Machine Learning APIs for Data Science

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There are many companies like Google, IBM, Amazon, and Microsoft helping businesses process big data by building Machine Learning APIs so that organizations can make the best use of the machine learning technology. Machine Learning is the big frontier in big data innovation but it is daunting for people who are not tech geeks or data science domain experts.Similar to how standard APIs help developers create applications, Machine Learning APIs make machine learning easy to use, for everyone. Machine Learning APIs provide businesses with the ability to bring together predictive analytics so that they can get to know their customers better, understand their requirements and deliver products or services based on the past data trends, thereby initiating the selling process.There is an increasing percentage of real time consumer interactions through Machine Learning APIs – making them an ideal option for exposing real time predictive analytics to app developers. Azure Machine Learning makes it easy for data scientists to use predictive models in IoT applications by providing APIs for fraud detection, text analytics, recommendation systems and several other business scenarios.



Artificial Intelligence Applications: Revolutionizing Data Management - DATAVERSITY

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In the last decade, Data Management personnel solved business problems with data; in the next decade, highly capable machines using Artificial Intelligence Applications will solve problems with available data in a scale unheard before. As the algorithm economy continues to gain momentum among global businesses, the challenges facing Deep Learning are still real. Big Data going mainstream may successfully help combat the Data Management issues making Big Data and Deep Learning the formidable combination for unlocking any complex data handling problem. How Artificial Intelligence is Revolutionizing IT Operation Analytics companies are already leveraging AI-powered Operations Analytics to optimize real-time business operations with "unprecedented granularity, preciseness, and impact."


Avoiding industrial IoT digital exhaust with machine learning - IoT Agenda

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With the Industry 4.0 factory automation trend catching on, data-driven artificial intelligence promises to create cyber-physical systems that learn as they grow, predict failures before they impact performance, and connect factories and supply chains more efficiently than we could ever have imagined. To avoid IIoT digital exhaust and preserve the potential latent value of IIoT data, enterprises need to develop long-term IIoT data retention and governance policies that will ensure they can evolve and enrich their IoT value proposition over time and harness IIoT data as a strategic asset. A practical compromise IoT architecture must first employ some centralized (cloud) aggregation and processing of raw IoT sensor data for training useful machine learning models, followed by far-edge execution and refinement of those models. A multi-tiered architecture (involving far-edge, private cloud and public cloud) can provide an excellent balance between local responsiveness and consolidated machine learning, while maintaining privacy for proprietary data sets.


How Big Data Is Empowering AI and Machine Learning at Scale

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As big data initiatives mature, organizations are now combining the agility of big data processes with the scale of artificial intelligence (AI) capabilities to accelerate the delivery of business value. Whereas statisticians and early data scientists were often limited to working with "sample" sets of data, big data has enabled data scientists to access and work with massive sets of data without restriction. Johnson previously held positions as senior vice president for Strategic Technology with Mellon Bank and served as the executive vice president and chief technology officer of Cognitive Systems Inc. (CSI), an early artificial intelligence company specializing in natural language processing, expert systems, case-based reasoning, and data mining. Participants in the most recent executive breakfasts have included chief data officers, chief analytics officers, chief digital officers, chief technology officers, and heads of big data for firms including AIG, American Express, Blackrock, Charles Schwab, CitiGroup, General Electric (GE), MetLife, TD Ameritrade, VISA, and Wells Fargo, among others.


Why AI won't replace all human data analysts

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When most people think of artificial intelligence, they think of a coldly rational decision maker, lacking in emotion -- like Data, the fictional android from Star Trek. But as AI and machine learning have progressed, algorithms have become incredibly good at pattern recognition, and have started to act more biologically -- more like instincts based on experience than decisions based on logic. The work of an analyst, however, does not just involve conducting data analysis within closed environments. Like a manager, every human will have a task force of AI, pattern matching and conducting closed environment analysis.


Factoring Massive Numbers with Machine Learning Techniques

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Here you will also learn how data science techniques are applied to big data, including visualization, to derive insights. Here, we use techniques of big data, statistics, and machine learning - in short a data science approach - to hopefully discover new efficient factoring techniques for these massive numbers. The remarkable fact here is that convergence occurs in many cases (albeit rarely), sometimes in as little as 2 or 3 iterations, sometimes for a large number of starting points (the number s in step 1) despite the fact that we are dealing with highly chaotic structures that behave almost randomly - something very different from the classic fixed-point theorem. So, pattern recognition techniques can be used here to further optimize the algorithm, and for instance, to identify the optimal threshold for the maximum number of iterations allowed (here, the number 20 is arbitrary.).