data mining


Case Study: How IoT and Bigdata Transform Sports Industry

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Handling and analyzing massive troves of unstructured data has become a strategic imperative for businesses in 2019, with the healthcare and sports industries being no exception. Emerging tech-enabled solutions can give fitness and other health-related companies a huge edge over competitors in terms of using Big Data analysis tools and introducing automated IoT devices across their employee and customer/patient base. New analysis from Accenture estimates that AI-driven applications can save up to $150 billion annually for the US healthcare industry by 2026. With these numbers, however, there exist some concerns among business owners and employees that can jeopardize the large-scale implementation and subsequent adoption of these new cognitive solutions. For instance, there are some groundless fears of massive job losses for people getting replaced by robots, a steep learning curve for both managers and customers, and suchlike.


Machine Learning & Data Science Masterclass in Python and R

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Regression, Classification and much more.HOT & NEW 4.8 (7 ratings) 161 students enrolled Created by Denis Panjuta What you'll learn Create machine learning applications in Python as well as R Apply Machine Learning to own data You will learn Machine Learning clearly and concisely Learn with real data: Many practical examples (spam filter, is fungus edible or poisonous etc. ...) No dry mathematics - everything explained vividly Use popular tools like Sklearn, and Caret You will know when to use which machine learning model This course contains over 200 lessons, quizzes, practical examples, ... - the easiest way if you want to learn Machine Learning. Step by step I teach you machine learning. In each section you will learn a new topic - first the idea / intuition behind it, and then the code in both Python and R. Machine Learning is only really fun when you evaluate real data. That's why you analyze a lot of practical examples in this course: Create machine learning applications in Python as well as R Apply Machine Learning to own data You will learn Machine Learning clearly and concisely Learn with real data: Many practical examples (spam filter, is fungus edible or poisonous etc. ...) No dry mathematics - everything explained vividly Use popular tools like Sklearn, and Caret You will know when to use which machine learning model Learn with real data: Many practical examples (spam filter, is fungus edible or poisonous etc. ...)


Augmented Analytics And Predefined Models As A Service: The Next Frontier In AI's Evolution

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As a recent article in the Wall Street Journal points out, artificial intelligence (AI) is becoming one of the most important technological advances of our era. It uses statistical methods and very large datasets to identify patterns and predict outcomes, but it still has a ways to go before it can identify cause-and-effect relationships. Being able to do this, however, just may represent the next frontier in AI. According to the Wall Street Journal article, determining causal relationships requires tried and true scientific, empirical and measurable methods that can "detect faint signals within large and/or noisy data sets -- the proverbial needle in a haystack." It's one thing to use statistical methods and very large data sets to find patterns that, for example, can identify the presence of a mass on an Xray, but it's another thing entirely to identify how a specific treatment will affect the outcome.


IBM Releases AI-Powered Anomaly Detection Capabilities to Mitigate Supply Chain Disruptions

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Gartner Supply Chain Executive Summit -- IBM (NYSE: IBM) today launched Business Transactional Intelligence (BTI), an AI-powered solution that offers anomaly detection and visualization capabilities for mitigating supply chain disruptions and accelerating data-driven decision making. BTI, part of IBM's Supply Chain Business Network, enables companies to garner deeper insights into supply chain data to help them better manage, for example, order-to-cash and purchase-to-pay interactions. The technology does this, in part, using machine learning to identify volume, velocity and value-pattern anomalies in supply chain documents and transactions. Machine learning is a method used to teach artificial intelligence how to learn from data, spot patterns and make decisions on its own. This enables companies to discover potential issues faster and resolve them before they escalate and impact the business.


Realizing the Benefits of Artificial Intelligence - Manufacturing Leadership Council

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It's always tempting to begin an article on AI with some form of science fiction analogy, but the truth is that the technology has been around almost as long as the genre! Some of the people reading this, like me, may remember the burst of excitement around AI in the 1980s and 90s. We've come a long way since then, and many facets of AI, especially machine learning, have become very mature. The increasing digital transformation happening within manufacturing is bringing the potential of AI into focus. International Data Corp., a technology research firm based in Framingham, MA, suggests that manufacturing companies are "at the heart of a perfect storm, both living with and seeking to exploit disruptive technologies such as cloud, big data, AI-assisted analytics and the Internet of Things (IoT), while facing increasing IT security challenges, regulatory pressures and a changing workforce".1 The explosion of big data and IoT is pivotal.


Global Big Data Conference

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Artificial Intelligence is a technology solution with the power to change the world. Once the sole property of sci-fi writers and creative minds, artificial intelligence is now an increasingly common part of our professional and personal lives. We make purchases through virtual assistants hooked up to our phones and speakers. Our bots remind us when we have appointments and help us to manage our schedule. In the business world, artificial intelligence even has the potential to take care of mundane tasks on our behalf, freeing up more time for us to be as intuitive and innovative as we like.


Mathematics for Data Science and Machine Learning using R

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From healthcare to business, everywhere data is important. However, it revolves around 3 major aspects i.e. data, foundational concepts and programming languages for interpreting the data. This course teaches you everything about all the foundational mathematics for Data Science using R programming language, a language developed specifically for performing statistics, data analytics and graphical modules in a better way. What you'll learn Master the fundamental mathematical concepts required for Datas Science and Machine Learning Learn to implement mathematical concepts using R Master Linear alzebra, Calculus and Vector calculus from ground up Master R programming language Udemy Promo Coupon 75% off Discount Mathematics for Data Science and Machine Learning using R


The Impact of AI on the Data Analyst - insideBIGDATA

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In this special guest feature, Glen Rabie, CEO of Yellowfin, believes that while many analysts may fear they will be replaced by automation and AI, the role of the data analyst will increase in significance to the business and breadth of skills required. Yellowfin is an Analytics and Business Intelligence software company focused on helping businesses understand their data. Rabie is passionate about data and improving business performance through analytics. Prior to starting Yellowfin, he worked in various roles at National Australia Bank including senior e-business consultant and global manager of employee self-service. Rabie holds a Masters in Commerce from the University of Melbourne.


What is The Role Of A Machine Learning Engineer?

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Machine learning engineers are the key role player in machine learning model development. They are responsible for multiple task from coding to deployment, testing and troubleshooting the issues comes while developing such models. However, to know what exactly machine learning engineer do you need to can find out their role, duties and actual task performed by such professionals. Sometimes Machine learning engineers also called data scientist as they study and transform the data science prototypes and algorithms while working on ML-based modles. Similarly, there are many other responsibilities machine learning engineers perform.


Machine Learning for Data Analysts -- BigQuery ML

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Data analytics involves analyzing raw data in order to extract valuable insights. These insights are frequently used as an aid in future decision making. The data that is analyzed is usually stored in one or more formats. These could be as flat files or text files (comma separated, tab separated), spreadsheets (Excel, Google Sheets), databases, and other formats. My first experience with Structured Query Language (SQL) was with an early version of MySql, sometime around the year 2000.