Data Science


r/MachineLearning - [R] Machine Learning Reproducibility Challenges and DVC

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When ML models need to be regularly updated in production, a host of challenges emerges. No one tool can do it all for you - organizations using a mix of Git, Makefiles, ad hoc scripts and reference files for reproducibility.


Data Analytics Performance Gap Ruins CX in Banking

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The mission of building one-to-one communication and engagement is not a new concept. Back in 1993, Don Peppers and Martha Rogers, Ph.D., proposed that organizations could use technology to gather information about, and to communicate directly with, individuals to form a personal bond. The book, The One to One Future: Building Relationships One Customer at a Time, stated that technology had made it possible and affordable to track individual consumers, to understand each person's individual journey, and to provide contextual offers at the optimal time of need. Six years later, internationally recognized best-selling author Seth Godin published Permission Marketing. He built a logical case for creating incentives for consumers to accept advertising voluntarily.


Adopt Artificial Intelligence to improve operational efficiency in financial services sector

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The explosion of emerging technologies such as artificial intelligence (AI) is dramatically changing the way businesses operate today. As businesses collect more and more data, the need for solutions to drive true value from that data grows in importance. AI, in conjunction with big data and analytics, can deliver that baseline value and go beyond traditional solutions to find deeper insights. In India, banks are fast moving in this direction and deploying AI-powered chatbots for their operations to gain better insights into their customers' usage patterns, offer customised products, help in detecting fraudulent transactions and improving operational efficiency amongst others. There is no denying that AI helps banks nurture their relationships through better interactions with their customers however, not without challenges.


How Big Data And Machine Learning Can Predict, Prevent Isolated Cases Of Disease

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Measles, once thought to have been eliminated in the U.S., is popping up in isolated outbreaks as a result of skipped well-child visits and parents' fears that the measles-mumps-rubella (MMR) vaccine is linked to autism. Though some 350 measles cases occurred in 15 states in the first three months of 2019, more than half were in Brooklyn, N.Y., and nearby Rockland County, N.Y., where large religious communities have adopted anti-vaccine positions. Rockland County responded by pulling 6,000 unvaccinated children out of schools and barring them from public places. The county's actions were effective; in just a few months, 17,500 doses of MMR were administered to area children. Yet, wouldn't it have been better to contain the outbreak before it got started?


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.