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Machine Learning


Best practices to build data literacy into your Gen Z workforce - Data Dreamer

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This is a guest post by Kirk Borne, Ph.D., Chief Science Officer at DataPrime.ai, Kirk is also a consultant, astrophysicist, data scientist, blogger, data literacy advocate and renowned speaker, and is one of the most recognized names in the industry. A survey of 1,100 data practitioners and business leaders reported that 84% of organizations consider data literacy to be a core business skill, agreeing with the statement that the inability of the workforce to use and analyze data effectively can hamper their business success. In addition, 36% said data literacy is crucial to future-proofing their business. Another survey found that 75% of employees are not comfortable using data.


Effect of Unbalanced and Mixed Dataset on ML Model

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Machine learning requires creating a robust training dataset since the training performs as the seed for subsequent model evaluation. If the training data is corrupted, the model will perform badly…


Researchers use AI to predict risk of developing type 2 diabetes

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Artificial intelligence could be used to predict who is at risk of developing type 2 diabetes – information that could be used to improve the lives of millions of Canadians. Researchers at the University of Toronto used a machine learning model to analyze health data, collected between 2006 to 2016, of 2.1 million people living in Ontario. They found that they were able to use the model to accurately predict the number of people who would develop type 2 diabetes within a five-year time period. The machine learning model was also able to analyze different factors that would influence whether people were high or low risk to develop the disease. The results of the study were recently published in the journal JAMA Network Open.


Deadline 2024: Why you only have 3 years left to adopt AI

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While AI can be traced to the mid-1950s and machine learning dates back to the late 1970s, the concept of deep learning was popularized by the …


Enhanced Crop Forecasting for Ethanol Producers

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The combination of data-rich remote sensing and new machine learning approaches is creating a revolution in yield forecasting, with accurate and …


Machine Learning Market In-Depth Analysis including key players IBM, Microsoft Corporation, SAP …

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This Machine Learning report will surely act as a handy instrument for the market participants to develop effective strategies with an aim to reinforce their …


Artificial intelligence

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The intelligence demonstrated by machines is known as artificial intelligence. Artificial intelligence makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Using these, machines can be designed to accomplish different tasks in different fields. Email filters, digital calls, data analysis are all examples of NLP. Machines can accurately identify and locate objects then react to what they "see" using digital images from cameras, videos, and deep learning models.


Beyond first-order methods in ML Systems

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Description: In the last few decades, much effort has been devoted to the development of first-order methods. These methods enjoy a low per-iteration cost and have optimal complexity, are easy to implement, and have proven to be effective for most machine learning applications. In contrast, higher-order methods, such as Newton, quasi-Newton and adaptive gradient descent methods, are extensively used in many scientific and engineering domains. At least in theory, these methods possess several nice features: they exploit local curvature information to mitigate the effects of ill-conditioning, they avoid or diminish the need for hyper-parameter tuning, and they have enough concurrency to take advantage of distributed computing environments. However, often higher-order methods are "undervalued."


The Hitchhiker's Guide to Responsible Machine Learning

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Yesterday Olga Tokarczuk (2018 Nobel Prize in Literature) said in an interview that when she thinks about literature, she no longer thinks about books!!! So, how should we effectively tell the most important story in predictive modelling i.e. We (MI2DataLab) are currently working on an exciting and interdisciplinary experiment combining a classic textbook with a comic book, combining a description of methods and software with a description of process, combining a description of a specific use-case about COVID-19 data analysis with universal best practices. These 52 page long teaching materials describe how to build a predictive model, compare the developed models, and use XAI to analyze them, plus a bonus -- how to deploy model with explanations in a similar form to https://crs19.pl/. The material is prepared as a starter for predictive modelling. The included code examples can be executed and experimented with on your own (the first version has examples in R, but there will be albo translation for Python).


Making Friends with Machine Learning

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Making Friends with Machine Learning was an internal-only Google course specially created to inspire beginners and amuse experts.* It is one of Google's best-loved educational offerings of all time. Curious to know what's in there? The course is designed to give everyone -- no matter your role -- the tools you need for effective participation in machine learning for solving business problems and for being a good citizen in an increasingly AI-fueled world. MFML is perfect for humans of all stripes; it focuses on conceptual understanding (rather than the mathematical and programming details) and guides you through the ideas that form the basis of successful approaches to machine learning.