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Create Machine Learning Models in Microsoft Azure

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Machine learning is the foundation for predictive modeling and artificial intelligence. If you want to learn about both the underlying concepts and how to get into building models with the most common machine learning tools this path is for you. In this course, you will learn the core principles of machine learning and how to use common tools and frameworks to train, evaluate, and use machine learning models. This course is designed to prepare you for roles that include planning and creating a suitable working environment for data science workloads on Azure. You will learn how to run data experiments and train predictive models. In addition, you will manage, optimize, and deploy machine learning models into production.


AI & ML Cloud Deployment For Beginners

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Want To Know How to deploy powerful ML solutions on the cloud? This program is designed for the AI & ML professional who wants to excel in Deep learning, Computer vision, Data Mining, computer vision, Image processing, and more using cloud technologies. This program gives you in-depth knowledge on how to use Azure Machine Learning Designer using Microsoft Azure and build AI models. You can also learn the computer vision workloads and custom vision services using Microsoft Azure through this program. Learn essential to advanced topics like image analysis, face service, form recognizer, and optical character recognizer using Microsoft Azure.


How can artificial intelligence understand time and space?

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Time and space are fundamental to the existence of the universe, and human intelligence is our tool for navigating time and space in an appropriate manner. Our ability to see the future is critical. Through evolution, the human brain has evolved into a tool that perceives not only time, place, and things, but our neural network also predicts what will happen in the near future. What kind of path will the stone that you throw take? In which direction does the tree fall?


Machine learning radically reduces workload of cell counting for disease diagnosis

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The use of machine learning to perform blood cell counts for diagnosis of disease instead of expensive and often less accurate cell analyzer machines has nevertheless been very labor-intensive as it takes an enormous amount of manual annotation work by humans in the training of the machine learning model. However, researchers at Benihang University have developed a new training method that automates much of this activity. Their new training scheme is described in a paper published in the journal Cyborg and Bionic Systems on April 9. The number and type of cells in the blood often play a crucial role in disease diagnosis, but the cell analysis techniques commonly used to perform such counting of blood cells--involving the detection and measurement of physical and chemical characteristics of cells suspended in fluid--are expensive and require complex preparations. Worse still, the accuracy of cell analyzer machines is only about 90 percent due to various influences such as temperature, pH, voltage, and magnetic field that can confuse the equipment.


MIT, Harvard scientists find AI can recognize race from X-rays -- and nobody knows how - The Boston Globe

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A doctor can't tell if somebody is Black, Asian, or white, just by looking at their X-rays. The study found that an artificial intelligence program trained to read X-rays and CT scans could predict a person's race with 90 percent accuracy. But the scientists who conducted the study say they have no idea how the computer figures it out. "When my graduate students showed me some of the results that were in this paper, I actually thought it must be a mistake," said Marzyeh Ghassemi, an MIT assistant professor of electrical engineering and computer science, and coauthor of the paper, which was published Wednesday in the medical journal The Lancet Digital Health. "I honestly thought my students were crazy when they told me."


Advanced Data Science with IBM

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Apache Spark is the de-facto standard for large scale data processing. This is the first course of a series of courses towards the IBM Advanced Data Science Specialization. We strongly believe that is is crucial for success to start learning a scalable data science platform since memory and CPU constraints are to most limiting factors when it comes to building advanced machine learning models. In this course we teach you the fundamentals of Apache Spark using python and pyspark. We'll introduce Apache Spark in the first two weeks and learn how to apply it to compute basic exploratory and data pre-processing tasks in the last two weeks.


Detect the Age and Gender of a Face using OpenCV - Geeky Humans

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In this tutorial, we are going to quickly go over how you can detect the age and gender of a face using OpenCV. In computer vision, detecting a face is a very important task. In the past, detecting a face required a lot of time and effort, but today we have pre-trained models that can do it in a few seconds. We will be using a pre-trained model in the OpenCV library to detect a face and return a ground truth label. OpenCV: It is a tool that specializes in the areas of image processing, video analysis, or computer vision. OpenCV can be used to help developers solve lots of problems in your field when it comes down to analyzing images and videos through sophisticated digital algorithms.


Teach yourself data science at your own pace for less than $40

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The following content is brought to you by ZDNet partners. If you buy a product featured here, we may earn an affiliate commission or other compensation. Artificial intelligence (AI) has become so commonplace that it's easy to forget it was once a science fiction pipe dream. But AI and the machine learning concepts behind it are still new enough that programmers and data scientists will be in demand for the foreseeable future. So if you want to pursue a career in one of the fields where data science know-how is essential, this e-learning bundle can serve as a great first step.


Advanced Reinforcement Learning: policy gradient methods

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Sample efficiency for policy gradient methods is pretty poor. We throw out each batch of data immediately after just one gradient step. This is the most complete Reinforcement Learning course series on Udemy. In it, you will learn to implement some of the most powerful Deep Reinforcement Learning algorithms in Python using PyTorch and PyTorch lightning. You will implement from scratch adaptive algorithms that solve control tasks based on experience.


Interview with Daniel Shearly, VP of Products at GfK

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Daniel Shearly answers questions about data-centricity, trusted data, and how data-driven intelligence can help in better decision-making for future-proofing businesses. What specific trends do you see that will shape the future of its adoption? Every organization has its own reasons for adopting AI which have historically ranged from a real desire to answer complex problems and uncover insights from large data sets that would be impossible to process through traditional statistical methods to driving operational efficiencies and even just a company's desire to appear cutting edge. As AI is now much more mainstream it's less of a buzzword and badge of innovation and more of something to be applied practically to real-world problems. The main trends I see emerging are the broader adoption of it by companies big and small as knowledge and talent build and barriers to adoption decrease.