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Feature Selection For Machine Learning - AI Summary

#artificialintelligence

Free Coupon Discount – Feature Selection for Machine Learning, From beginner to advanced Throughout this course you will learn a variety of techniques used worldwide for variable selection, gathered from data competition websites and white papers, blogs and forums, and from the instructor's experience as a Data Scientist. This course is therefore suitable for complete beginners in data science looking to learn how to go about to select features from a data set, as well as for intermediate and even advanced data scientists seeking to level up their skills. Throughout this course you will learn a variety of techniques used worldwide for variable selection, gathered from data competition websites and white papers, blogs and forums, and from the instructor's experience as a Data Scientist. This course is therefore suitable for complete beginners in data science looking to learn how to go about to select features from a data set, as well as for intermediate and even advanced data scientists seeking to level up their skills.


1 Artificial Intelligence Growth Stock to Buy Right Now

#artificialintelligence

The stock market is currently on the roughest losing streak since the start of the pandemic in 2020. The broad S&P 500 index is down 19% from its all-time high, putting it within a whisker of bear market territory. But the tech-centric Nasdaq-100 index is already there, with a loss of 28.3% since November 2021. While the investment picture might be nerve-wracking for many investors, history suggests down markets always eventually recover, so this might actually be a great time to put some money to work. Here's one fast-growing stock leveraging advanced technology, and it's worth considering because it's trading at an 88.9% discount to its all-time high, despite the company being highly profitable.


Why are we failing at the ethics of AI? A critical review

#artificialintelligence

Anja Kaspersen and Wendell Wallach are senior fellows at Carnegie Council for Ethics in International Affairs. In November 2021, they published an article that changed the AI ethics conversation: Why Are We Failing at the Ethics of AI? Six months later, the questions the article raised are no closer to resolution. This article was a don't-hold-your-punches review on the state of AI ethics, with which I am in almost complete agreement. If we want to advance the AI conversation, this is still a good place to start. I've quoted a portion of their article, with my comments interspersed: While it is clear that AI systems offer opportunities across various areas of life, what amounts to a responsible perspective on their ethics and governance is yet to be realized.


Help! My Mom Is Catfishing a Guy Online--By Pretending to Be Me.

Slate

Our advice columnists have heard it all over the years. Each Sunday, we dive into the Dear Prudie archives and share a selection of classic letters with our readers. For the past few months, my mom has been catfishing a guy online and I don't know what to do. Earlier this year, I decided to give online dating a try and signed up for a free online dating site. My mom was very supportive and interested in me finding someone, and, unbeknownst to me, created a fake profile to scope out the site.


CS229: Machine Learning - AI Summary

#artificialintelligence

CS229: Machine Learning Course Description This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs, practical advice); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing. Course Description This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs, practical advice); reinforcement learning and adaptive control.


Create Machine Learning Models in Microsoft Azure

#artificialintelligence

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

#artificialintelligence

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?

#artificialintelligence

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

#artificialintelligence

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

#artificialintelligence

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."