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Machine learning project : Salary prediction using python.

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If you want to become a machine learning professional, you'd have to gain experience using its technologies. The best way to do so is by completing projects. That's why in this article, we're sharing multiple machine learning projects in Python so you can quickly start testing your skills and gain valuable experience. However, before you begin, make sure that you're familiar with machine learning and its algorithm. If you haven't worked on a project before, don't worry because we have also shared a detailed tutorial on one project: The Iris dataset is easily one of the most popular machine learning projects in Python.



AI-assisted device could soon replace traditional stethoscopes

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Stethoscopes are among doctors' most important instruments, yet there have not been any essential improvements to the device since the 1960s. Now, researchers at Aalto University have developed a device that analyzes a broad range of bodily functions and offers physicians a probable diagnosis as well as suggestions for appropriate further examinations. The researchers believe that the new device could eventually replace the stethoscope and enable quicker and more precise diagnoses. A startup called Vital Signs is taking the device to the market. The researchers are currently testing the device in a clinical pilot trial.


Council Post: Is Decision Intelligence The New AI?

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Pascal Bornet is an expert in AI and Automation, best-selling author, keynote speaker, and CDO at Aera Technology. Decision intelligence is a new field that helps support, augment and automate business decisions by linking data with decisions and outcomes. It uses a combination of methods (e.g., decision mapping and decision theories) and technologies (e.g., machine learning and automation) to improve the way decisions are made in companies. Decision intelligence includes continually evaluating decision outcomes and optimizing them through a feedback system. The term "decision intelligence" was popularized in Lorien Pratt's 2019 book, Link: How Decision Intelligence Connects Data, Actions, and Outcomes for a Better World, after Google launched its decision intelligence department in 2018.


Monetize data, the most valuable asset of Machine Learning

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The data associated with machine learning can be extremely valuable, but, Kimberley Bayliss of Haseltine Lake Kempner writes in this co-edited article, before it can be monetized, there are some major issues to be resolved. One of the things I hear over and over again from inventors is that data is the most valuable asset in machine learning (ML). After all, an ML model is only as good as the quality and quantity of data on which it is trained. If data is really that valuable, the burning question is whether it can be successfully protected and monetized. Just as employees must be aware when they access a trade secret, and the responsibilities that come with it, employees must also be aware of their responsibilities when accessing and using company data.


Top Ten Open Source MLOPS Tools Every Software Developer Should Be Aware Of

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Given the ever-changing needs of ML projects, it is considered safe to use open source MLOps tools. ML models are easy to design when the only factor to consider is the ability to predict the outcome. Continuous learning, considered as the fundamental step towards artificial intelligence, is achieved by redesigning the ML models used for training. With millions upon millions of bytes of data involved and tasks spread across multiple computers, it becomes a futile chase when it comes time to debug or adapt changed parameters. To build scalability, flexibility, and retractability into an ML model, developers often opt for MLOps frameworks.


Probabilistic Graphical Models

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Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems. This course is the first in a sequence of three.


Using machine learning to derive different causes from the same symptoms

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Machine learning is playing an ever-increasing role in biomedical research. Scientists at the Technical University of Munich (TUM) have now developed a new method of using molecular data to extract subtypes of illnesses. In the future, this method can help to support the study of larger patient groups. Nowadays doctors define and diagnose most diseases on the basis of symptoms. However, that does not necessarily mean that the illnesses of patients with similar symptoms will have identical causes or demonstrate the same molecular changes.


AI Is Like Lego; Why You Should Hire A Chief AI Now - AI Summary

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Artificial intelligence is no different to Lego; you want to make sure that different algorithms are compatible with each other, you want to make sure that the algorithms are correct and have minimal fault tolerance and when you start to combine different algorithms, you can create an algorithmic business with enormous potential. And to manage this AI Lego building process, your organisation requires a Chief AI. Since the Chief AI has a clear understanding of the business objectives of the organisation as well as the available technology already in-house, the Chief AI should be able to attract the right AI talent. Next to attracting the right AI talent, the Chief AI should be able to retain this talent by offering them interesting and challenging AI projects. Therefore, the Chief AI should be able to understand the business needs and be able to translate these to technical requirements and adapt (existing) AI tools to the business needs.


Research Papers based on Gated RNN'S(Deep Learning)

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Abstract: Data augmentation has proven to be a promising prospect in improving the performance of deep learning models by adding variability to training data. In previous work with developing a noise robust acoustic-to-articulatory speech inversion system, we have shown the importance of noise augmentation to improve the performance of speech inversion in noisy speech. In this work, we compare and contrast different ways of doing data augmentation and show how this technique improves the performance of articulatory speech inversion not only on noisy speech, but also on clean speech data. We also propose a Bidirectional Gated Recurrent Neural Network as the speech inversion system instead of the previously used feed forward neural network. The inversion system uses mel-frequency cepstral coefficients (MFCCs) as the input acoustic features and six vocal tract-variables (TVs) as the output articulatory features.