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Best Master's Programs in Machine Learning (ML) for 2021

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Considering various factors such as the research areas, research focus, courses offered, duration of the program, location of the university, honors, awards, and job prospects, we came up with the best universities to help you in your choosing process. This article is most suited for individuals who'd like to pursue a master's degree with a focus on machine learning and need some guidance on their decision making. Feel free to jump to the end if you are only looking for the university names. Note: The universities mentioned below are in no particular order.


Best Public Datasets for Machine Learning and Data Science

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This resource is continuously updated. If you know any other suitable and open datasets, please let us know by emailing us at pub@towardsai.net or by dropping a comment below. Google Dataset Search: Similar to how Google Scholar works, Dataset Search lets you find datasets wherever they are hosted, whether it's a publisher's site, a digital library, or an author's web page. It's a phenomenal dataset finder, and it contains over 25 million datasets. Kaggle: Kaggle provides a vast container of datasets, sufficient for the enthusiast to the expert.


Driving Efficiency with MLOps

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The advancements in machine learning has more and more enterprises turning towards the insights provided by it. Data scientists are busy creating and fine-tuning machine learning models for tasks ranging from recommending music to detecting fraud. Here's what a machine learning model lifecycle looks like: According to Wikipedia, "MLOps ('Machine Learning' 'Operations') is a practice for collaboration and communication between data scientists and operations professionals to help manage the production ML lifecycle. MLOps looks to increase automation and improve the quality of production ML while also focusing on business and regulatory requirements. MLOps applies to the entire lifecycle - from integrating with model generation (software development lifecycle, continuous integration/continuous delivery), orchestration, and deployment, to health, diagnostics, governance, and business metrics." So, is MLOps just another fancy name for DevOps?


Python Code Assistant Powered by GPT-3

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GPT-3 from OpenAI has captured public attention unlike any other AI model in the 21st century. The sheer flexibility of the model in performing a series of generalized tasks with near-human efficiency and accuracy is what makes it so exciting. It has created a paradigm shift in the world of Natural Language Processing(NLP), where till now the models were trained based on the ungenralized approach to excel at one or two tasks. GPT-3 is trained by OpenAI with a generalized approach on a massive scale involving 175 billion parameters which allows it to mimic functionalities of the human brain (like GPT-3 is capable of generating text that is surprisingly human-like after only being fed a few examples of the task you want it to do). Like a human brain GPT-3 is able to learn and do things with few shots of training unlike the conventional way of training an NLP model over a large corpus, which is both difficult and time-consuming.


The AI Monthly Top 3 -- February 2021

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Odei Garcia-Garin et al. from the University of Barcelona have developed a deep learning-based algorithm able to detect and quantify floating garbage from aerial images. They also made a web-oriented application allowing users to identify these garbages, called floating marine macro-litter, or FMML, within images of the sea surface.


Practical Guide: Build and Deploy a Machine Learning Web App

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This is a guide for a simple pipeline of a machine learning project. For this course, our target is to create a web app that will take as input a CSV file of flower attributes (sepal length/width and petal length/width) and returns a CSV file with the predictions (Setosa Versicolour Virginica). I know that you want to skip this step but don't. This will organize your packages and you will know exactly the packages you need to run your code incase we want to share it with someone else. Trust me, this is crucial.


The Future of AI Innovation

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After rapid growth over the past few years, artificial intelligence has become one of the biggest focuses of enterprises. Well, what has made it so hot? With AI, we can design systems that learn and adapt to all the new data we collect. Just a few years ago, AI seemed to be impossible. But now, it's quickly becoming necessary and expected.


AI Reveals Current Drugs May Help Combat Alzheimer's Disease

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Researchers have developed a method based on Artificial Intelligence (AI) that rapidly identifies currently available medications that may treat Alzheimer's disease. The method could represent a rapid and inexpensive way to repurpose existing therapies into new treatments for this progressive, debilitating neurodegenerative condition. Importantly, it could also help reveal new, unexplored targets for therapy by pointing to mechanisms of drug action. "Repurposing FDA-approved drugs for Alzheimer's disease is an attractive idea that can help accelerate the arrival of effective treatment -- but unfortunately, even for previously approved drugs, clinical trials require substantial resources, making it impossible to evaluate every drug in patients with Alzheimer's disease," said researcher Artem Sokolov from Harvard Medical School. "We, therefore, built a framework for prioritising drugs, helping clinical studies to focus on the most promising ones," Sokolov added.


Artificial intelligence vs humans - can you outwit technology?

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Imagine if artificial intelligence (AI) could pick the next Jeff Bezos, Richard Branson, Bill Gates or Elon Musk? It may be just around the corner according to a QUT researcher into entrepreneurship. A QUT study is looking for thousands of people to complete a short online survey asking them to look at photographs and identify which people are entrepreneurs. The results will be compared to the performance of AI models given the same challenge. Led by Professor Martin Obschonka Director of QUT's Australian Centre for Entrepreneurship Research, the aim of the project is to gauge the capacity of modern AI in recognising entrepreneurship potential in a variety of people.


Curriculum Learning and Symbolic Mathematics

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We can think of the humongous field of deep learning as the Earth's crust, floating on a mantle of mathematical and algorithmic understanding. It is a vast sphere of knowledge that is divided into specializations, similar to how tectonic plates divvy up our world. Most important of all, the specializations of deep learning -- natural language processing and cognitive computational science, for instance -- can coincide to form beautiful mountain ranges that help to define landmark areas of deep learning. Curriculum learning is one such Himalayan-range of a deep learning technique between the two fields of AI-oriented cognitive science and NLP. While currently not known to many practitioners or enthusiasts (its Wikipedia page is currently pending approval), for those who choose to explore this hidden gem, the find is worth the time.