Global Big Data Conference

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While the tools may change, the mistakes stay the same. Here are four common issues that IT leaders should be aware of when managing data science teams. In 2019, companies looking to gain an edge on competitors and insight into customers and trends have come to rely more heavily on data scientists to inform their business decisions. A good data scientist is invaluable to a company with any online presence. They will assess and interpret complex information and build out machine learning algorithms.


Machine Learning Yearning - deeplearning.ai

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AI is transforming numerous industries. Machine Learning Yearning, a free ebook from Andrew Ng, teaches you how to structure Machine Learning projects. This book is focused not on teaching you ML algorithms, but on how to make ML algorithms work.


PyTorch

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Data Cleaning and Preprocessing

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Data preprocessing involves the transformation of the raw dataset into an understandable format. Preprocessing data is a fundamental stage in data mining to improve data efficiency. The data preprocessing methods directly affect the outcomes of any analytic algorithm. Data is raw information, its the representation of both human and machine observation of the world. Dataset entirely depends on what type of problem you want to solve.


Machine Learning and UX Meetup is creating community events! Patreon

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We're a mix of {UX, ML, Data Science, PM, more} excited about the future of ML UX! Join us at our newsletter (eepurl.com/dA_7oH) Feel free to follow us on twitter (@mluxsf) or youtube to see our past events and photos and what we're up to:) Our Vision We want to create a collaborative environment between UX, Data Science, and everyone in between. We aim to organize a community that helps foster cooperation, creativity, and learning across disciplines. Our goal is to create a space to discuss human-centered machine learning, and share ideas and resources. Host regular "technical talks" and panels on Data Science/ML/AI UX/Design focused on sharing best practices, case studies, and lessons learned (check out our youtube channel for past talks, or our twitter - @mluxsf!) Semi-regular networking and happy hours so we can meet other folks and build a community!


Machine Learning & User Experience (MLUX) (San Francisco, CA)

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Want to be the first to know about our upcoming events? We're excited about creating a future of human-centered smart products, and we believe the first step to doing this is to connect UX and Data Science/Machine Learning folks to get together and learn from each other. We're a mix of data scientists, designers, machine learning scientists, PMs and more - exploring a human centered approach to machine learning. We have been super fortunate to have a fellowship from the Center for Technology, Society & Policy (https://ctsp.berkeley.edu/)and If you are interested in giving back and supporting us and helping enable us to invite other speakers who might not otherwise have access to speaking, please consider supporting us on Patreon! https://www.patreon.com/mluxsf


Top Artificial Intelligence (AI) Predictions For 2020 From IDC and Forrester

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IDC and Forrester issued recently their predictions for artificial intelligence (AI) in 2020 and beyond. While external "market events" may make companies cautious about AI, says Forrester, "courageous ones" will continue to invest and expand the initial "timid" steps they took in 2019. "RPA needs intelligence and AI needs automation to scale," says Forrester. As a quarter of Fortune 500 enterprises redirects AI investments to more mundane shorter-term or tactical IPA projects with "crystal-clear efficiency gains," around half of the AI platform providers, global systems integrators, and managed service providers will emphasize IPA in their portfolios. Building on the proven success of these IPA use cases, IDC predicts that by 2022, 75% of enterprises will embed intelligent automation into technology and process development, using AI-based software to discover operational and experiential insights to guide innovation.



Machine Learning and Artificial Intelligence Advancing Mineral Exploration

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Machine learning and artificial intelligence are becoming key components of mineral exploration programs as companies set exploration targets. Machine learning and artificial intelligence (AI) have the ability to solve two of the mining industry's biggest challenges: rising exploration costs and a lack of new discoveries. After a heavy downturn in the past few years, the mining and mineral exploration sector is finally starting to recover, but deep challenges remain. In an industry that thrives on new discoveries, today's resource companies are finding it harder and more expensive to locate new deposits. Gold provides one of the greatest examples of this dearth of new discoveries in the face of rising exploration costs.


Artificial Intelligence can design new TB drug regimens

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With a shortage of new tuberculosis drugs in the pipeline, a software tool from the University of Michigan can predict how current drugs, including unlikely candidates, can be combined in new ways to create more effective treatments – leading to the design of TB drug regimens. Sriram Chandrasekaran, U-M assistant professor of biomedical engineering, who leads the research, said: "This could replace our traditional trial-and-error system for drug development that is comparatively slow and expensive. Dubbed INDIGO, short for INferring Drug Interactions using chemoGenomics and Orthology, the software tool has shown that the potency of tuberculosis drugs can be amplified when they are teamed with antipsychotics or antimalarials. Shuyi Ma, a research scientist at the University of Washington and a first author of the study, said: "This tool can accurately predict the activity of drug combinations, including synergy, where the activity of the combination is greater than the sum of the individual drugs. "It also accurately predicts antagonism between drugs, where the activity of the combination is lesser. In addition, it also identifies the genes that control these drug responses."