Machine learning business plan

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Short essay description
Management and Leadership. Machine learning, a branch of artificial intelligence, is the science of programming computers to improve their performance by learning from data. Dramatic progress has been made in the last decade, driving machine learning into the spotlight of conversations surrounding disruptive technology. This six-week online program from the MIT Sloan School of Management and the MIT Computer Science and Artificial Intelligence Laboratory CSAIL aims to demystify machine learning for the business professional — offering you a firm, foundational understanding of the advantages, limitations, and scope of machine learning from a management perspective. How will machine learning affect your business?
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Machine learning: From science project to business plan

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Machine Learning (ML) - Digital and Classroom Training | AWS

Rip-and-replace your expensive hardware? Trust a startup? Or DIY? Here is real-world advice for easing into artificial intelligence and machine learning applications.
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How to identify scenarios and plan for advanced analytics data processing

The TDSP provides a systematic approach for teams to collaborate on building intelligent applications. The scenarios presented here illustrate options available in the data processing workflow that depend on the data characteristics, source locations, and target repositories in Azure. The decision tree for selecting the sample scenarios that are appropriate for your data and objective is presented in the last section. Each of the following sections presents a sample scenario. For each scenario, a possible data science or advanced analytics flow and supporting Azure resources are listed.
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No longer was it an esoteric discipline commanded by the few, the proud, the data scientists. That movement revolved around three trends: new and improved tool kits for machine learning, better hardware and easier access to it , and more cloud-hosted, as-a-service variants of machine learning that provided both open source and proprietary tools. Once upon a time, if you wanted to implement machine learning in an app, you had to roll the algorithms yourself. Eventually, third-party libraries came onto the field that saved you the trouble of reinventing the wheel, but still required a lot of heavy lifting to be productive. Now the state of the art involves frameworks designed to make machine learning an assembly-line process: Data in one end, trained models and useful results out the other.
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