Get yourself trained on Beginning Machine Learning with this Online Training Beginning Machine Learning with AWS.
Online Training Beginning Machine Learning with AWS
Machine Learning with AWS is the right place to start if you are a beginner interested in learning useful artificial intelligence (AI) and machine learning skills using Amazon Web Services (AWS), the most popular and powerful cloud platform. You will learn how to use AWS to transform your projects into apps that work at high speed and are highly scalable. From natural language processing (NLP) applications, such as language translation and understanding news articles and other text sources, to creating chatbots with both voice and text interfaces, you will learn all that there is to know about using AWS to your advantage. You will also understand how to process huge numbers of images fast and create machine learning models.By the end of this course, you will have developed the skills you need to efficiently use AWS in your machine learning and artificial intelligence projects. About the AuthorJeffrey Jackovich, is the author of this course, and a curious data scientist with a background in health-tech and mergers and acquisitions (M&A). He has extensive business-oriented healthcare knowledge, but enjoys analyzing all types of data with R and Python. He loves the challenges involved in the data science process, and his ingenious demeanor was tempered while serving as a Peace Corps volunteer in Morocco. He is completing a Masters of Science in Computer Information Systems, with a Data Analytics concentration, from Boston University. Ruze Richards, is the author of this course, and a data scientist and cloud architect who has spent most of his career building high-performance analytics systems for enterprises and startups. He is especially passionate about AI and machine learning, having started life as a physicist who got excited about neural nets, then going on to work at AT&T Bell Labs in order to further pursue this area of interest. With the new wave of excitement along with the actual computing power being available on the cloud for anybody to actually get amazing results with machine learning, he is thrilled to be able to spread the knowledge and help people achieve their goals. Kesha Williams is a software engineer with over 20 years of experience in web application development. She is specialized in working with Java, Spring, Angular, and Amazon Web Services (AWS). She has trained and mentored thousands of developers in the US, Europe, and Asia while teaching Java at the university level. She has won the Ada Lovelace Award in Computer Engineering from LookFar and the Think Different Innovation Award from Chick-fil-A for working with emerging technologies and artificial intelligence.
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As a society, we spend hundreds of billions of dollars measuring the return on our financial assets. Yet, at the same time, we still haven’t found convincing ways of measuring the return on our investments in developing people.
And I get it: If my bank account pays me 1% a year, I can measure it to the penny. We’ve been collectively trained to expect neat and precise ROI calculations on everything, so when it’s applied to something as seemingly squishy as how effectively people are learning in the workplace, the natural inclination is to throw up our hands and say it can’t be done. But we need to figure this out. In a world where skills beat capital, the winners and losers of the next 30 years will be determined by their ability to attract and develop great talent.
Fortunately, corporate learning & development (L&D), like most business functions, is evolving quickly. We can embrace some level of ambiguity and have rigor when measuring the ROI of learning. It just might look a little different than an M.B.A. would expect to see in an Excel model.