Think about your last online order. If you're a frequent online shopper and have created profiles for sites you visit often (*raises hand*), then you're probably familiar with customized recommendations. Based on your purchase history, location, and other factors, the website may suggest other items you might be interested in buying. And if you're on the site long enough, chatbots may appear asking if you have questions or need assistance locating something. These are just two examples of how artificial intelligence (AI) and automation have made the consumer experience easier and created ways to help businesses understand their buying patterns and what they need.
While there are so many how-to courses for hands-on techies, there are practically none that also serve business leaders – a striking omission, since success with machine learning relies on a very particular business leadership practice just as much as it relies on adept number crunching. Rather than a hands-on training, this specialization serves both business leaders and burgeoning data scientists alike with expansive, holistic coverage of the state-of-the-art techniques and business-level best practices. There are no exercises involving coding or the use of machine learning software. Brought to you by industry leader Eric Siegel – a winner of teaching awards when he was a professor at Columbia University – this specialization stands out as one of the most thorough, engaging, and surprisingly accessible on the subject of machine learning. Across this range of topics, this specialization keeps things action-packed with case study examples, software demos, stories of poignant mistakes, and stimulating assessments.
One of the responsible things to do when a year is ending is to reflect on it. What accomplishments you have made, what challenges did you face, what did you learn, and how you can make the remainder of the year count. One experience that I can definitely share, and hopefully it would be beneficial to readers, is being awarded the 2019 Bertelsmann Tech Scholarship and receive the Deep Learning Nanodegree from Udacity, completely free of charge. And this year, Bertelsmann Tech is opening another scholarship application, which you should definitely try if you have a passion for data and cloud tech. Many people have asked online what it was like to apply for the Bertelsmann Tech scholarship, win it, and complete the Nanodegree from Udacity.
In this article, I will discuss several resources that can help you master the foundations of data science. In the modern age of information technology, there is an enormous amount of free resources for data science self-study. As a matter of fact, you can design your own data science curriculum from the innumerable amount of available resources. The rising demand for data science practitioners has given rise to a proliferation of massive open online courses (MOOC). If you are going to be taking one of these courses, keep in mind that some MOOCs are 100% free, while some do require you to pay a subscription fee (it could range anywhere from $50 to $200 per course or more, varies from platforms to platforms).
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Coursera's Machine Learning for Everyone (free access) fulfills two different kinds of unmet learner needs. It's a conceptually-complete, end-to-end course series – its three courses amount to the equivalent of a college or graduate-level course – that covers both the technology side and the business side. While fully accessible and understandable to business-level learners, it's also also vital to data scientists and budding technical practitioners, since it covers:
Learn Python & Ethical Hacking From Scratch, Online Courses Udemy, Start from 0 & learn both topics simultaneously from scratch by writing 20+ hacking programs 4.6 (5,342 ratings), Created by Zaid Sabih, English [Auto-generated], Indonesian [Auto-generated], 1 more PREVIEW THIS COURSE - GET COUPON CODE
Exam cheating is as ancient a practice as education itself. Back in ancient China, cheating on the Imperial exams was a serious offense. And yet a Qing dynasty cheatsheet--in the shape of a handkerchief with 10,000 symbols in microscopic writing--is on display at the Minneapolis Institute of Arts, showing that students have always been inclined to borrow knowledge and ideas. Exam cheating is as ancient a practice as education itself. Historically, students could attempt to use their own handwriting as false proof of originality, but now the digital age has changed that.
Quickly shifting to remote work has enterprises looking to meet the ops needs of a suddenly distributed team, and there are open source options to get them there. The recent mad rush to scale to remote work may prove to be a key chapter in DevOps and AIOps evolution. This need for rapid, widescale change is creating a real conundrum concerning AIOps, DevOps, and ITSM, as organizations seek the best monitoring and incident response solution for their now distributed enterprises. The key question both the DevOps and IT service management (ITSM) communities need to answer is how quickly they can pivot and adapt to increasing demands for operational intelligence. Artificial intelligence for IT Operations (AIOps) brings together artificial intelligence (AI), analytics, and machine learning (ML) to automate the identification and remediation of IT operations issues.