educational setting

Stock Technical Analysis with Python Udemy


It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or do research as experienced investor. Learning stock technical analysis is indispensable for finance careers in areas such as equity research and equity trading. It is also essential for academic careers in quantitative finance. And it is necessary for experienced investors stock technical trading research and development. But as learning curve can become steep as complexity grows, this course helps by leading you step by step using S&P 500 Index ETF prices historical data for back-testing to achieve greater effectiveness.

Python Design Patterns Udemy


A knowledge of design patterns enables developers to improve their codebase, promotes code reuse, and makes the architecture more robust. We start off by easing you into the world of design patterns, and helping you brush up on your OOP skills. From there, you'll explore the most widely used patterns and create objects in a manner best suited to the situation. Then we take you through some patterns that will help you identify simple ways to realize relationships between entities. Next, we show you how to encapsulate behavior in an object and delegate requests to it, before we up the ante and delve into some advanced patterns.

Practical Time Series Analysis Coursera


About this course: Welcome to Practical Time Series Analysis! Many of us are "accidental" data analysts. We trained in the sciences, business, or engineering and then found ourselves confronted with data for which we have no formal analytic training. This course is designed for people with some technical competencies who would like more than a "cookbook" approach, but who still need to concentrate on the routine sorts of presentation and analysis that deepen the understanding of our professional topics. In practical Time Series Analysis we look at data sets that represent sequential information, such as stock prices, annual rainfall, sunspot activity, the price of agricultural products, and more.

Machine Learning with Apache Spark 2: 2-in-1 Udemy


Apache Spark lets you apply machine learning techniques to data in real time, giving users immediate machine-learning based insights based on what's happening right now. It's used to create machine learning models and programs that are distributed and much faster compared to standard machine learning toolkits such as R or Python. If you're a data professional who is familiar with machine learning and wants to use Apache Spark for developing efficient and fast machine learning systems, then this learning path is for you. This comprehensive 2-in-1 course teaches you to build machine learning systems, perform analytics, and predictions with Apache Spark. You'll learn through practical demonstrations of use cases, clear explanations, and interesting real-world applications.

Scala For Beginners Udemy


This is a very basic introductory course to the fundamentals of the Scala programming language for anyone new to the language. Scala was derived from Java which is one of the top-five programming languages in the world today. It is a versatile and elegant object –oriented programming language. This means it is class based and treats everything as an object. It has a robust security .

Video: Andrew Ng on Deploying Machine Learning in the Enterprise - insideHPC


In this video from Intel AI DevCon 2018, Andrew Ng from and When you ask Siri for directions, peruse Netflix's recommendations or get a fraud alert from your bank, these interactions are led by computer systems using large amounts of data to predict your needs. The market is only going to grow. By 2020, the research firm IDC predicts that AI will help drive worldwide revenues to over $47 billion, up from $8 billion in 2016. Still, Andrew NG says fears that AI will replace humans are misplaced: "Despite all the hype and excitement about AI, it's still extremely limited today relative to what human intelligence is."

Work of the future and the future of work for women in political science

MIT News

After a 30-year career focused on the economic institutions of wealthy democracies, Kathleen Thelen, the Ford Professor of Political Science, has recently begun carving out time from her globe-hopping schedule to pursue compelling opportunities closer to home. "At a certain point in your career, you feel that part of what you want to do is give back," says Thelen, who is a member of the American Academy of Arts and Sciences and holds a permanent appointment at the Max Planck Institute for the Study of Societies and honorary degrees from three European universities. As the 2017-18 president of the 12,000-member American Political Science Association (APSA), Thelen is spearheading an effort to understand and address the challenges to career advancement faced by women with doctorates in political science. Thelen will also be a key player in MIT's Task Force on the Work of the Future, an Institute-wide venture launched in February to explore the impacts of technology on jobs. "The task force will be putting the interaction of technology and society at the forefront," she says.

How to Build Your Expertise in Artificial Intelligence


Despite half a century's research on AI, building expertise in the technology is still a huge challenge due to the lack of specialists in this sphere. We are all heading towards an era of an AI-powered society. As Andrew Ng, a leading AI Researcher put in an interview with the MIT Technology Review: "AI is the new electricity." Leading tech companies and P2P marketplaces who had foreseen the AI invasion and had started experimenting with ML-field features have already left behind their competitors. Artificial intelligence was previously perceived with skepticism, however, the attitude towards it has changed considerably in the last two years.

Automation Will Make Lifelong Learning a Necessary Part of Work


President Emmanuel Macron together with many Silicon Valley CEOs will kick off the VivaTech conference in Paris this week with the aim of showcasing the "good" side of technology. Our research highlights some of those benefits, especially the productivity growth and performance gains that automation and artificial intelligence can bring to the economy -- and to society more broadly, if these technologies are used to tackle major issues such as fighting disease and tackling climate change. But we also note some critical challenges that need to be overcome. To see just how big those shifts could be, our latest research analyzed skill requirements for individual work activities in more than 800 occupations to examine the number of hours that the workforce spends on 25 core skills today. We then estimated the extent to which these skill requirements could change by 2030, as automation and artificial technologies are deployed in the workplace, and backed up our findings with a detailed survey of more than 3,000 business leaders in seven countries, who largely confirmed our quantitative findings.

39 Machine Learning Resources that will help you in every essential step


For almost all machine learning projects, the main steps of the ideal solution remains same. For each step, I was doing some research on the web depending on my business object and jotting down the best resources I ran across. The resources include Online Courses, Kernels from Kaggle, Cheat Sheets and Blog Posts. Below I've listed them and categorised by each step (all of the resources are free except the ones that have'paid' in the end):