Goto

Collaborating Authors

 Learning Management


Volatility Trading Analysis with R Udemy

@machinelearnbot

Learn volatility trading analysis through a practical course with R statistical software using CBOE, S&P 500, VelocityShares volatility strategies benchmark indexes and replicating ETFs or ETNs historical data for risk adjusted performance back-testing. It explores main concepts from advanced to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or do your research as experienced sophisticated investor. Learning volatility trading analysis is indispensable for finance careers in areas such as derivatives research, derivatives development, and derivatives trading mainly within investment banks and hedge funds. It is also essential for academic careers in derivatives finance. And it is necessary for experienced sophisticated investors' volatility trading strategies research.


Topcoder - developers are excited about AI, but they must embrace data science

#artificialintelligence

One clever way to pierce the PR swamp of my inbox is with an optimistic twist. If that optimism is backed by data? Topcoder PR recently won my inbox with this email subject header: "Coders aren't scared of losing work to AI โ€“ Topcoder community explains why." It helps that I've known about Topcoder for years. With 1,200,000 developers, all signed up to collaborate on crowdsourced projects and compete in online challenges, Topcoder know a thing or two about what makes developers tick โ€“ and how coders upskill against requirements.


Introduction to R Udemy

#artificialintelligence

With "Introduction to R", you will gain a solid grounding of the fundamentals of the R language! This course has about 90 videos and 140 exercise questions, over 10 chapters. To begin with, you will learn to Download and Install R (and R studio) on your computer. Then I show you some basic things in your first R session. From there, you will review topics in increasing order of difficulty, starting with Data/Object Types and Operations, Importing into R, and Loops and Conditions.


Learning Path: Your Guide to Learn Data Science using Python

@machinelearnbot

Python is a popular programming language, widely used in many scenarios and easy to use to use. Data Science is an interdisciplinary field that employs techniques to extract knowledge from data. As one of the fast growing fields in technology, the interest for Data Science is booming, and the demand for specialized talent is on the rise. Packt's Video Learning Path is a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. To start off with your learning journey, you can learn some of the fundamental tools of the trade and apply them to real data problems.



[D] Can you help me choose a Deep Learning online course? Coursera Specialization VS Udacity Nanodegree โ€ข r/MachineLearning

@machinelearnbot

I haven't taken this specific Udacity course on deep learning. But, I have completed their Nanodegree for the self-driving cars that covered a decent amount of deep learning material. I won't be surprised if they borrow some of the contents from there as well. Udacity offers high quality lectures and related projects. Their content is usually ver well organized and they are constantly improving.


App Deployment, Debugging, and Performance Coursera

@machinelearnbot

About this course: In this course, application developers learn how to design, develop, and deploy applications that seamlessly integrate components from the Google Cloud ecosystem. Through a combination of presentations, demos, and hands-on labs, participants learn how to use GCP services and pre-trained machine learning APIs to build secure, scalable, and intelligent cloud-native applications. Prerequisites and Pre-work โ€ข Completed Google Cloud Platform Fundamentals or have equivalent experience โ€ข Working knowledge of Node.js โ€ข Basic proficiency with command-line tools and Linux operating system environments โ€ข Previous course(s) in the specialization


Machine Learning to Assess Machine Learning Engineers

#artificialintelligence

Data science and within that, machine learning has seen an explosive uptick in both interest and application in recent years. This has meant that the job market has expanded quickly. With no real sign of slowing demand and a limit to the number of experienced individuals with computer science degrees, the market has been opened up to a diverse set of prospective candidates. Many individuals are moving into the industry from backgrounds such as the sciences, engineering or from engagement with massive open online courses (MOOCs). In fact, Andrew Ng himself recently placed emphasis on taking on interns who had completed his Deep Learning MOOC on Coursea.


Robotics: Mobility Coursera

@machinelearnbot

Now we'll put physical links and joints together and consider the geometry and the physics required to understand their coordinated motion. We'll learn about the geometry of degrees of freedom. We'll then go back to Newton and learn a compact way to write down the physical dynamics that describes the positions, velocities and accelerations of those degrees of freedom when forced by our actuators.Of course there are many different ways to put limbs and bodies together: again, the animals can teach us a lot as we consider the best morphology for our limbed robots. Sprawled posture runners like cockroaches have six legs which typically move in a stereotyped pattern which we will consider as a model for a hexapedal machine. Nature's quadrupeds have their own varied gait patterns which we will match up to various four-legged robot designs as well.


2444

AI Magazine

Column n The Educational Advances in Artificial Intelligence column discusses and shares innovative educational approaches that teach or leverage AI and its many subfields at all levels of education (K-12, undergraduate, and graduate levels). In this column I describe my experience adapting the content and infrastructure from massive, open, online courses (MOOCs) to enhance my courses in the Department of Electrical Engineering and Computer Science at Vanderbilt University. I begin with my informal, early use of MOOC content and then move to two deliberatively designed strategies for adapting MOOCs to campus (that is, wrappers and small private online classes [SPOCs]). I describe student reactions and touch on selected policy and institutional considerations. In the never-ending search for increasing student bang-for-the-buck, I was motivated to increase the bang, rather than reduce the buck, the latter being well above my pay grade.