A key challenge in online learning is that classical algorithms can be slow to adapt to changing environments. Recent studies have proposed "meta" algorithms that convert any online learning algorithm to one that is adaptive to changing environments, where the adaptivity is analyzed in a quantity called the strongly-adaptive regret. This paper describes a new meta algorithm that has a strongly-adaptive regret bound that is a factor of $\sqrt{\log(T)}$ better than other algorithms with the same time complexity, where $T$ is the time horizon. We also extend our algorithm to achieve a first-order (i.e., dependent on the observed losses) strongly-adaptive regret bound for the first time, to our knowledge. At its heart is a new parameter-free algorithm for the learning with expert advice (LEA) problem in which experts sometimes do not output advice for consecutive time steps (i.e., \emph{sleeping} experts). This algorithm is derived by a reduction from optimal algorithms for the so-called coin betting problem. Empirical results show that our algorithm outperforms state-of-the-art methods in both learning with expert advice and metric learning scenarios.

Be a Technology Creator Today!!! Discover the scientist in you. Are you excited to create something immediately without getting into too much subject theory which bores you? Then you have landed at the right course. Research has shown that theoretical learning leads to decrease in interest in the subject and is one of the biggest hindrances to learn new things or new Technology. That's why we have created a course for every body where you start building applications and learn theory along with it.

This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data.

My interest in ml stems back to 2014 when I started reading articles about it on Hacker News. I simply found the idea of teaching machines stuff by looking at data appealing. At the time I wasn't even a professional developer, but a hobby coder who'd done a couple of small projects. So I began watching the first few chapters of Udacity's Supervised Learning course, while also reading all articles I came across on the subject. This gave me a little bit of conceptual understanding, though no practical skills.

In this Introduction to IoT Programming with JavaScript training course, expert author Patrick Catanzariti will teach you how to create interactions with connected devices and dashboards. This course is designed for users that already have experience with web development, JavaScript, and Node. You will start by learning how to build your first dashboard, including setting up a modular Node server and getting your server onto the web. From there, Patrick will show you how to set up an Arduino, display Arduino data, and go wireless with Arduino Yun and node-serialport. This video tutorial also covers Spark, Tessel, pairing Android and JavaScript using on{X}, and voice recognition with Wit.

This course is all about A/B testing. A/B testing is used everywhere. A/B testing is all about comparing things. If you're a data scientist, and you want to tell the rest of the company, "logo A is better than logo B", well you can't just say that without proving it using numbers and statistics. Traditional A/B testing has been around for a long time, and it's full of approximations and confusing definitions. In this course, while we will do traditional A/B testing in order to appreciate its complexity, what we will eventually get to is the Bayesian machine learning way of doing things.

The majority of employed data scientists gained their skills through self-learning or a Massive Open Online Course (MOOC) rather than a traditional computer science degree, according to a survey from data scientist community Kaggle, which was acquired by Google Cloud earlier this year. Some 32% of full-time data scientists started learning machine learning or data science through a MOOC, while 27% said that they began picking up the needed skills on their own, the 2017 State of Data Science & Machine Learning Survey report found. Some 30% got their start in data science at a university, according to the survey of more than 16,000 people in the field. More than half of currently employed data scientists still use MOOCs for ongoing education and skillbuilding, the report found, demonstrating the potential of these courses for helping people gain real world skills. Data scientist took the no. 1 spot in Glassdoor's Best Jobs in America list in 2016 and 2017, and reports a median base salary of $110,000.

Then we invite you to check out this very friendly introduction we made at Udacity! There are actually 19 videos included in this playlist, covering topics like Linear Regression, Neural Networks, Hierarchical Clustering, and more. Really got the Machine Learning fever? Then consider enrolling in our Machine Learning Nanodegree program. It's the best way to learn everything you need to know to become a successful Machine Learning Engineer!

Then this course is for you! This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way. We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science. This course is fun and exciting, but at the same time we dive deep into Machine Learning.

This post is authored by Matthew Calder, Senior Business Strategy Manager, and Ke Wang, Research Intern at Microsoft. There are more than 9,000 students enrolled in the Microsoft Introduction to C# course on edX.org. Although course staff can't offer the type of guidance available in an on-campus classroom setting, students can receive personalized help, thanks to a project from Microsoft Research. When a student's assignment contains mistakes, that student--within seconds--receives a message specific to their code submission. Beyond just informing the student that their program doesn't work, Microsoft has created a tool which automatically generates feedback that precisely identifies errors and even hints at how to correct them.