Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists in the tech industry - and prepare you for a move into this hot career path. This comprehensive course includes 68 lectures spanning almost 9 hours of video, and most topics include hands-on Python code examples you can use for reference and for practice. I'll draw on my 9 years of experience at Amazon and IMDb to guide you through what matters, and what doesn't. Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon.

Andrew Ng taught one of the most-viewed online courses of all time--more than 1.5 million people have registered to take one of the many sequences of his free online course about machine learning. That experience spurred him to co-found Coursera. Today Ng announced that this summer he's launching sequels to that blockbuster, with a series of courses on the AI concept known as deep learning. For the past two years Ng had been applying concepts of deep learning in the commercial sector, as a chief scientist for the Chinese tech giant Baidu. But he left that company in March, and since then has been working on three undisclosed projects in AI.

Machine learning is increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. With this course, you will learn how to perform various machine learning tasks in different environments. We'll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the course, you'll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms.

Mike Salem from Udacity's Robotics Nanodegree is hosting a series of interviews with professional roboticists as part of their free online material. Jillian is a video game designer turned roboticist attempting to combine games in robotics in a unique user experience. You can find all the interviews here. We'll be posting them regularly on Robohub.

Data science and machine learning are some of the top buzzwords in the technical world today. Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. Using powerful algorithms and techniques offered by machine learning you can automate any analytical model. Python is one of the most popular languages used for machine learning and arguably, the best entry point to the fascinating world of machine learning (ML). If you're interested to explore both the programming and machine learning world with python, then go for this course.

R is considered as lingua franca of Data Science. Candidates with expertise in R programming language are in exceedingly high demand and paid lucratively in Data Science. IEEE has repeatedly ranked R as one of the top and most popular Programming Languages. Almost every Data Science and Machine Learning job posted globally mentions the requirement for R language proficiency. All the top ranked universities like MIT have included R in their respective Data Science courses curriculum.

What does the future of work look like? Will there still be jobs even if the nature of work is exceptionally different from today? New technologies undoubtedly changed the way we work. Recent studies suggest that unemployment rate today is significant in most developed nations and it's only going to get worse. By 2030, mid-level jobs will be by and large obsolete.

There have been a lot of surveys over the past few years on the educational background of data scientists. As a result, there have also been many different results. In the O'Reilly Data Science Salary Survey of 2014, about 28% of the respondents had a Bachelor's degree, while 44% had a Master's degree and 20% had a Ph.D. Common fields that data scientists have as backgrounds are mathematics/Statistics, Computer Sciences, and Engineering. The results that are represented in the infographic are from 2016. They are very similar to the ones of the O'Reilly survey.

If you're plugged into the tech industry, you'll know that two things have been making consistent waves in many areas over the past few years; machine learning and Python. What happens when you combine the new gold standard programming language with the most significant tech development in areas such as financial trading, online search, digital marketing and even data and personal security (among others)? This course will show you what's what, and get you started on becoming a machine learning guru. If you have a desire to learn machine learning concepts and have some previous programming or Python experience, this course is perfect for you. If you're more of a beginner than an intermediate, don't worry; each module starts with theory to explain upcoming concepts.

We consider a variant of online convex optimization in which both the instances (input vectors) and the comparator (weight vector) are unconstrained. We exploit a natural scale invariance symmetry in our unconstrained setting: the predictions of the optimal comparator are invariant under any linear transformation of the instances. Our goal is to design online algorithms which also enjoy this property, i.e. are scale-invariant. We start with the case of coordinate-wise invariance, in which the individual coordinates (features) can be arbitrarily rescaled. We give an algorithm, which achieves essentially optimal regret bound in this setup, expressed by means of a coordinate-wise scale-invariant norm of the comparator. We then study general invariance with respect to arbitrary linear transformations. We first give a negative result, showing that no algorithm can achieve a meaningful bound in terms of scale-invariant norm of the comparator in the worst case. Next, we compliment this result with a positive one, providing an algorithm which "almost" achieves the desired bound, incurring only a logarithmic overhead in terms of the norm of the instances. Keywords: Online learning, online convex optimization, scale invariance, unconstrained online learning, linear classification, regret bound.