What could be more convenient than upgrading skills online? There are plenty of courses / certifications available to kick-start your career in analytics. These courses are provided in online, offline or hybrid mode. The only difficulty students face is to decide the best out of these courses. With some newly introduced courses, it has become even more difficult to make a convincing decision. The fear of investing in unworthy courses continues to remain the biggest hurdle for students. Last year, I received thousand of emails after I published Top Certifications on SAS, R, Python, Machine Learning. Later, I came to know that my analysis helped many people in deciding the best course for themselves. The year 2016 is no different either. I am back with my thorough analysis and rankings of best certifications courses in India. I assure you these rankings are unbiased. Last month, we released our rankings on Top Business Analytics Programs in India 2015-16. If you too are planning for a degree in analytics, you may like to consider these institutes. In this article, I'll focus on ranking short duration and certification courses. I've considered the courses which are delivered in online or hybrid mode.

Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. It's incredible, but it can also be overwhelming. That's why we created This Week in Machine Learning! Each week we publish a curated list of Machine Learning stories as a resource to help you keep pace with all these exciting developments.

The ability to make continual, accurate decisions based on evolving data is key in many of today's data-driven intelligent systems. This tutorial-style talk presents an introduction to the modern study of sequential learning and decision making under uncertainty. The broad objective is to cover modeling frameworks for online prediction and learning, explore algorithms for decision making, and gain an understanding of their performance. Specifically, we will look at multi-armed bandits- models of decision making that capture the explore-vs-exploit tradeoff in learning, regret minimization, non-stochastic or adversarial online learning, and online convex optimization. Time permitting, we will discuss new directions and frontiers in the area of sequential decision making.

IN JULY 2011 Sebastian Thrun, who among other things is a professor at Stanford, posted a short video on YouTube, announcing that he and a colleague, Peter Norvig, were making their "Introduction to Artificial Intelligence" course available free online. By the time the course began in October, 160,000 people in 190 countries had signed up for it. At the same time Andrew Ng, also a Stanford professor, made one of his courses, on machine learning, available free online, for which 100,000 people enrolled. Both courses ran for ten weeks. Such online courses, with short video lectures, discussion boards for students and systems to grade their coursework automatically, became known as Massive Open Online Courses (MOOCs).

Hi, I'm planning to make a 5/6 part series to reflect about my experience in University of Washington Machine Learning Specialization in Coursera while I take the five courses: Foundations, Regression, Classification, Clustering, Deep Learning and finish the capstone project. This is the first article in the series. I'll feature the first course Machine Learning Foundations: A Case Study Approach in this article and describe the philosophy behind the'case study approach' with a brief overview of the tools used and reflect on what I've learnt. I hope it will help people who want to use the same specialization.I'm also taking courses in Udacity, Edx and using other resources too, but experience those resources will be described in separate articles. I'm also planning to write a whole separate series on Udacity Machine Learning Nanodegree in recent future.

The edtech trend on the tip of everyone's tongue at this year's EdtechXEurope event is artificial intelligence. By harnessing the power of AI and deep learning, educators can glean insights from the vast quantities of data hoovered up from their students. AI could also help lecturers make better decisions and could improve student retention rates, according to experts. "AI is a tool to make better sense of data," says Satya Nitta, director of education and cognitive sciences at IBM. The world's top online learning platforms are working with business schools such as Yale SOM and Duke Fuqua, and are offering advanced analytical tools to help them refine and enhance student learning.

Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. It's incredible, but it can also be overwhelming. That's why we created This Week in Machine Learning! Each week we publish a curated list of Machine Learning stories as a resource to help you keep pace with all these exciting developments.

Claned Co-founder Vesa Perala believes that instead of attempting to retrofit technology to out-dated educational systems, EdTech start-ups should be helping to write a new rulebook. For the past 3 years, Claned has been in what he describes as semi-stealth mode, focusing on developing a robust artificial intelligence system that uses machine-learning algorithms to map out what factors most impact individual learning. That knowledge, he says, was already out there, because it's something universities routinely do. Over time, tutors build an understanding of how each student learns, yet that data is trapped in a system which simply isn't scalable. Claned set out to solve this by combining these tried-and-tested academic evaluation metrics with machine learning algorithms and Artificial Intelligence.

In the standard setting of approachability there are two players and a target set. The players play repeatedly a known vector-valued game where the first player wants to have the average vector-valued payoff converge to the target set which the other player tries to exclude it from this set. We revisit this setting in the spirit of online learning and do not assume that the first player knows the game structure: she receives an arbitrary vector-valued reward vector at every round. She wishes to approach the smallest ("best") possible set given the observed average payoffs in hindsight. This extension of the standard setting has implications even when the original target set is not approachable and when it is not obvious which expansion of it should be approached instead. We show that it is impossible, in general, to approach the best target set in hindsight and propose achievable though ambitious alternative goals. We further propose a concrete strategy to approach these goals. Our method does not require projection onto a target set and amounts to switching between scalar regret minimization algorithms that are performed in episodes. Applications to global cost minimization and to approachability under sample path constraints are considered.

Claned Co-founder Vesa Perala believes that instead of attempting to retrofit technology to out-dated educational systems, EdTech start-ups should be helping to write a new rulebook. For the past 3 years, Claned has been in what he describes as semi-stealth mode, focusing on developing a robust artificial intelligence system that uses machine-learning algorithms to map out what factors most impact individual learning. That knowledge, he says, was already out there, because it's something universities routinely do. Over time, tutors build an understanding of how each student learns, yet that data is trapped in a system which simply isn't scalable. Claned set out to solve this by combining these tried-and-tested academic evaluation metrics with machine learning algorithms and Artificial Intelligence.