So instead, I've put this video together showing my code running. Topics covered broadly follow the course contents: * Linear Regression * Logistic Regression * Regularization * Hand-writing Recognition * Neural Networks * Support Vector Machines * Unsupervised Learning * Anomaly Detection * Recommender Systems Thanks for watching!

A few months ago I had the opportunity to complete Andrew Ng's Machine Learning MOOC taught on Coursera. It serves as a very good introduction for anyone who wants to venture into the world of AI/ML. I always wondered how amazing this course could be if it were in Python. I finally decided to re-take the course but only this time I would be completing the programming assignments in Python. In these series of blog posts, I plan to write about the Python version of the programming exercises used in the course.

Statistics is a field of mathematics that is universally agreed to be a prerequisite for a deeper understanding of machine learning. Although statistics is a large field with many esoteric theories and findings, the nuts and bolts tools and notations taken from the field are required for machine learning practitioners. With a solid foundation of what statistics is, it is possible to focus on just the good or relevant parts. In this crash course, you will discover how you can get started and confidently read and implement statistical methods used in machine learning with Python in seven days. This is a big and important post. You might want to bookmark it.

In this blogpost, you will learn how Azure SQL Database intelligent performance feature Intelligent Insights has successfully helped a customer troubleshoot a hard to find 6-month intermittent database performance issue in a single day only. You will find out how Intelligent Insights helps an ISV operate 60,000 databases by identifying related performance issues across their database fleet. You will also learn how Intelligent Insights helped an enterprise seamlessly identify a hard to troubleshoot performance degradation issue on a large-scale 35TB database fleet. Azure SQL Database, the most intelligent cloud database, is empowering small and medium size business, and large enterprises to focus on writing awesome applications while entrusting Azure to autonomously take care of running, scaling, and maintain a peak performance with a minimum of human interaction, or advanced technical skill set required. Intelligent Insights is a new disruptive intelligent performance technology leveraging the power of artificial intelligence (AI) to continuously monitor and troubleshoot Azure SQL Database performance issues with a pinpoint accuracy and at a large scale simply not possible before.

The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data. Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.

In this paper, we demonstrate novel ways in which the synthesis of these data can illuminate the terrain of users' environment and support them in their decision making and wayfinding. A novel application of Recurrent Neural Networks and skip-gram models, approaches popularized by their application to modeling language, are brought to bear on student university enrollment sequences to create vector representations of courses and map out traversals across them. We present demonstrations of how scrutability from these neural networks can be gained and how the combination of these techniques can be seen as an evolution of content tagging and a means for a recommender to balance user preferences inferred from data with those explicitly specified. From validation of the models to the development of a UI, we discuss additional requisite functionality informed by the results of a field study leading to the ultimate deployment of the system at a university.

We propose a family of optimization methods that achieve linear convergence using first-order gradient information and constant step sizes on a class of convex functions much larger than the smooth and strongly convex ones. This larger class includes functions whose second derivatives may be singular or unbounded at their minima. Our methods are discretizations of conformal Hamiltonian dynamics, which generalize the classical momentum method to model the motion of a particle with non-standard kinetic energy exposed to a dissipative force and the gradient field of the function of interest. They are first-order in the sense that they require only gradient computation. Yet, crucially the kinetic gradient map can be designed to incorporate information about the convex conjugate in a fashion that allows for linear convergence on convex functions that may be non-smooth or non-strongly convex. We study in detail one implicit and two explicit methods. For one explicit method, we provide conditions under which it converges to stationary points of non-convex functions. For all, we provide conditions on the convex function and kinetic energy pair that guarantee linear convergence, and show that these conditions can be satisfied by functions with power growth. In sum, these methods expand the class of convex functions on which linear convergence is possible with first-order computation.

On a balmy summer's day in San Francisco, Milena Marinova is sitting on the roof terrace of the offices of Pearson, a company in the midst of a radical transformation from publishing powerhouse to digital-education platform, wrapped in a gray shawl and explaining how she plans to build advanced, deep-learning algorithms that could educate the next generation of students. This is no easy task. With millions of students using its education-software, Pearson has amassed "terrabytes" of data from student homework and even textbooks that have been digitized, data that Marinova is now pulling together to build software that can automatically give students feedback on their work like a teacher would. Instead of just telling them that an answer is right or wrong, a future update to Pearson's math homework tool will give more detailed feedback on how they went wrong in the steps taken to get an answer, Marinova told Forbes in an interview. Pearson is starting with math because the topic is relatively easy to structure and digitize.

What is it, where is it now, where is it going? Artificial Intelligence holds significant promise to revolutionise our educational systems, but are our educational systems ready for a revolution? In this article, published in Ireland's Yearbook of Education 2017-2018, Brett Becker explores current advances of AI in education and discusses how AI is likely to affect our education systems in the years ahead. Very few subjects in science and technology today are causing as much excitement, and as much misconception, as Artificial Intelligence (AI). It seems that everyone from Obama to Putin and Bezos to Zuckerburg are commenting on both the possibilities and the problems that AI could bring to humanity.

Apple is about to release the most expensive phone ever made. The most premium of the three handsets expected to be released this week – tentatively known as the iPhone XS Max – will almost certainly cost well over £1,000 and could be approaching £1,500. The new iPhones already stand to be the most expensive phones Apple have ever made: last year's iPhone X had the highest ever price of any iPhone, and the larger version of the same phone will almost certainly be considerably more expensive. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph. The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.