The new MITx MicroMasters Program in Statistics and Data Science, which opened for enrollment today, will help online learners develop their skills in the booming field of data science. The program offers learners an MIT-quality, professional credential, while also providing an academic pathway to pursue a PhD at MIT or a master's degree elsewhere. "There are many online programs that provide a professional overview of data science, but they don't offer the level of detail learners gain from an actual, residential master's program," says Professor Devavrat Shah, faculty director of the program and MIT professor in the Department of Electrical Engineering and Computer Science (EECS). "This new MicroMasters program in Statistics and Data Science is bringing the quality, rigor, and structure of a master's-level, residential program in data science at MIT to a wider audience around the world, and at a very accessible price, so people can learn anywhere they are while keeping their day jobs." In all, seven universities will be accepting the new MicroMasters Statistics and Data Science (SDS) credential towards a master's degree, including the Rochester Institute of Technology (United States), Doane University (United States), Galileo University (Guatemala), Reykjavik University (Iceland), Curtin University (Australia), Deakin University (Australia), and RMIT University (Australia).

In this webinar, Sébastien Leger from Loud ML will share with you the power of using unsupervised learning frameworks to gain deep insights into your InfluxData time series data (application and performance metrics, network flows, and financial or transactional data). He will then show you how to configure, model, and dig into the modeled times series data using the Loud ML API and your existing InfluxDB databases. This will open the recording. Here is an unedited transcript of the webinar "How to Build Your First Predictive Model in Seconds with InfluxDB and Loud ML" This is provided for those who prefer to read than watch the webinar. Please note that the transcript is raw. We apologize for any transcribing errors. We have a really great webinar today. We actually always have a great webinar. But today, I'm really excited. We'll get started in just one minute. In the meantime, I'll just cover some housekeeping items. If you have any questions during the presentation, please feel free to type them in either the Q&A, or the Chat Panel. And if you really, really, really want to speak out your questions, just raise your hand and I can un-mute you and you can talk to Sebastian directly. In addition, as always, I will--this session's being recorded. After I do the edit, then I'll post it and you will get--usually you'll get the email first thing tomorrow morning. But I usually end up posting this in a couple of hours. So if you go back to the link, you'll see that the page actually will change from the registration page to the recording. So you'll be able to take a listen to it again. And also, we have trainings on Thursdays.

Source code (with copious amounts of comments) is attached as a resource with all the code-alongs. The source code has been provided for both Python 2 and Python 3 wherever possible. This team has decades of practical experience in working with Java and with billions of rows of data. Prerequisites: No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided. Taught by a Stanford-educated, ex-Googler and an IIT, IIM – educated ex-Flipkart lead analyst.

China has recently published its first artificial intelligence (AI) textbook for high school students, following a plan by central government last year to include AI courses in primary and secondary school. Under the joint efforts by the research center for MOOC at East China Normal University and AI startup SenseTime Group, the nine-chapter textbook, named Fundamentals of Artificial Intelligence, was written by eminent scholars from well-known schools nationwide, Xinhua reported on Sunday. It includes the history of AI and how the technology can be applied in areas such as facial recognition, auto driving and public security. "The textbook focuses not only on basics of AI, also on practical use of AI in daily life," said Chen Yukun, a professor at East China Normal University, who is also a contributor to the book. At present, about 40 high schools across the country have joined the first batch of AI high education pilot program, by introducing the textbook in curriculum.

Since the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area. Remedying this deficiency, Machine Learning: An Algorithmic Perspective, Second Edition helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation.

About this course: The course aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include: (1) understanding where the problem one faces lands on a general landscape of available ML methods, (2) understanding which particular ML approach(es) would be most appropriate for resolving the problem, and (3) ability to successfully implement a solution, and assess its performance. A learner with some or no previous knowledge of Machine Learning (ML) will get to know main algorithms of Supervised and Unsupervised Learning, and Reinforcement Learning, and will be able to use ML open source Python packages to design, test, and implement ML algorithms in Finance. Fundamentals of Machine Learning in Finance will provide more at-depth view of supervised, unsupervised, and reinforcement learning, and end up in a project on using unsupervised learning for implementing a simple portfolio trading strategy.

The practice of Business Analysis revolves around the formation, transformation and finalisation of requirements to recommend suitable solutions to support enterprise change programmes. Practitioners working in the field of business analysis apply a wide range of modelling tools to capture the various perspectives of the enterprise, for example, business process perspective, data flow perspective, functional perspective, static structure perspective, and more. These tools aid in decision support and are especially useful in the effort towards the transformation of a business into the "intelligent enterprise", in other words, one which is to some extent "self-describing" and able to adapt to organisational change. However, a fundamental piece remains missing from the puzzle. Achieving this capability requires us to think beyond the idea of simply using the current mainstream modelling tools.

A staggering amount of data is created everyday; analyzing and organizing this enormous amount of data can be quite a complex task. Haskell is a powerful and well-designed functional programming language that is designed to work with complex data. It is trending in the field of data science as it provides a powerful platform for robust data science practices. This course will introduce the basic concepts of Haskell and move on to discuss how Haskell can be used to solve the issues by using the real-world data. The course will guide you through the installation procedure, after you have all the tools that you require in place, you will explore the basic concepts of Haskell including the functions, and the data structures.

About this course: This course aims at providing an introductory and broad overview of the field of ML with the focus on applications on Finance. Supervised Machine Learning methods are used in the capstone project to predict bank closures. Simultaneously, while this course can be taken as a separate course, it serves as a preview of topics that are covered in more details in subsequent modules of the specialization Machine Learning and Reinforcement Learning in Finance. The goal of Guided Tour of Machine Learning in Finance is to get a sense of what Machine Learning is, what it is for and in how many different financial problems it can be applied to.

In this tutorial, you will learn how to use the video analysis features in Amazon Rekognition Video using the AWS Console. Amazon Rekognition Video is a deep learning powered video analysis service that detects activities and recognizes objects, celebrities, and inappropriate content. As a developer, analyzing video is a challenge you will face if you are developing a video cataloging system or creating an application to provide sentiment analysis. This challenge can be solved by building your own machine learning model, however this option is time-intensive, expensive, and requires machine learning expertise. Amazon Rekognition Video provides an easy-to-use API that offers real-time analysis of streaming video and facial analysis.