Your team needs it, your boss demands it, and your career loves it. After all, LinkedIn places it as one of the top few "Skills Companies Need Most" and as the very top emerging job in the U.S. If you want to participate in the deployment of machine learning (aka predictive analytics), you've got to learn how it works. Even if you work as a business leader rather than a hands-on practitioner – even if you won't crunch the numbers yourself – you need to grasp the underlying mechanics in order to help navigate the overall project. Whether you're an executive, decision maker, or operational manager overseeing how predictive models integrate to drive decisions, the more you know, the better. And yet, looking under the hood will delight you. The science behind machine learning intrigues and surprises, and an intuitive understanding is not hard to come by.

Humans tutoring other humans works pretty well. The trouble is, it requires a lot of people. Artificially intelligent tools tutoring humans works pretty well, too--but building those digital systems takes time and expertise. So researchers hoping to engineer better teaching and learning systems are working to unlock a new level of education efficiency by creating AI tools that make it easier for almost anyone to build an AI tutor. "We are trying to leverage the joint power of human tutoring and computer tutoring," says Ken Koedinger, a professor of human-computer interaction and psychology at Carnegie Mellon University.

E-learning and ed-tech startups are becoming a rage as more and more people are developing interest in new forms of education involving innovation. This change is productive, crucial and able to break the geological boundaries for gaining quality education. The rising number of COVID-positive cases has made it very clear that without social distancing, it will be very difficult to control the spread of this deadly virus. So, in order to overcome the challenges faced by educational institutions, online teaching tools are now being implemented across different levels of education among many schools and universities in the world. Digitization of education is viewed as a need of the hour in India, where not many institutions are equipped with the right tools nor do they have the specialized aptitudes to make the learning environment technologically advanced.

Azer Bestavros, associate provost for computing and data sciences, says the new PhD program will emphasize the ethical and responsible application of computing technology. With its state-of-the-art Center for Computing & Data Sciences scheduled to open in 2022, BU's Faculty of Computing & Data Sciences (CDS) is now accepting applications for a new cross-disciplinary PhD program, where candidates will be able to work under the guidance of faculty within CDS as well as CDS-affiliated faculty in a domain of their choice. Azer Bestavros, associate provost for computing and data sciences, says PhD students in CDS will have the opportunity to pursue research that stands to impact a variety of disciplines. "That's what computing and data science research does," he says, "so the idea is to have a program where it is possible to marry data science with another discipline. That discipline could be in the social sciences or the humanities or it could be in engineering or life sciences. That will be up to the student."

Statistics is the science of turning data into insights and ultimately decisions. Behind recent advances in machine learning, data science and artificial intelligence are fundamental statistical principles. The purpose of this class is to develop and understand these core ideas on firm mathematical grounds starting from the construction of estimators and tests, as well as an analysis of their asymptotic performance. Taking this class will allow you to expand your statistical knowledge to not only include a list of methods, but also the mathematical principles that link them together, equipping you with the tools you need to develop new ones. This course is part of theMITx MicroMasters Program in Statistics and Data Science.

There were seven interesting and varied invited talks at NeurIPS this year. Here, we summarise the first three, which were given by Charles Isbell (Georgia Tech), Jeff Shamma (King Abdullah University of Science and Technology) and Shafi Goldwasser (UC Berkeley, MIT and Weizmann Institute of Science). The invited talks kicked off in style with a presentation from Charles Isbell. He had posted a teaser on Twitter indicating that he was trying something new with the format, and it certainly did not disappoint. The talk received rave reviews during both the live chat channel and afterwards on social media.

Besides being the home of Santa Claus, Finland is known as a tech leader, even ahead of the US, according to the UNDP. Indeed, tech operations constitute "over 50% of all Finnish exports." We even owe technologies like Linux and the first web browser to Finland. Today, Finland is keeping up its tech legacy with its free Elements of AI online course. Elements of AI is a set of two online courses made by Reaktor and the University of Helsinki, combining theory and practice with teaching as many people as possible about AI.

Recently, I actively started looking for a job change to Data science, and I don't have any formal education like a Master's or Ph.D. background in AI/Machine Learning. I started learning it completely out of my own interest (not just because of the hype). It was one of the challenging tracks to opt-in, especially if you are working simultaneously on some other technology. I started my journey by enrolling myself in many MOOCs(Massive Open Online Courses) and started reading multiple blogs. It slowly started making sense.

If you think technology has only credited the Retail, F&B, eCommerce, Financial, and Agriculture verticles, then it's time for you to amaze yourself with the known yet unknown fact, the educational sector. The black & whiteboard to the projector screen and after COVID-19 it's the technology that makes education alive among the students. And it's important to acknowledge that the students enjoy the use of technology at every level of their learning process. Like how workplace digital transformation helped with high productivity and employee retention rate, similarly, creative ways to use technology in the classroom has created an excitement for learning among the students. The use of technology in teaching and learning has made life easier for both the teachers and students, the communication between the students and teachers are no more in a distress spot, right from the academic calendar schedules to lessons with media and visuals, technology integration has truly benefited education institutes.

Welcome to AI and Machine Learning for Coders, a book that I've been wanting to write for many years but that has only really become possible due to recent advances in machine learning (ML) and, in particular, TensorFlow. The goal of this book is to prepare you, as a coder, for many of the scenarios that you can address with machine learning, with the aim of equipping you to be an ML and AI developer without needing a PhD! I hope that you'll find it useful, and that it will empower you with the confidence to get started on this wonderful and rewarding journey. If you're interested in AI and ML, and you want to get up and running quickly with building models that learn from data, this book is for you. If you're interested in getting started with common AI and ML concepts--computer vision, natural language processing, sequence modeling, and more--and want to see how neural networks can be trained to solve problems in these spaces, I think you'll enjoy this book.