Discover how to use Python--and some essential machine learning concepts--to build programs that can make recommendations. She helps you learn the concepts behind how recommendation systems work by taking you through a series of examples and exercises. Once you're familiar with the underlying concepts, Lillian explains how to apply statistical and machine learning methods to construct your own recommenders. She demonstrates how to build a popularity-based recommender using the Pandas library, how to recommend similar items based on correlation, and how to deploy various machine learning algorithms to make recommendations. At the end of the course, she shows how to evaluate which recommender performed the best.

The other day I was reading an article on how AI has progressed so far and where it is going. I was awestruck and had a hard time digesting the picture the author drew on possibilities in the future. Here is how I reacted. "A surgeon could control a machine scalpel with her motor cortex instead of holding one in her hand, and she could receive sensory input from that scalpel so that it would feel like an 11th finger to her. So it would be as if one of her fingers was a scalpel and she could do the surgery without holding any tools, giving her much finer control over her incisions. An inexperienced surgeon performing a tough operation could bring a couple of her mentors into the scene as she operates to watch her work through her eyes and think instructions or advice to her. And if something goes really wrong, one of them could "take the wheel" and connect their motor cortex to her outputs to take control of her hands."

In this work, we design and evaluate a computational learning model that enables a human-robot team to co-develop joint strategies for performing novel tasks that require coordination. The joint strategies are learned through "perturbation training," a human team-training strategy that requires team members to practice variations of a given task to help their team generalize to new variants of that task. We formally define the problem of human-robot perturbation training and develop and evaluate the first end-to-end framework for such training, which incorporates a multi-agent transfer learning algorithm, human-robot co-learning framework and communication protocol. Our transfer learning algorithm, Adaptive Perturbation Training (AdaPT), is a hybrid of transfer and reinforcement learning techniques that learns quickly and robustly for new task variants. We empirically validate the benefits of AdaPT through comparison to other hybrid reinforcement and transfer learning techniques aimed at transferring knowledge from multiple source tasks to a single target task. We also demonstrate that AdaPT's rapid learning supports live interaction between a person and a robot, during which the human-robot team trains to achieve a high level of performance for new task variants. We augment AdaPT with a co-learning framework and a computational bi-directional communication protocol so that the robot can co-train with a person during live interaction. Results from large-scale human subject experiments (n=48) indicate that AdaPT enables an agent to learn in a manner compatible with a human's own learning process, and that a robot undergoing perturbation training with a human results in a high level of team performance. Finally, we demonstrate that human-robot training using AdaPT in a simulation environment produces effective performance for a team incorporating an embodied robot partner.

Last Friday, we mentioned how Google's artificial intelligence software DeepMind has the ability to teach itself many things. It can teach itself how to walk, jump and run. Or defeat the world's best player of the Chinese strategy game, Go. The science of teaching computers how to do things is called Deep Learning. Offered through Udacity, the course is taught by Vincent Vanhoucke, the technical lead in Google's Brain team.

FOR a ten-year-old, Amartya is a thoughtful chap. One Monday morning at the Khan Lab School (KLS) in Mountain View, California, he explains that his maths is "pretty strong" but he needs to work on his writing. Not to worry, though; Amartya has a plan. He will practise grammar online, book a slot with an English teacher and consult his mentor. Later he will e-mail your correspondent to ask for help, too. This is the sort of pluck KLS produces. Its pupils do not have homework or report cards or spend all day in classrooms.

My latest machine learning book has been published and will be available during the last week of July. In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered in this book are Linear regression, Logistic Regression, SVM, Naïve Bayes, K-Means, Random Forest, and Feature engineering. In this book you will also learn how these algorithms work and their practical implementation to resolve your problems.

Make it good and share it. A quick search yields a plethora of possible resources that could help -- MOOCs, blogs, Quora answers to this exact question, books, Master's programs, bootcamps, self-directed curricula, articles, forums and podcasts. Their quality is highly variable; some are excellent resources and programs, some are click-bait laundry lists. Since this is a relatively new role and there's no universal agreement on what a data scientist does, it's difficult for a beginner to know where to start, and it's easy to get overwhelmed. Many of these resources follow a common pattern: 1) here are the skills you need and 2) here is where you learn each of these.

Course Description Caffe is a deep learning framework made with expression, speed, and modularity in mind. Audience This course is suitable for Deep Learning researchers and engineers interested in utilizing Caffe as a framework. After completing this course, delegates will be able to: understand Caffe's structure and deployment mechanisms carry out installation / production environment / architecture tasks and configuration assess code quality, perform debugging, monitoring implement advanced production like training models, implementing layers and logging

SHANGHAI-BASED online English learning platform Liulishuo said yesterday it has raised US$100 million from institutional investors and previous investors to fuel its future growth into artificial intelligence and tailor-made programs for English learners. China Media Capital and Wu Capital, as well as previous investors including TrustBridge, IDG Capital, GGV Capital, Cherubic Ventures and Hearst Ventures, have been announced as investors in the platform. Wang Yi, co-founder and chief executive officer of Liulishuo, said the company plans to hire more talent in the artificial intelligence field and to offer more AI-driven educational services besides its current AI-powered personalized interactive courses. "We hope to maintain our leading position in the artificial intelligence-backed online education field and to further enhance efficiency in English learning," he said, adding that they also hope to build an artificial intelligence learning research institution within two or three years. It will also provide AI-backed spoken English evaluating services for NASDAQ-listed TAL Education Group to integrate with TAL's current learning systems.

Editor's note: This tutorial series was started in September of 2014, with the 8 installments coming over the course of 2 years. I only mention this to put John's first paragraph into context, and to assure readers that this informative series of tutorials, including all of its code, is as relevant and up-to-date today as it was at the time it was written. This is great material, both for anyone taking Andrew Ng's MOOC and as a standalone resource. One of the pivotal moments in my professional development this year came when I discovered Coursera. I'd heard of the "MOOC" phenomenon but had not had the time to dive in and take a class.