In this guest post, you will learn by example how to do two popular machine learning techniques called random forest and extremely random forests. In fact, this post is an excerpt (adapted to the blog format) from the forthcoming Artificial Intelligence with Python – Second Edition: Your Complete Guide to Building Intelligent Apps using Python 3.x and TensorFlow 2. Now, before you will learn how to carry out random forests in Python with scikit-learn, you will find some brief information about the book. The new edition of this book, which will guide you to artificial intelligence with Python, is now updated to Python 3.x and TensorFlow 2. Furthermore, it has new chapters that, besides random forests, cover recurrent neural networks, artificial intelligence and Big Data, fundamental use cases, chatbots, and more. Finally, artificial Intelligence with Python – Second Edition is written by two experts in the field of artificial intelligence; Alberto Artasanches and Pratek Joshi (more information about the authors can be found towards the end of the post). Now, in the next section of this post, you will learn what random forests and extremely random forests are.

We recently highlighted one of the most acclaimed courses on using deep learning techniques for natural language processing, Stanford's freely available Natural Language Processing with Deep Learning (CS224n). Stanford has another fantastic NLP course which is also freely available online, and which is also taught by a world renowned NLP researcher, academic, and author. The course in question is From Languages to Information (CS124), and it is taught by Dan Jurafsky. Just as with the previous Stanford NLP course profile, let's be clear about a couple of things; first, this isn't a recent occurrence, and the course materials and videos (see below) have been available online for quite some time (the materials were once collected into a Coursera course as well). Second, and possibly more importantly, there is no option to enroll, as this is not a MOOC; it is simply the freely available materials from this world-class course on the foundations of natural language processing.

As 40% of people infected with COVID-19 are asymptomatic, if a patient is imaged for an unrelated health concern and doctors can identify COVID-19, we'll be in a much better position. In addition to identifying COVID-19 by viral detection and antibody response, we can also suspect viral infection indirectly through resting heart rate. Dr. Eric Topol explained in the "AI for Healthcare Keynote" that for a flu-like illness, the resting heart rate marker allows us to predict illness throughout the country from a wearable device like a Fitbit or Apple watch. Dr. Topol states that heart rate rises before a fever is present, so even if someone doesn't get a fever or experience symptoms, we can still detect that their body is fighting a virus. "Resting heart rate, with the analytics of AI for healthcare, can predict where an outbreak is likely to happen and that's a topic that doesn't get enough respect because people just think test, test, test and they don't understand that digital surveillance with AI can be very useful," said Dr. Topol. Pulse oximetry in wearable devices can also help us detect the virus's damage to the lungs. Dr. Topol thinks that the way to get ahead of this virus is simple: equip everyone with a wearable device that has a pulse oximeter and collects resting heart rate and body temperature. "Here we are in the US spending trillions of dollars. What we should be thinking about is: what can we arm each person with, so that we can help protect them?"

As you already know, AI is one of the leading technologies in the world today, and people are talking about it much more than ever. We now can find AI applications every where: from finances, marketing, healthcare, to autonomous vehicles, security, or robotics. However, the domain of AI still lacks of qualified employees while the number of investments in AI is increasing rapidly. Thus, open a great opporturnity for people having a background in this domain. After several years of researching and working in AI, now I'd like to share my knowledge and my experiences to people who want to learn about AI, because I really hope that my small contribution can help many ones find a fast and easy way in learning AI.

Deep learning techniques play an increasingly important role in industrial and research environments due to their outstanding results. However, the large number of hyper-parameters to be set may lead to errors if they are set manually. The state-of-the-art hyper-parameters tuning methods are grid search, random search, and Bayesian Optimization. The first two methods are expensive because they try, respectively, all possible combinations and random combinations of hyper-parameters. Bayesian Optimization, instead, builds a surrogate model of the objective function, quantifies the uncertainty in the surrogate using Gaussian Process Regression and uses an acquisition function to decide where to sample the new set of hyper-parameters. This work faces the field of Hyper-Parameters Optimization (HPO). The aim is to improve Bayesian Optimization applied to Deep Neural Networks. For this goal, we build a new algorithm for evaluating and analyzing the results of the network on the training and validation sets and use a set of tuning rules to add new hyper-parameters and/or to reduce the hyper-parameter search space to select a better combination.

Then this course is for you! This course is designed in a very simple and easily understandable content. You might have seen lots of buzz on deep learning and you want to figure out where to start and explore. This course is designed exactly for people like you! If basics are strong, we can do bigger things with ease.

Optimization and The NFL's Toughest Scheduling Problem - June 23 At first glance, the NFL's scheduling problem seems simple: 5 people have 12 weeks to schedule 256 games over the course of a 17-week season. The scenarios are potentially well into the quadrillions. In this latest Data Science Central webinar, you will learn how the NFL began using Gurobi's mathematical optimization solver to tackle this complex scheduling problem.

We have developed an AI-powered socio-technical system for making online learning in higher education more accessible, affordable and achievable. In particular, we have developed four novel and intertwined AI technologies: (1) VERA, a virtual experimentation research assistant for supporting inquiry-based learning of scientific knowledge, (2) Jill Watson Q&A, a virtual teaching assistant for answering questions based on educational documents including the VERA user reference guide, (3) Jill Watson SA, a virtual social agent that promotes online interactions, and (4) Agent Smith, that helps generate a Jill Watson Q&A agent for new documents such as class syllabi. The results are positive: (i) VERA enhances ecological knowledge and is freely available online; (ii) Jill Watson Q&A has been used by >4,000 students in >12 online classes and saved teachers >500 hours of work; (iii) Jill Q&A and Jill Watson SA promote learner engagement, interaction, and community; and (iv). Agent Smith helps generate Jill Watson Q&A for a new syllabus within ~25 hours. Put together, these innovative technologies help make online learning simultaneously more accessible (by making materials available online), affordable (by saving teacher time), and achievable (by providing learning assistance and fostering student engagement).

IIT-Ropar, one of the eight new IITs established by the Ministry of Human Resource Development (MHRD), Government of India, and TSW, the executive education division of Times Professional Learning (a part of The Times of India Group), have launched a Post Graduate Certificate Programme in Artificial Intelligence & Deep Learning. The programme will be coordinated by The Indo-Taiwan Joint Research Centre (ITJRC) on Artificial Intelligence (AI) and Machine Learning (ML), at IIT-Ropar. Supported by the Ministry of Science and Technology, Taiwan, ITJRC is a bilateral centre for collaborative research in disruptive technologies like AI and ML. The programme, with its focus on Artificial Intelligence and Deep Learning, has an eligibility criterion of a minimum of 2 years of work experience in the IT industry. Though an engineering degree is a desirable prerequisite for this programme, one does not need a coding or mathematics background to be eligible.

In this advanced course on deep reinforcement learning, you will learn how to implement policy gradient, actor critic, deep deterministic policy gradient (DDPG), and twin delayed deep deterministic policy gradient (TD3) algorithms in a variety of challenging environments from the Open AI gym. From there we will progress to teaching an agent to balance the cart pole using Q learning. After mastering the fundamentals, the pace quickens, and we move straight into an introduction to policy gradient methods. We cover the REINFORCE algorithm, and use it to teach an artificial intelligence to land on the moon in the lunar lander environment from the Open AI gym. Next we progress to coding up the one step actor critic algorithm, to again beat the lunar lander.