In this new era of remote working, our reliance on technology is greater than ever, with many businesses having migrated processes from offline to online to accommodate the transition. But amongst the success stories of companies adapting to this change, working in the current climate has raised a pertinent issue for employers: how can they deliver effective learning and development (L&D) to employees when they are scattered across different workplaces? Irrespective of the shift to working from home, providing personalised and efficient L&D has been an issue for quite some time. We are in an age where we are more informed than ever, and while this is no bad thing, one of the drawbacks of this knowledge explosion is that employers are now faced with the prospect of ever-changing policies, procedures and regulations. It is vital that staff are kept up-to-date with industry developments, but this is becoming an increasingly unattainable goal.

Auto generate assessments in edtech like MCQs, True/False, Fill-in-the-blanks etc using state-of-the-art NLP techniques. This course focuses on using state-of-the-art Natural Language processing techniques to solve the problem of question generation in edtech. If we pick up any middle school textbook, at the end of every chapter we see assessment questions like MCQs, True/False questions, Fill-in-the-blanks, Match the following, etc. In this course, we will see how we can take any text content and generate these assessment questions using NLP techniques. This course will be a very practical use case of NLP where we put basic algorithms like word vectors (word2vec, Glove, etc) to recent advancements like BERT, openAI GPT-2, and T5 transformers to real-world use.

Learning from a textbook provides a more refined and in-depth knowledge beyond what you get from online courses. This book provides a great introduction to data science and machine learning, with code included: "Python Machine Learning", by Sebastian Raschka.

Image semantic segmentation is one of the most significant areas of research and engineering in the computer vision domain. From segmenting pedestrians and cars for autonomous drive [1] to segmentation and localization of pathology in medical images [2], there are several use-cases of image segmentation. With the wide-spread use of deep learning models for end-to-end delivery for machine learning (ML) models, the U-net model has emerged as a scalable solution across autonomous drive and medical imaging use-cases [3–4]. However, most existing papers and methods implement binary classification tasks for detecting objects/regions of interest over the backgrounds [4]. In this hands-on tutorial we will review how to start from a binary semantic segmentation task and transfer the learning to suit multi-class image segmentation tasks.

The Quarterly CS Econ Workshop brings in three or four experts at the interface between computer science and economics to present their perspective and research on a common theme. Chicago area researchers with interest in economics and computer science are invited to attend. The technical program is in the morning and includes coffee and lunch (on your own). The afternoon of the workshop will allow for continued discussion between attendees and the speakers. The workshop series is organized by Jason Hartline, Benjamin Golub, Annie Liang, Marciano Siniscalchi, and Alireza Tahbaz-Salehi.

Embedded machine learning applications power every modern aspect of our modern life. From Siri to fitness wearables, space rovers to medical implants, you will find tiny instructions embedded on micronized hardware that delivers magical and important experiences. At Edge Impulse, our mission is to enable any developer to use machine learning to build better products while making it easier for them to get started and be successful. We teamed up with instructor Shawn Hymel, Coursera, Arm, Arduino and the tinyML Foundation to bring you the ultimate course to get you started with embedded machine learning. This course is an important piece of our journey and mission, and we aim to reach millions of developers so they can make machine learning part of their stack.

Basin hopping is a global optimization algorithm. It was developed to solve problems in chemical physics, although it is an effective algorithm suited for nonlinear objective functions with multiple optima. In this tutorial, you will discover the basin hopping global optimization algorithm. Basin Hopping Optimization in Python Photo by Pedro Szekely, some rights reserved. Basin Hopping is a global optimization algorithm developed for use in the field of chemical physics.

Having one of these in your resume can make a lot of difference. Data science is one of the most versatile fields ever around; even its name is not very explanatory of what the field actually involves. Perhaps that's one reason people find this field quite challenging and difficult to get into and even more difficult to show professionalism. It is well known within the data science community that to be a "good" data scientist is all about how strong of a portfolio you build, how diverse your projects are, and how well they show your ability to solve any problem creatively and efficiently. Although being a data scientist -- or have a specialty in any of its branches -- doesn't require a university degree, having some certificate that proves your profession in some aspects of the field can transform your portfolio and take your career on step further.

Description Python is the most emerging programming language in the world. It is used for web and software development. It has a lot of things that you can't consider in other programming languages. You will learn and everything by coding the programs. This course will take you from beginner to expert in Python, easily and smartly.

Artificial Intelligence (AI) is revolutionizing the way we live and work, so today I invite you to learn more about this topic by approaching it from three different and increasingly complex segments. Broadly speaking, we can refer to AI as the simulation of human intelligence by machines. In other words, a discipline that tries to create systems capable of learning and reasoning like people . Importantly, Artificial Intelligence is the most debated technology of the 21st century. Today, it is widely used to solve complex problems and facilitate human tasks.