This class can be summarized in one sentence, "to learn how to put your machine learning ideas into your customer's plate". Here we will extend multiple Python machine learning ideas into fully interactive web applications, into a format that anybody anywhere can access as long as they have access to a web browser. Our last project will be built around a professional paywall infrastructure so you can control and monetize how and whom can access it. Whether you want to test out business ideas or share advanced and predictive analytics ideas with the world, the tools taught in this class will allow you to do that quickly, easily and without spending a lot of money.Who this course is for:

Udemy Course Computer Science 101: Intro to Java & Algorithms NED Computer Science 101: Intro to Java & Algorithms by Tristan Hull, Joshua Benz 11 hours on-demand video Master Coding The Right Way! Learn Java and Algorithms with instructors Tristan and Joshua by Tristan Hull, Joshua Benz hat you'll learn Fundamentals of Programming Object Oriented Programming Basic Syntax to Expressions Selection Statements to Loops Advanced OOP Concepts Description Learn Java and Algorithms with instructors Tristan and Joshua. This course is designed for students who are struggling in their computer science program, or anyone that wants to learn programming with little to no prior experience. We will take you from level zero to mastery in no time. The two instructors have combined 20 years experience with software development and computer science. We designed this course to make sure the student actually understands, and to cover what every introduction college class would teach.

Transformational advancements in technology in today's world are making it possible for data scientists to develop machines that think for themselves. Based on complex algorithms that can glean information from data, today's computers can use neural networks to mimic human brains, and make informed decisions based on the most likely scenarios. The immense possibilities that machine learning can unlock are fascinating, and with data exploding across all fields, it appears that in the near future Machine Learning will be the only viable alternative simply because there is nothing quite like it! With so many opportunities on the horizon, a career as a Machine Learning Engineer can be both satisfying and rewarding. A good workshop, such as the one offered by KnowledgeHut, can lead you on the right path towards becoming a machine learning expert.

You might know the theory of Machine Learning and know how to create algorithms. But as you know you must get your hands Dirty on Real-World Case Studies. There are so many courses which teaches the basic of Machine Learning But do not cover the Applications. This course will help you bridge the gap between a person who knows machine learning and a person who actually know how to apply Machine Learning in real world. Knowing Machine learning and Applying it in the real world is totally different.

Upskilling is the new corporate mantra, and unless you're running an AI-only startup (and maybe even then), your workforce needs it. But the old kind of upskilling -- offering learning opportunities focused on a siloed technology -- is not enough to get your employees or your company ready for AI at scale. True upskilling requires more than offering training courses. As executives in our survey recognized (50%), you also need to give immediate opportunities and incentives for people to apply what they've learned, so that knowledge turns into real-world skills that improve performance. Such a citizen-led approach is not only the most effective way to teach tech chops like creating data sets, building a machine learning model, or using Python or R notebooks.

Udemy Course Mask R-CNN - Practical Deep Learning Segmentation in 1 hour NED Mask R-CNN – Practical Deep Learning Segmentation in 1 hour free download also includes 6 hours on-demand video, 5 articles, 80 downloadable resources, Full lifetime access, Access on mobile and TV, Assignments, Certificate by Augmented Startups, Geeky Bee AI Private Limited What you'll learn What is Instance Segmentation How to take object segmentation further using Mask RCNN Secret tip to multiply your data using Data Augmentation. How to use AI to label your dataset for you. Find out how to train your own custom Mask R-CNN from scratch. Description ***Important Notes*** This is a practical-focused course. While we do provide an overview of Mask R-CNN theory, we focus mostly on helping you get Mask R-CNN working step-by-step.

Future Shock: 25 trends in education post COVID-19.By Sandeep Goyal This Future Shock series is inspired by the Alvin Toffler book with the same name, first published in the 1970s. The book future gazed a rapidly changing world, propelled into newer and newer orbits by not just science and technology, but by newer political realities, sociological change and the emergence of newer opportunities, newer aspirations and newer lifestyles. But even Toffler had not visualized a world faced with cataclysmic change because of a pandemic, a metamorphosis triggered by a virus. Most governments around the world have temporarily closed educational institutions in an attempt to contain the spread of the COVID-19 pandemic. Some 1.3-1.5 billion students and youth across the planet are affected by school and university closures. These nationwide closures are impacting over 72% of the world's student population. Several other countries have implemented localized closures impacting millions of additional learners. Governments around the world are making efforts to mitigate the immediate impact of school closures, particularly for more vulnerable and disadvantaged communities, and to facilitate the continuity of education for all through remote learning. School closures carry high social and economic costs for people across communities. Their impact however is particularly severe for the most vulnerable and marginalized boys and girls, and their families.

May 15, 2020--In the race to stem COVID-19, researchers around the world are testing the capacity of artificial intelligence (AI) to assist in tasks such as diagnosis and drug discovery. So far, AI's biggest success during the pandemic has been in speeding up the process of identifying existing drugs that can be repurposed to help suffering patients, said Deborah DiSanzo, who recently lectured on COVID-19 in the new course she's leading at Harvard T.H. Chan School of Public Health--Artificial Intelligence in Health. DiSanzo cited in her lecture an AI knowledge graph developed by researchers at the UK startup BenevolentAI and the Imperial College London, which found that baricitinib, a rheumatoid arthritis drug, had the potential to inhibit the virus that causes COVID-19. It and other drugs identified in similar studies have now gone into clinical trials. "Two years ago, finding either a new or repurposed drug target would take six to 18 months," said DiSanzo, a former health care technology executive.

Our goal for this workshop is to educate researchers about the technological needs of people with vision impairments while empowering researchers to improve algorithms to meet these needs. A key component of this event will be to track progress on a new dataset challenge, where the task is to caption images taken by people who are blind. Winners of this challenge will receive awards sponsored by Microsoft. The second key component of this event will be a discussion about current research and application issues, including by invited speakers from both academia and industry who will share about their experiences in building today's state- of-the- art assistive technologies as well as designing next-generation tools. We invite submissions of results from algorithms for the image captioning challenge task.

Online one-on-one class is created for highly interactive and immersive learning experience. It demands a large number of qualified online instructors. In this work, we develop six dialogic instructions and help teachers achieve the benefits of one-on-one learning paradigm. Moreover, we utilize neural language models, i.e., long short-term memory (LSTM), to detect above six instructions automatically. Experiments demonstrate that the LSTM approach achieves AUC scores from 0.840 to 0.979 among all six types of instructions on our real-world educational dataset.