Artificial Intelligence is one of the fastest growing technologies of the 21st century and accompanies us in our daily lives when interacting with technical applications. However, reliance on such technical systems is crucial for their widespread applicability and acceptance. The societal tools to express reliance are usually formalized by lawful regulations, i.e., standards, norms, accreditations, and certificates. Therefore, the T\"UV AUSTRIA Group in cooperation with the Institute for Machine Learning at the Johannes Kepler University Linz, proposes a certification process and an audit catalog for Machine Learning applications. We are convinced that our approach can serve as the foundation for the certification of applications that use Machine Learning and Deep Learning, the techniques that drive the current revolution in Artificial Intelligence. While certain high-risk areas, such as fully autonomous robots in workspaces shared with humans, are still some time away from certification, we aim to cover low-risk applications with our certification procedure. Our holistic approach attempts to analyze Machine Learning applications from multiple perspectives to evaluate and verify the aspects of secure software development, functional requirements, data quality, data protection, and ethics. Inspired by existing work, we introduce four criticality levels to map the criticality of a Machine Learning application regarding the impact of its decisions on people, environment, and organizations. Currently, the audit catalog can be applied to low-risk applications within the scope of supervised learning as commonly encountered in industry. Guided by field experience, scientific developments, and market demands, the audit catalog will be extended and modified accordingly.

In the third course of the Deep Learning Specialization, you will learn how to build a successful machine learning project and get to practice decision-making as a machine learning project leader. By the end, you will be able to diagnose errors in a machine learning system; prioritize strategies for reducing errors; understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance; and apply end-to-end learning, transfer learning, and multi-task learning. This is also a standalone course for learners who have basic machine learning knowledge. This course draws on Andrew Ng's experience building and shipping many deep learning products. If you aspire to become a technical leader who can set the direction for an AI team, this course provides the "industry experience" that you might otherwise get only after years of ML work experience.

Abstraction is the cornerstone of modern-day scalable formal verification. Classic papers in formal literature talk about abstraction as a Galois connection, but understanding abstraction when you're new to formal is not easy. We discuss it in detail in our upcoming webinar on 11 Feb 2021 but for now, here is an intuitive and simple explanation of abstraction and refinement.

It was nothing more than what Brad Stevens termed "a curveball," as it turned out. After an initial false positive COVID test, Evan Fournier turned in a string of negative tests, leading to his first-time availability for the Celtics Monday night against New Orleans. "He will play significant minutes, as he will all the rest of the year," Stevens said of how he planned to begin with the talented wing player, acquired from Orlando at the trade deadline for the since-waived Jeff Teague and two second-round draft picks. "We had an obvious need for another wing that can do what he does, and we're fortunate he's with us, and he's on our team," said the Celtics coach. "So I got a chance to go over to the gym (Sunday) while he was shooting around when we got back and then this morning we went through some stuff prior to our shootaround, we shot around as a team for 30 minutes, so he's gotten the crash course in a very short amount of time. He's been there, done that. He's played against us, you know, tons of times, probably knows our plays as well as anybody, and certainly we just want him to play to his strengths and not worry about anything else."

Are you looking for the Best Free Udemy Courses 2021? This Online Courses list contains the Best Udemy Certifications, and Tutorial for you.

Highlight: In the previous post we talked how we can manipulate pixels and images in Python using the OpenCV library. Now, it's time to focus our attention to videos. In this post, you will learn some basic operations which are necessary for building your computer vision applications. First, we will explain how you can load camera frames and video files. Second, you will see how you can read, display and save videos using OpenCV.

Description Are you going to learn image processing and machine vision? Do you want to learn it in one of the easiest and interesting programming softwares? Image processing is a technology by which you can perform some changes on an image, in order to do measurement, calibration, identification, control in different industries such as production, medicine, meteorology, astronomy, military and security. Machine vision is a type of technology that provides automatic inspection, identification, measurement, control and analysis. One of the amazing features of machine learning is that it can be integrated with deep learning and machine learning technologies which might help business and organizations a lot.

Python for Machine Learning with Numpy, Pandas & Matplotlib Learn to Code in Python and How to use NumPy, Pandas, Matplotlib and Seaborn by real time Machine Learning project. Description Are you ready to start your path to becoming a Data Scientist or ML Engineer? This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms! Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems!

Today, staying competitive means progressing with machine learning and analytics. Fortunately, the journey to success doesn't require teams have data scientists or deep analytics expertise. Register for this webinar to learn the proven processes and software technologies that make analytics accessible for every industrial organization. Cobus van Heerden is senior product manager for analytics and machine learning software for GE Digital. Cobus has 20 years of experience in developing and implementing industrial software globally.

A project-based course to build an AIoT system from theory to prototype. Artificial Intelligence and Automation with Zang Cloud Sample codes are provided for every project in this course. You will receive a certificate of completion when finishing this course. There is also Udemy 30 Day Money Back Guarantee, if you are not satisfied with this course. This course teaches you how to build an AIoT system from theory to prototype particularly using Naive Bayes algorithm.