In this course, we aim to specialize in artificial intelligence by doing Machine Learning and Deep Learning Projects at various levels. Before starting the course, you must have basic Python knowledge. Our aim in this course is to turn real-life problems that seem difficult to do into projects and then solve them using latest versions of artificial intelligence algorithms and Python(3.8). This course was prepared in July 2021. We will carry out some of our projects using machine learning and some using deep learning algorithms.

Recent work has raised concerns on the risk of spurious correlations and unintended biases in statistical machine learning models that threaten model robustness and fairness. In this paper, we propose a simple and intuitive regularization approach to integrate causal knowledge during model training and build a robust and fair model by emphasizing causal features and de-emphasizing spurious features. Specifically, we first manually identify causal and spurious features with principles inspired from the counterfactual framework of causal inference. Then, we propose a regularization approach to penalize causal and spurious features separately. By adjusting the strength of the penalty for each type of feature, we build a predictive model that relies more on causal features and less on non-causal features. We conduct experiments to evaluate model robustness and fairness on three datasets with multiple metrics. Empirical results show that the new models built with causal awareness significantly improve model robustness with respect to counterfactual texts and model fairness with respect to sensitive attributes.

Parallel to the rising debates over sustainable energy and artificial intelligence solutions, the world is currently discussing the ethics of artificial intelligence and its possible negative effects on society and the environment. In these arguments, sustainable AI is proposed, which aims at advancing the pathway toward sustainability, such as sustainable energy. In this paper, we offered a novel contextual topic modeling combining LDA, BERT, and Clustering. We then combined these computational analyses with content analysis of related scientific publications to identify the main scholarly topics, sub-themes, and cross-topic themes within scientific research on sustainable AI in energy. Our research identified eight dominant topics including sustainable buildings, AI-based DSSs for urban water management, climate artificial intelligence, Agriculture 4, the convergence of AI with IoT, AI-based evaluation of renewable technologies, smart campus and engineering education, and AI-based optimization. We then recommended 14 potential future research strands based on the observed theoretical gaps. Theoretically, this analysis contributes to the existing literature on sustainable AI and sustainable energy, and practically, it intends to act as a general guide for energy engineers and scientists, AI scientists, and social scientists to widen their knowledge of sustainability in AI and energy convergence research.

Any convictions will be evaluated to determine if they directly relate to the responsibilities and requirements of the position. Having a conviction history will not automatically disqualify an applicant from being considered for employment.

Upon registration with Studyum, students upload their desired learning objectives and learning styles, and take certain tests to determine their ideal courses and teachers. This information, which is encrypted to an individual's wallet, is used to generate AI-driven'Smart Chat' that continuously learns about the factors that contribute most to their ability to learn. The more classes you take on Studyum, the more the platform learns about you, and the more personalized and effective your education will become. As you start doing better or worse in certain subjects, the platform adjusts the educational content being delivered, to maximize learning in real-time. Studyum pulls AI and blockchain together to provide custom solutions for different types of educational institutions or companies.

Watson is among millions of teachers across the nation who are in their second year of teaching either in-person, online or both -- depending on the state, city and district they live in. Like many other professions, teachers' jobs have become increasingly complex due to the pandemic. This year, many students are back in the classroom, but teachers have to constantly adapt if there is virus exposure. There aren't specific guidelines on how best to teach students using the many technologies that are available. Teachers are also struggling to keep students engaged while learning new tech tools that are required to make online classes successful.

Artificial Intelligence for Business, Solve Real World Business Problems with AI Solutions Rating: 4.5 out of 5 Created by Hadelin de Ponteves, Kirill Eremenko, SuperDataScience Team English [Auto], French [Auto]Preview this Course - GET COUPON CODE Structure of the course: Part 1 - Optimizing Business Processes Case Study: Optimizing the Flows in an E-Commerce Warehouse AI Solution: Q-Learning Part 2 - Minimizing Costs Case Study: Minimizing the Costs in Energy Consumption of a Data Center AI Solution: Deep Q-Learning Part 3 - Maximizing Revenues Case Study: Maximizing Revenue of an Online Retail Business AI Solution: Thompson Sampling Real World Business Applications: With Artificial Intelligence, you can do three main things for any business: Optimize Business Processes We will show you exactly how to succeed these applications, through Real World Business case studies. And for each of these applications we will build a separate AI to solve the challenge. In Part 1 - Optimizing Processes, we will build an AI that will optimize the flows in an E-Commerce warehouse. In Part 2 - Minimizing Costs, we will build a more advanced AI that will minimize the costs in energy consumption of a data center by more than 50%! Just as Google did last year thanks to DeepMind.

Data is information collected through observations. It is often a set of qualitative and quantitative variables or a compilation of both. Data often entered in a system can have multiple layers of issues while retrieving, which in most cases will cause you to clean the data before you can make sense of the same and process the same to come up with actionable insights. Data cleaning is a very crucial first step in any machine learning project. It is an inevitable step in the process of model building and data analysis, but no one really can or tells you how to go about the same.

Tilt 365, a strengths assessment and team development disruptor with educational tools for its network of certified coaches, announced Erika Bill-Peter was appointed as Chief Learning Officer (CLO) to lead the expansion of the company's coaching and organizational development (OD) tools and services. In a strong position to empower organizations to create an agile culture in today's hybrid-work environment, Tilt 365 grew its revenues by 20% last year, during the height of the pandemic and has seen a 46% revenue increase year-to-date (YTD) in 2021. Tilt assessments have been used by more than 1,000 organizations and the company recently added new customers including Atlassian, DoorDash, HelloFresh UK and Google. With more than 20 years of experience, Erika Bill-Peter is an International Coach Federation (ICF) certified coach who has served as an OD consultant for external firms as well as in-house at Bose Corporation. In addition, she will lead the evolution of groundbreaking development offerings that will build on the long-term research of the Tilt model, such as the laser coaching certification that she launched when joining.

Video Production can be time-consuming, difficult, technical and expensive, but it doesn't have to be. This video production course is about how to do simple, easy talking head videos for a wide range of business communication needs. There is a video explosion going on in the online world. Are you unsure where to start? This course will lead you through the simplest and easiest ways to start communicating with your customers, clients, prospects and colleagues in the most effective manner: talking head video.