This is Numpy and Pandas for Beginners course. An excellent choice for both beginners and experts looking to expand their knowledge on one of the most popular Python libraries in the world! If you've spent time in a spreadsheet software like MS Excel or Google Sheets and want to take your data analysis skills to the next level, this course is for you! Pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python.

The future is now, or soon will be, at the Arlington Career Center. Arlington School Board members on Oct. 27 approved a one-year, $10,000 grant in support of the new artificial-intelligence club that is starting at the school. The program will be overseen by physics instructor Ryan Miller in collaboration with Inspirit AI, which provides curriculum for middle-school and high-school students in the artificial-intelligence field.

Let me tell you my story. I graduated with my Ph. D. in computational nano-electronics but I have been working as a data scientist in most of my career. My undergrad and graduate major was in electrical engineering (EE) and minor in Physics. After first year of my job in Intel as a "yield analysis engineer" (now they changed the title to Data Scientist), I literally broke into data science by taking plenty of online classes.

Machine Learning with Python Basics (For Beginners) - Learn the Basics of Machine Learning with Python (First Step For Beginners)

This is Numpy,Pandas and PostgreSQL for Beginners course. One question or concern I get a lot is that people want to learn deep learning and data science, so they take these courses, but they get left behind because they don't know enough about the Numpy stack in order to turn those concepts into code. Even if I write the code in full, if you don't know Numpy, then it's still very hard to read. This course is designed to remove that obstacle – to show you how to do things in the Numpy stack that are frequently needed in deep learning and data science. So what are those things?

With more companies leveraging software that runs on the Cloud, there is a growing need to find and hire individuals with the skills needed to build solutions on a variety of Cloud platforms. Employers agree: Cloud talent is hard to find. This Specialization is designed to address the Cloud talent gap by providing training to anyone interested in developing the job-ready, pragmatic skills needed for careers that leverage Cloud-native technologies. In the first course, you will learn how to build foundational Cloud computing infrastructure, including websites involving serverless technology and virtual machines, using the best practices of DevOps. The second course will teach you how to build effective Microservices using technologies like Flask and Kubernetes that are continuously deployed to a Cloud platform: Amazon Web Services (AWS), Azure or Google Cloud Platform (GCP).

Throughout the cognitive-science literature, there is widespread agreement that decision-making agents operating in the real world do so under limited information-processing capabilities and without access to unbounded cognitive or computational resources. Prior work has drawn inspiration from this fact and leveraged an information-theoretic model of such behaviors or policies as communication channels operating under a bounded rate constraint. Meanwhile, a parallel line of work also capitalizes on the same principles from rate-distortion theory to formalize capacity-limited decision making through the notion of a learning target, which facilitates Bayesian regret bounds for provably-efficient learning algorithms. In this paper, we aim to elucidate this latter perspective by presenting a brief survey of these information-theoretic models of capacity-limited decision making in biological and artificial agents.

The quintessential model-based reinforcement-learning agent iteratively refines its estimates or prior beliefs about the true underlying model of the environment. Recent empirical successes in model-based reinforcement learning with function approximation, however, eschew the true model in favor of a surrogate that, while ignoring various facets of the environment, still facilitates effective planning over behaviors. Recently formalized as the value equivalence principle, this algorithmic technique is perhaps unavoidable as real-world reinforcement learning demands consideration of a simple, computationally-bounded agent interacting with an overwhelmingly complex environment, whose underlying dynamics likely exceed the agent's capacity for representation. In this work, we consider the scenario where agent limitations may entirely preclude identifying an exactly value-equivalent model, immediately giving rise to a trade-off between identifying a model that is simple enough to learn while only incurring bounded sub-optimality. To address this problem, we introduce an algorithm that, using rate-distortion theory, iteratively computes an approximately-value-equivalent, lossy compression of the environment which an agent may feasibly target in lieu of the true model. We prove an information-theoretic, Bayesian regret bound for our algorithm that holds for any finite-horizon, episodic sequential decision-making problem. Crucially, our regret bound can be expressed in one of two possible forms, providing a performance guarantee for finding either the simplest model that achieves a desired sub-optimality gap or, alternatively, the best model given a limit on agent capacity.

Deep Learning & Deep Neural Networks made super easy for absolute beginners without digging deep into harsh mathematics. Want to master the essential Deep Learning concepts fast? Ready to train your machine like how a father would teach his son? Yes, we know you can choose from lots of similar courses and lectures out there regarding DNNs. But this truly step-by-step course is different!

We are running a series of webinars looking at recent exciting developments in AI, robotics and digital technologies including the work the DART-Ed programme are undertaking around education, and preparing the workforce with the knowledge and skills they will need, now and for the future. Digital technology is transforming how dentistry will be delivered in the future. Adopting digital opportunities will enable staff and patients to confidently navigate this new digital environment. This webinar will provide a scene setting to digital readiness in dentistry, as well as demonstrate the potential role of AI in dentistry and the interoperability challenge in the context of the profession. The third installment of the DART-Ed webinar series took place on 11 August 2022 and looked at how artificial intelligence has the potential to transform healthcare, and in many cases is starting to do so.