Udemy Course Data Science & Machine Learning For Non Technical Executives NED Data Science & Machine Learning For Non Technical Executives free download also includes 8 hours on-demand video, 3 articles, 34 downloadable resources, Full lifetime access by Ankit Mistry Basic idea bout Machine learning technology Different ML algorithm like Regression, Classification & Clustering KNN and Logistic Regression algorithm Linear and Multiple Regression K means Clustering algorithm Overview about Deep Learning, Computer Vision Field Description Welcome to course on Data Science & Machine Learning For Non Technical Executives. Disclaimer: This is not python based machine learning course. I would highly suggest you not to enroll in this course if you are interested in implementation part of machine learning algorithm. There are many course on Udemy which teach machine learning with R/Python. I have designed this course for absolute beginner and non technical people who just want to start diving into machine learning world.

The "Introduction to Machine Learning" course is built by journalists, for journalists, and it will help answer questions such as: What is machine learning?

The young journalists at YR Media (formerly Youth Radio) were curious about "what artificial intelligence means for race, art, and the apocalypse." So they asked the opinion of a a few experts, including tech journalist Alexis Madrigal, engineer Deb Raji of New York University's AI Now Institute, artist/programmer Sam Lavigne, and AI ethicisit Rachel Thomas.

Machine learning is an intimidating topic to tackle for the first time. The term encompasses so many fields, research topics and business use cases, that it can be difficult to even know where to start. To combat this, it's often a good idea to turn to textbooks that will introduce you to the basic principles of your new field of research. This holds true for AI and machine learning, especially if you have a background in statistics or programming. When used alongside more focused online articles like our introduction to training data, they can be an essential part of a powerful toolkit with which to learn and grow.

Ensemble smoothers are among the most successful and efficient techniques currently available for history matching. However, because these methods rely on Gaussian assumptions, their performance is severely degraded when the prior geology is described in terms of complex facies distributions. Inspired by the impressive results obtained by deep generative networks in areas such as image and video generation, we started an investigation focused on the use of autoencoders networks to construct a continuous parameterization for facies models. In our previous publication, we combined a convolutional variational autoencoder (VAE) with the ensemble smoother with multiple data assimilation (ES-MDA) for history matching production data in models generated with multiple-point geostatistics. Despite the good results reported in our previous publication, a major limitation of the designed parameterization is the fact that it does not allow applying distance-based localization during the ensemble smoother update, which limits its application in large-scale problems. The present work is a continuation of this research project focusing in two aspects: firstly, we benchmark seven different formulations, including VAE, generative adversarial network (GAN), Wasserstein GAN, variational auto-encoding GAN, principal component analysis (PCA) with cycle GAN, PCA with transfer style network and VAE with style loss. These formulations are tested in a synthetic history matching problem with channelized facies. Secondly, we propose two strategies to allow the use of distance-based localization with the deep learning parameterizations.

In this tutorial you will learn how to perform Human Activity Recognition with OpenCV and Deep Learning. Our human activity recognition model can recognize over 400 activities with 78.4-94.5% accuracy (depending on the task). To learn how to perform human activity recognition with OpenCV and Deep Learning, just keep reading! In the first part of this tutorial we'll discuss the Kinetics dataset, the dataset used to train our human activity recognition model. From there we'll discuss how we can extend ResNet, which typically uses 2D kernels, to instead leverage 3D kernels, enabling us to include a spatiotemporal component used for activity recognition.

The AI Experience Centre invites you to participate in the first session of its webinar series all of which focuses on the key take aways of the recent COVID-19 outbreak across a variety of sectors. Upon your registration we will send you the URL of the webinar via e-mail. The senior researchers will discuss the role of social media & fake news analysis in times of the COVID19 crisis. The webinar will also shed light on the key technological challenges the scientific community encountered to then finally provide insights for media players and policy-makers based on this experience. Come prepared and you will get the chance to ask questions from experts at the end of the sessions.

Learning Path Recommendation is the heart of adaptive learning, the educational paradigm of an Interactive Educational System (IES) providing a personalized learning experience based on the student's history of learning activities. In typical existing IESs, the student must fully consume a recommended learning item to be provided a new recommendation. This workflow comes with several limitations. For example, there is no opportunity for the student to give feedback on the choice of learning items made by the IES. Furthermore, the mechanism by which the choice is made is opaque to the student, limiting the student's ability to track their learning. To this end, we introduce Rocket, a Tinder-like User Interface for a general class of IESs. Rocket provides a visual representation of Artificial Intelligence (AI)-extracted features of learning materials, allowing the student to quickly decide whether the material meets their needs. The student can choose between engaging with the material and receiving a new recommendation by swiping or tapping. Rocket offers the following potential improvements for IES User Interfaces: First, Rocket enhances the explainability of IES recommendations by showing students a visual summary of the meaningful AI-extracted features used in the decision-making process. Second, Rocket enables self-personalization of the learning experience by leveraging the students' knowledge of their own abilities and needs. Finally, Rocket provides students with fine-grained information on their learning path, giving them an avenue to assess their own skills and track their learning progress. We present the source code of Rocket, in which we emphasize the independence and extensibility of each component, and make it publicly available for all purposes.

These six brief videos, recorded in 2020, contain ideas and suggestions from Professor Strang about the recommended order of topics in teaching and learning linear algebra. The first topic is called A New Way to Start Linear Algebra. The key point is to start right in with the columns of a matrix A and the multiplication Ax that combines those columns.That leads to The Column Space of a Matrix and the idea of independent columns and the factorization A = CR that tells so much about A. With good numbers, every student can see dependent columns.The remaining videos outline very briefly the full course: The Big Picture of Linear Algebra; Orthogonal Vectors; Eigenvalues & Eigenvectors; and Singular Values & Singular Vectors. Singular values have become so important and they come directly from the eigenvalues of A'A.You can see this new idea developing in the first video lecture of Professor Strang’s 2019 course 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning.

Leeds Digital Festival 2020 How to be loved by Google - LIVE SEO Audits! Leeds Digital Festival 2020 Be strategic with your marketing, putting a simple plan together - Leeds Digital Festival 2020 Marketing in a crisis: How brands learn, survive, and grow @ The Leeds Digital Festival April 2020. Maintaining business culture in a remote world webinar Delivering public and voluntary sector projects in a remote world webinar Advanced Google Analytics for SEO & Beyond Patric Curran Tech Nation Talks: Yorkshire Setting SEO targets in 2020: how to beat Google's ad engine Duncan Colman Using Natural Language Processing to support E.A.T Steve Bailey The Adventures of a PM and BA in Agile Land by Katherine Zambaldi (AND Digital) Building Practical Blazor Apps in C# by Peter'Shawty' Shaw Diversity & Inclusion by Ben Collingwood Twitter and Hootsuite Workshop with Amanda Dixon I DKE Webinars by Digital Enterprise LinkedIn Profiles: What You Really Need To Be Saying with Judy Parsons I DKE ...