Based on Apache OpenWhisk, IBM Cloud Functions is a Functions as a Service (FaaS) platform that makes it easy to build and deploy serverless applications. In this tutorial, you'll build a serverless application using IBM Cloud Functions that monitors the content of a Cloud Object Storage bucket and analyzes the content of images that are uploaded to the bucket by a human or an automated process. For illustrative purposes, analysis is performed by a deep learning microservice from the Model Asset eXchange and analysis results are stored as JSON files in the same bucket. You can easily adapt the outlined approach to take advantage of hosted cognitive services, such as those provided by IBM Watson, and to store results in a NoSQL datastore like Cloudant or a relational database. By completing this introductory tutorial, you learn how to monitor a Cloud Object Storage bucket for changes (new objects, updated objects, or deleted objects) using Cloud Functions and how to use deep learning microservices from the Model Asset eXchange to automatically analyze those objects in near real time.

Density estimation is the problem of estimating the probability distribution for a sample of observations from a problem domain. There are many techniques for solving density estimation, although a common framework used throughout the field of machine learning is maximum likelihood estimation. Maximum likelihood estimation involves defining a likelihood function for calculating the conditional probability of observing the data sample given a probability distribution and distribution parameters. This approach can be used to search a space of possible distributions and parameters. This flexible probabilistic framework also provides the foundation for many machine learning algorithms, including important methods such as linear regression and logistic regression for predicting numeric values and class labels respectively, but also more generally for deep learning artificial neural networks.

Have you ever felt overwhelmed by the sheer number of images or documents, or hours of video footage you needed to sort through for a report? Training a machine to do the work for you may be the answer. Learn how artificial intelligence can improve your reporting with the new course from the Knight Center for Journalism in the Americas and instructor John Keefe, "Hands-on Machine Learning Solutions for Journalists." The four-week Big Online Course (BOC) runs from Nov. 18 to Dec. 15, 2019 and costs $95, which includes a certificate for those who successfully complete the course requirements. "At the end of this class, students will have a much better understanding of machine learning. They will actually be able to sort documents, especially images, based on the criteria they set up," said Keefe, who uses these techniques in his work as investigations editor at Quartz.

Machine learning is a branch of Artificial Intelligence (AI) that presents systems with the ability to learn automatically to increase their accuracy without being programmed. The primary aim is to enable the machine systems to learn on their own, without any form of human intervention. Even though most people must have heard about it, only a few fully understand what it is and its benefits to eLearning. There are many benefits of machine learning for online training. However, one needs to make use of the best practices to achieve the benefits and deliver a better Learning Experience.

While siloed systems have created barriers to generating analytic insights, tools are either limited to specialized functions or require complex skills. To address these challenges, in this meetup, you will learn how you can be empowered with industry-leading, AI-powered, self-service analytics capabilities for data preparation, visualization, enterprise reporting, augmented analysis, and natural language processing. Break through those barriers by allowing stakeholders of all analytic-skill levels to collaborate confidently with a common foundation for sharing knowledge. Autonomous features make it easy to setup and scale! Brendan Doyle is Senior Product Manager, Data & Analytics at Oracle.

UPDATE Oct, 2019: We just added a new season with 4 new podcasts focused on artificial intelligence, machine learning, data science, and data orchestration. Building a data foundation is essential to driving innovation. This is just as true for mid-market companies as for large enterprise companies. Mid-market and large enterprise companies have different challenges, so we've brought together experts from each size company to discuss key trends that are reshaping the way successful companies use their data: from data management and data foundation to spatial and machine learning to data-based process and information excellence. Listen to this chat series on all things data!

You'll probably agree that there's a widespread perception that technological advances and artificial intelligence (AI) will replace human resources professionals. As artificial intelligence keeps evolving, you can take advantage and turn your passion into a profitable career. In this article, therefore, you're going to learn the basic concepts of artificial intelligence and how you can integrate AI in your HR tasks. Once you understand the basic concepts, you'll become an unstoppable and valuable manager for your organization. Artificial intelligence (AI) is the science behind the transformation of machines into more intelligent entities. In other words, machines and software are designed to simulate the behavior and thoughts of human beings.

WordPress is a powerful platform for creating feature-rich and attractive websites but, with a little extra tweaking and effort, your WordPress site can dominate search engines and bring thousands of new customers to your business. WordPress Search Engine Optimization will show you the secrets that professional SEO companies use to take websites to the top of search results. You'll take your WordPress site to the next level; you'll brush aside even the stiffest competition with the advanced tutorials in this book.

Along with the latest PyTorch 1.3 release came with the next generation ground-up rewrite of its previous object detection framework, now called Detectron2. This tutorial will help you get started with this framework by training an instance segmentation model with your custom COCO datasets. If you know how to create COCO datasets, please read my previous post -- How to create custom COCO data set for instance segmentation. For a quick start, we will do our experiment in a Colab Notebook so you don't need to worry about setting up the development environment on your own machine before getting comfortable with Pytorch 1.3 and Detectron2. In the Colab notebook, just run those 4 lines to install the latest Pytorch 1.3 and Detectron2.

This course is designed for any graduates as well as Software Professionals who are willing to learn data science in simple and easy steps using R programming, Python Programming, WEKA tool kit and SQL. Data is the new Oil. This statement shows how every modern IT system is driven by capturing, storing and analysing data for various needs. Be it about making decision for business, forecasting weather, studying protein structures in biology or designing a marketing campaign. All of these scenarios involve a multidisciplinary approach of using mathematical models, statistics, graphs, databases and of course the business or scientific logic behind the data analysis.