IBM provided an Analytics Assessment & Insights Impact Grant to the Shenzhen CDC to help further their mission of infectious disease prevention and control, shouldering the monitoring, alarming and treatment of emergency public health events in Shenzhen for the 12 million citizenry by building a self adaptive online machine learning module that provides cognitive-based modeling for epidemic disease prediction and analysis on case number and trend. The output of this work has allowed the organization to develop prediction models to help forecast seasonal flu outbreaks and provide information to citizens on affected areas. CDC organizations across China are now considering the implementation of this solution to help track flu and other infectious diseases.

I am a Machine Learning Engineer, Deep Learning Engineer and even an Indie Game Developer with a Major in Compilers and a Master's degree in Artificial Intelligence from University Politehnica of Bucharest. I am passionate about Games and Artificial Intelligence. I love to give life to A.I. agents in my project or my friend's projects and I want to teach you too.

This is part of a new series of articles: once or twice a month, we post previous articles that were very popular when first published. These articles are at least 6 month old but no more than 12 month old. The previous digest in this series was posted here a while back. Below is our fourth edition. Top 20 Big Data Experts to Follow (Includes Scoring Algorithm) Text Classification & Sentiment Analysis tutorial / blog Learn Everything about Sentiment Analysis using R 1.5 TB dataset of anonymized user interactions released by Yahoo Fuzzy Matching Algorithms To Help Data Scientists Match Similar Data

Data Science Middle East Foundation in partnership with EVERATI running 3-day training workshop series across Middle East to get you started on your data science and machine learning journey, as you learn how to use data and science to deliver insights, value and innovation. Data Science and Machine Learning workshop is a 3-day practical training program for applied introduction to data science industry practices and models of machine learning. The workshop has a strong focus on gaining hands-on experience implementing algorithms and building predictive models on real datasets. By the end of the workshop, participants will be ready to implement the machine learning algorithms using data science on their own data, and immediately generate business value. The workshop will take participants through the conceptual and applied foundations of the subject.

One day we will "think about machine learning the way we think about electricity: It's hard to imagine the world without it," said SAP Chief Innovation Officer Juergen Mueller at the recent SAP TechEd Barcelona. Under Mueller, SAP has embarked on a journey to bring machine learning to business around the world, essentially "electrifying" all applications with this technology. For the uninitiated, machine learning takes Big Data, runs it against sophisticated algorithms and helps applications to learn from this information. Massively improved computing power makes this possible in real time. Most importantly, it allows applications to "think" and independently resolve problems – going beyond what they were explicitly programmed to do, and often what humans can do.

This course aimed at students with beginner to intermediate skill in F#, basic understanding of the F# syntax and a light functional understanding would be beneficial. You'll also need a computer with Linux, OSX or Windows with F# installed and an internet connection. Have you wanted to understand how to'do' machine learning or implement algorithms from a textbook in a programming language, or deploy a library to Nuget? Well, this course includes sections on machine learning using a mathematical theorem known as Bayes' Theorem. We will start by creating a predictive text engine and deploy it to Nuget, while learning how to write some basic unit tests in FsUnit.

This team has decades of practical experience in working with Java and with billions of rows of data. If you are an analyst or a data scientist, you're used to having multiple systems for working with data. With Spark, you have a single engine where you can explore and play with large amounts of data, run machine learning algorithms and then use the same system to productionize your code. Scala: Scala is a general purpose programming language - like Java or C . It's functional programming nature and the availability of a REPL environment make it particularly suited for a distributed computing framework like Spark. Analytics: Using Spark and Scala you can analyze and explore your data in an interactive environment with fast feedback.

Like the course I just released on Hidden Markov Models, Recurrent Neural Networks are all about learning sequences - but whereas Markov Models are limited by the Markov assumption, Recurrent Neural Networks are not - and as a result, they are more expressive, and more powerful than anything we've seen on tasks that we haven't made progress on in decades. So what's going to be in this course and how will it build on the previous neural network courses and Hidden Markov Models? In the first section of the course we are going to add the concept of time to our neural networks. I'll introduce you to the Simple Recurrent Unit, also known as the Elman unit. We are going to revisit the XOR problem, but we're going to extend it so that it becomes the parity problem - you'll see that regular feedforward neural networks will have trouble solving this problem but recurrent networks will work because the key is to treat the input as a sequence.

But when is the last time you saw someone building a house with a hammer, a hand saw and some 2x4s? When you build a house, you need the right tools and materials to build a house. The basic ingredients for learning are neurons and myelin. Each time you fire a set of neurons while learning, they get wrapped in another thin layer of myelin, which is like insulation on an electric cord. The more the neurons get wrapped up, the faster the neurons can send signals.

I am a data scientist, big data engineer, and full stack software engineer. For my masters thesis I worked on brain-computer interfaces using machine learning. These assist non-verbal and non-mobile persons communicate with their family and caregivers. I have worked in online advertising and digital media as both a data scientist and big data engineer, and built various high-throughput web services around said data. I've created new big data pipelines using Hadoop/Pig/MapReduce. I've created machine learning models to predict click-through rate, news feed recommender systems using linear regression, Bayesian Bandits, and collaborative filtering and validated the results using A/B testing.