The Applied Data Science: Machine Learning program will give you hands-on experience in one of the hottest areas of data science. You will learn tools for predictive modeling and analytics, harnessing the power of neural networks and deep learning techniques across a variety of types of data sets. Each of the four courses in this program will let you demonstrate your newly-acquired skills through a course project. ECTS credits will be awarded to learners who successfully complete all four courses and course projects as well as a final capstone project. These course details are subject to change; please refer to the program outline at the time of registration.

Vectors are a foundational element of linear algebra. Vectors are used throughout the field of machine learning in the description of algorithms and processes such as the target variable (y) when training an algorithm. In this tutorial, you will discover linear algebra vectors for machine learning. A Gentle Introduction to Vectors for Machine Learning Photo by Lachlan Donald, some rights reserved. Take my free 7-day email crash course now (with sample code).

The reality is that it's neither one. The fear is that automation is sweeping all before it, gobbling up jobs; displacing millions of workers and leaving them unemployed and, worse, unemployable; and exacerbating the income gap. It's reviled by many as a greater threat than jobs shipped overseas, even prompting some to suggest taxing robots to slow their spread. The counterview is that automation is not replacing jobs nearly fast enough. We don't have enough workers to do the jobs available now, and this will get worse as demographic trends play out.

In this DZone Refcard, learn about TensorFlow, an open-source library with a rich set of application programming interfaces for most major languages and environments needed for deep learning programs like sentiment analysis and object detection.

Webinar: Tuesday, February 13, 1:00 pm ET / 10:00 am PT Register now Building predictive applications allows companies to respond to new threats and take advantage of developing opportunities. But executing these new applications against high-volume event streams with sub-second latency requires a powerful combination of machine learning and streaming analytics. In this webinar, you'll learn how to create and evaluate new machine learning models with DataRobot and deploy them within the SQLstream Blaze streaming analytics engine - so that you can identify risk in real-time and prevent fraud as it happens - rather than after the fact. On this 45-minute webinar, you'll discover how Automated Machine Learning and Streaming Analytics provides: - Automated machine learning models that can be created by anyone - Rapid deployment against incoming, high-volume events with extremely low-latency - The ability to update those models seamlessly - with no downtime - Deep transparency, including prediction reason codes, to enable rapid, targeted investigations Speakers: Greg Michaelson, PhD - Head of DataRobot Labs David Hickman - Senior Director, Product Marketing, SQLstream Register now

Data is being generated in huge quantities everywhere. Twitter generates 12 TB of data every day, Facebook generates 25 TB of data everyday and Google generates much more than these quantities everyday. Conventional models on such huge data are infeasible. All these data contribute to prediction. A good algorithm can take in such variety of data.

Finally, a comprehensive hands-on machine learning course with specific focus on regression based models for the investment community and any passionate investors. In the past few years, there has been a massive adoption and growth in the use of data science, artificial intelligence and machine learning to find alpha. However, information on and application of machine learning to investment are scarce. This course has been designed to address that. It is meant to spark your creative juices.

Organizations everywhere, are aiming to embed AI in everything they do. But while 85 percent of companies surveyed believe AI will help them outpace competition, only a handful of them have gotten started. While seemingly daunting, there are foundational technologies available now to all organizations. Most likely these pieces are already in place. Your company's journey to AI has already begun and is ready to be accelerated.

One only needs to type in the following command to start finding the most optimal learning rate to use before training a neural network. At this juncture we've covered what learning rate is all about, it's importance, and how can we systematically come to an optimal value to use when we start training our model. Next we would go through how learning rates can still be used to improve our model's performance.

Hurry--early price ends March 9. This post was originally published on Insight Data Science; it is republished here with permission. Whether you are an established company or working to launch a new service, you can always leverage text data to validate, improve, and expand the functionalities of your product. The science of extracting meaning and learning from text data is an active topic of research called natural language processing (NLP). NLP produces new and exciting results on a daily basis, and is a very large field.