There's no doubt about it - Data Science is big news right now. We see it on the news every day, the increasing number of news stories about Big Data, the Internet of Things, Deep Learning, Artificial Intelligence, smart cars, smart cities, smart politicians. OK, maybe I went a bit too far with that last one... Every month I get an email from Udemy telling me which courses are their best sellers. The list isn't about Data Science, but there are always plenty of Data Science courses right up there at the top of the list. We decided to share this resource with you, and so here are Udemy's top selling courses.

Join us at the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017) for a full day workshop that will bring together international stakeholders in robotics to examine best practices for accelerating robotics innovation through strategic policy frameworks. This is a unique opportunity to learn from people who have played a significant role in designing and implementing major strategic robotics initiatives around the globe. Objectives In the past decade, a number of governing bodies and industry consortia have developed strategic roadmaps to guide investment and development of robotic technology. With the roadmaps from the US, South Korea, Japan and EU etc. well underway, the time is right to take stock of these strategic robotics initiatives to see what is working, what is not, and what best practices in roadmap development might be broadly applied to other regions. The objective of this two-part workshop is to examine the process of how these policy frameworks came to be created in the first place, how they have been tailored to local capabilities and strengths, and what performance indicators are being used to measure their success -- so that participants may draw from international collective experience as they design and evaluate strategic robotics initiatives for their own regions.

Imbalanced classes put "accuracy" out of business. This is a surprisingly common problem in machine learning (specifically in classification), occurring in datasets with a disproportionate ratio of observations in each class. Standard accuracy no longer reliably measures performance, which makes model training much trickier. In this guide, we'll explore 5 effective ways to handle imbalanced classes. Let's say your client is a leading research hospitals, and they've asked you to train a model for detecting a disease based on biological inputs collected from patients.

When I was writing books on networking and programming topics in the early 2000s, the web was a good, but an incomplete resource. Blogging had started to take off, but YouTube wasn't around yet, nor was Quora, Twitter, or podcasts. Over ten years later as I've been diving into AI and machine learning, it is a completely different ballgame. There are so many resources -- it's difficult to know where to start (and stop)! To save you some of the effort I went through in researching all the different nooks and crannies of the web to find the best content; I've organized them into a big collection here.

A time series is a sequentially indexed representation of your historical data that can be used to solve classification and segmentation problems, in addition to forecasting future values of numerical properties, for example, air pollution level in Madrid for the last two days. This is a very versatile method often used for predicting stock prices, sales forecasting, website traffic, production and inventory analysis, or weather forecasting, among many other use cases. Soon, BigML will have time series as a new resource. Following our mission of democratizing machine learning and making it easy for everyone, we will provide new learning material for you to start with time series from scratch and become a power user over time. We start by publishing a series of six blog posts that will progressively dive deeper into the technical and practical aspects of time series with an emphasis on time series models for forecasting.

Machine learning is a hot topic for developers, but where can one learn about how to use the technology? A lot depends on your current background and your long-term goals. I have already written about the basic differences between machine-learning techniques, but this was done at a relatively high level. Getting into the details can range from learning about machine-learning methodologies at an abstract level to examining deep-learning frameworks used to develop applications. Here, we'll take a more detailed look at some of the online resources available to you, and include links to websites with much more information about machine-learning classes, frameworks, and resources.

Today's highlighted deal comes via our Online Courses section of the Neowin Deals store, where you can save 95% off* this Complete iOS 11 Machine Learning Masterclass. Become a master of smart app development. Ready to start building professional, career-boosting mobile apps? Machine learning is one of the fastest growing fields in tech and many apps are starting to integrate machine learning to add a layer of intelligence. Only the smart will survive. In this comprehensive course, you'll learn how to build apps for the brand new iOS 11 while incorporating machine learning, turning them into smart apps that will interact and learn from users to enhance the user experience.

Time series datasets may contain trends and seasonality, which may need to be removed prior to modeling. Trends can result in a varying mean over time, whereas seasonality can result in a changing variance over time, both which define a time series as being non-stationary. Stationary datasets are those that have a stable mean and variance, and are in turn much easier to model. Differencing is a popular and widely used data transform for making time series data stationary. In this tutorial, you will discover how to apply the difference operation to your time series data with Python.

The data for your sequence prediction problem probably needs to be scaled when training a neural network, such as a Long Short-Term Memory recurrent neural network. When a network is fit on unscaled data that has a range of values (e.g. In this tutorial, you will discover how to normalize and standardize your sequence prediction data and how to decide which to use for your input and output variables. How to Scale Data for Long Short-Term Memory Networks in Python Photo by Mathias Appel, some rights reserved. There are two types of scaling of your series that you may want to consider: normalization and standardization.

Sure, there are lots of tutorials and overviews on gaining the insight you need into picking up machine learning, but many (most?) of them take the long view: get a foundation first, learn the basics next, then learn a bit of complementary theory before getting too far ahead of yourself in practical terms, take a step back, try your hand at a few examples, undertake a project on your own... This is all great advice, and a great approach to learning... well, almost anything. But let's say you're not starting from scratch. Or you don't have the patience to go through all of the motions. Let's say you want to hit the ground running and scramble under pressure to learn everything right now.