Knowing how good a set of predictions is, allows you to make estimates about how good a given machine learning model of your problem, In this tutorial, you will discover how to implement four standard prediction evaluation metrics from scratch in Python. You must estimate the quality of a set of predictions when training a machine learning model. As such, performance metrics are a required building block in implementing machine learning algorithms from scratch. These steps will provide the foundations you need to handle evaluating predictions made by machine learning algorithms.

In this online course, you will be introduced to the essential techniques of text mining, understood here as the extension of data mining's standard predictive methods to unstructured text. This course will discuss these standard techniques, and will devote considerable attention to the data preparation and handling methods that are required to transform unstructured text into a form in which it can be mined.

Welcome to the third installment of Learning AI if You Suck at Math. If you missed the earlier articles be sure to check out part 1, part 2, part 4, part 5, part 6 and part 7. Today we're going to build our own Deep Learning Dream Machine. This machine will slice through neural networks like a hot laser through butter. Other than forking over $129,000 for Nvidia's DGX-1, the AI supercomputer in a box, you simply can't get better performance than what I'll show you right here. Before we dig into building a DL beast, I want to give you the easiest upgrade path. If you don't want to build an entirely new machine, you still have one perfectly awesome option. Simply upgrade your GPU (with either a Titan X or a GTX 1080) and get VMware Workstation or use another virtualization software that supports GPU acceleration! Or you could simply install Ubuntu bare metal and if you need a Windows machine run that in a VM, so you max your performance for deep learning.

Researchers are breaking down the barrier between people and machines by teaching computers to recognize fear. On the 4th floor of the pristine Media Lab Complex at MIT lives a Nightmare Machine. These computers earned that nickname for a reason: they have been learning how to terrify people. A series of algorithms generates disturbing and grotesque images, like movie monsters, dead people, and other things that go bump in the night. "We wanted to playfully explore how artificial intelligence (AI) can become a demon that learns how to scare you," said Pinar Yanardag Delul, one of the creators of the gore-loving computer program.

In the past years, a collection of hardware, software and online service have managed to bring changes and reforms to classrooms and teaching methods. But the true disruption of education is yet to arrive. Artificial Intelligence has proven its role as a game changing factor in an increasing number of fields, causing transformations unimaginable in the past. It's now showing glimmers of how it might forever change the learning process, one of the oldest skills that mankind has mastered. Here's how AI and its derivatives are gradually finding their way into the classroom, and beyond.

This is a great project undertaken by Jordi Warmenhoven to implement the concepts from the book An Introduction to Statistical Learning with Applications in R by James, Witten, Hastie, Tibshirani (2013) in Python (the book has practical exercises in R, as you may have guessed). The book is freely available in as a PDF, which makes this repo even more attractive to those looking to learn.

Our world is generating more and more data, which people and businesses want to turn into something useful. This naturally attracts many data scientists – or sometimes called data analysts, data miners, and many other fancier names – who aim to help with this extraction of information from data. A lot of data scientists around me graduated in statistics, mathematics, physics or biology. During their studies they focused on individual modelling techniques or nice visualizations for the papers they wrote. Nobody had ever taken a proper computer science course that would help them tame the programming language completely and allow them to produce a nice and professional code that is easy to read, can be re-used, runs fast and with reasonable memory requirements, is easy to collaborate on and most importantly gives reliable results.

For an introduction to statistics, this tutorial with real-life examples is the way to go. The notebooks of this tutorial will introduce you to concepts like mean, median, standard deviation, and the basics of topics such as hypothesis testing and probability distributions. A fine way to start your stats learning, since it is inspired by the books "Think Bayes" and "Think Stats", which are two top recommendations that will come back below! If you're looking for books, you can try out this free book on computational statistics in Python, which not only contains an introduction to programming with Python, but also treats topics such as Markov Chain Monte Carlo, the Expectation-Maximization (EM) algorithm, resampling methods, and much more. Or you can buy this book by Thomas Haslwanter for a general introduction to common statistical tests, linear regression analysis and topics from survival analysis and Bayesian statistics. Note that this book does take life and medical sciences as an application area. Both of the above books already introduce you to more advanced statistics topics with Python too, as you can see. If you're a fan of videos, you should consider watching this tutorial on statistical data analysis with SciPy with Christopher Fonnesbeck, an Assistant Professor in the Department of Biostatistics at the Vanderbilt University School of Medicine.

Apple's about to hold its Worldwide Developers Conference – the event where it shows off the future of the company, and of all its products. The event is one of the biggest company in the world's biggest events. While there won't be a new iPhone revealed – that gets saved for its own event in September – there will be new iPhone software, and plenty of glimpses at where the handset might be headed. Here's everything we're expecting when Apple takes the stage for its big keynote presentation on 5 June. But with Apple the most reliable expectation is that there'll be a surprise, so while a lot has leaked it's sure not to be everything.