If you are a software developer who wants to learn how machine learning models work and how to apply them effectively, this book is for you. Familiarity with machine learning fundamentals and Python will be helpful, but is not essential. This book examines machine learning models including logistic regression, decision trees, and support vector machines, and applies them to common problems such as categorizing documents and classifying images. It begins with the fundamentals of machine learning, introducing you to the supervised-unsupervised spectrum, the uses of training and test data, and evaluating models. You will learn how to use generalized linear models in regression problems, as well as solve problems with text and categorical features. You will be acquainted with the use of logistic regression, regularization, and the various loss functions that are used by generalized linear models.

A few weeks ago I was emailing Tom Blomfield, the CEO of startup bank Monzo, to arrange lunch. He passed me over to his assistant, Amy Ingram, by CCing her into an email. Amy and I exchanged eight emails fixing up a date and then another five when Blomfield had to rearrange. Only then did I spot something odd in Amy's email signature: "Artificial intelligence for scheduling meetings." It turns out that I had been talking to an algorithm the whole time.

Prominent researcher Andrew Ng has stated that "AI is the new electricity. " Indeed, AI is already a silent force behind the scenes in many of our interactions with the digital world -- from the news in your feed to the next show to suggest after a binge session of Stranger Things (seriously, where did my eight hours go?). Tensorflow, Scikit), AI is on an unprecedented path towards democratization. However, democratization is the first step towards commoditization. So if AI does not automatically make a product amazing, what is it for?

Today's Web--characterized by social media and user-generated content--is a powerful, open medium that gives everyone a voice. Unfortunately, some use their voices to bully or harass other users. Social media platforms, online bulletin boards, blog sites, media companies, and anyone else who opens up posts to comments, struggle to identify and deter online harassment. So Intel has joined with a number of other organizations to create Hack Harassment--a collaborative effort to reduce the prevalence and severity of online harassment through increasing awareness and accountability, advancing anti-harassment technology solutions, and effecting change for individuals and communities. Online harassment is a large and growing problem.

According to a set of intelligent humans interviewed for this story, artificial intelligence (AI) and machine learning are going to help drive the tech economy in 2017. When CIO.com posted a query on Help a Reporter Out, a site designed to help journalists connect with sources, asking about startup trends to watch in 2017, the overwhelming majority of respondents pointed to AI. This coming year and beyond, AI will help companies "disrupt sectors that haven't been fully disrupted," says Anthony Glomski, principal of AG Asset Advisory, a financial advisory firm. "AI is in its beginning stages with massive potential impact." Here are eight startup categories and trends experts believe will be big in 2017.

The less constrained model indeed performs better (judging by AIC, which drops from to 3696 to 3278). Autocorrelation of errors also is reduced overall. Now, with the improved models, let's finally get forecasting!

Amazon's voice assistant Alexa has become a hugely popular and growing business. In fact, David Limp, an Amazon senior vice president who oversees Alexa and all of its Amazon devices, says that Alexa is rapidly adding "skills," with more than 1,000 people working on it. On Tuesday, at Fortune's Brainstorm Tech conference, Limp spoke to Fortune's Adam Lashinsky about the inspiration for Alexa (hint: Think Star Trek) and the origin of the name to where the business is heading. Here is the lightly-edited transcript. Dave Limp: The device business is less about building hardware for customers and more about building services behind that hardware. So the original vision of Kindle was to deliver any book ever written in less than 60 seconds, and that was all about creating a cloud-based service that had a great catalogue of books, great selection, and great prices. And as we've rolled out devices since then, everything from Fire TV to, as you mentioned, Echo and Alexa and everything in between, it's about creating that backend service that constantly improves and adds value for customers, and isn't just a gadget but instead a full end to end service that can benefit what customers want.

In this course, intended to expand upon your knowledge of neural networks and deep learning, you'll harness these concepts for computer vision using convolutional neural networks. Going in-depth on the concept of convolution, you'll discover its wide range of applications, from generating image effects to modeling artificial organs. Explore the StreetView House Number (SVHN) dataset using convolutional neural networks (CNNs) Build convolutional filters that can be applied to audio or imaging Extend deep neural networks w/ just a few functions Note: we strongly recommend taking The Deep Learning & Artificial Intelligence Introductory Bundle before this course. The Lazy Programmer is a data scientist, big data engineer, and full stack software engineer. For his master's thesis he worked on brain-computer interfaces using machine learning.

A few weeks ago I was emailing Tom Blomfield, the CEO of startup bank Monzo, to arrange lunch. He passed me over to his assistant, Amy Ingrams, by CCing her into an email. Amy and I exchanged eight emails fixing up a date and then another five when Blomfield had to rearrange. Only then did I spot something odd in Amy's email signature: "Artificial intelligence for scheduling meetings." It turns out that I had been talking to an algorithm the whole time.

Vertical markets such as the automotive, agriculture and healthcare have long been impervious, if not indifferent to many of the new technology trends sweeping the corporate business world. The absence of early adopters, the dependence on proprietary technology systems, and uncomfortably low product margins made these markets unappealing to cutting edge technology providers that could have transformed them. While each of these vertical markets is distinct, the rise of autonomous machines and services, which perform activities much more efficiently than humans ever could, will disrupt the current way of business in all markets whether on the road, in the field or in the hospital. Autonomous software based on advanced neural networks and deep learning will rapidly find its way into existing business and consumer products, but its potential to catapult traditional vertical industries into the 21 century is what has us most excited. Rather than resist the inevitable, previously conservative market players in non-tech verticals are beginning to engage with startups to be among the first to leverage the promise of autonomous technology.