So, you have been thinking about picking up machine learning, but given the confusing state of the web you don't know where to begin? Or maybe you have finished the first 7 steps and are looking for some follow-up material, beyond the introductory? This post is the second installment of the 7 Steps to Mastering Machine Learning in Python series (since there are 2 parts, I guess it now qualifies as a series). If you have started with the original post, you should already be satisfactorily up to speed, skill-wise. If not, you may want to review that post first, which may take some time, depending on your current level of understanding; however, I assure you that doing so will be worth your effort.

Natural language processing (NLP) is one of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. Applications of NLP are everywhere because people communicate most everything in language: web search, advertisement, emails, customer service, language translation, radiology reports, etc. There are a large variety of underlying tasks and machine learning models behind NLP applications. Recently, deep learning approaches have obtained very high performance across many different NLP tasks.

Time series forecasting is different from other machine learning problems. The key difference is the fixed sequence of observations and the constraints and additional structure this provides. In this mega Ebook written in the friendly Machine Learning Mastery style that you're used to, finally cut through the math and specialized methods for time series forecasting. Using clear explanations, standard Python libraries and step-by-step tutorials you will discover how to load and prepare data, evaluate model skill, and implement forecasting models for time series data.

After a year of delays, the UK government has finally shared its plans for a more prosperous digital Britain. Unveiled by the Department for Culture, Media and Sport (DCMS), the Digital Strategy report outlines steps to plug skills gaps and deliver free training to people who need it, pushing forward important UK technology sectors like AI and allowing UK companies to remain competitive as they come to terms with life after Brexit. One thing the report makes clear is that the government can't do it alone. It's enlisting the help of some of the UK's biggest employers and companies, which will offer four million free digital skills training "opportunities" to people who need them. Many are existing initiatives or have been expanded, but Google will offer five hours of free digital skills as part of its Garage initiative (unveiled late last year) and launch a summer programme in coastal towns, BT's Barefoot Computing Project will give teachers free extra computer science resources and O2 will continue to deliver online safety tips via its partnership with the NSPCC.

Following on from the updated Industrial Strategy announced in January and plans to boost the growth and support of Britain's artificial intelligence (AI) sector, the UK government outlined a brand new digital strategy to combat growing skills gap fears across the country. A Digital Skills Partnership with government, businesses and charities will see the creation of more than four million free digital skills training opportunities in the UK and includes education commitments from the likes of Lloyds Banking Group, Barclays and Google. Speaking to Silicon, several tech industry professionals have offered their reactions to the news, providing an insight into what the industry is thinking. "The Government's Digital Strategy is an exciting and welcome prospect at a time when UK businesses are facing a significant threat from digital disruption. To gain a competitive advantage, businesses should consider how they can use new technologies like artificial intelligence, even at this relatively early stage," she said.

Join NewVoiceMedia and Call Centre Helper as they discuss the effects of Artificial Intelligence on the contact centre. Artificial Intelligence has been threatening to transform the Contact Centre. But, how much of this talk is hype? And, how much is likely to happen? Could this lead to the decline of contact centres as we know it?

The new iPhone is going to change its charging point again, making most accessories defunct in one swift move. Apple will drop the Lightning Port that's currently used to put charge into the phone and get data out of it, and instead use USB-C, according to a report from the Wall Street Journal. USB-C is quickly becoming the standard for the bottom of phones, and is used in rivals including Google's Pixel and even in Apple's own laptops. But the change would come just a few years after the Lightning Port was introduced with the iPhone 5. The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.

This repository contains the lecture slides and course description for the Deep Natural Language Processing course offered in Hilary Term 2017 at the University of Oxford. This is an advanced course on natural language processing. Automatically processing natural language inputs and producing language outputs is a key component of Artificial General Intelligence. The ambiguities and noise inherent in human communication render traditional symbolic AI techniques ineffective for representing and analysing language data. This is an applied course focussing on recent advances in analysing and generating speech and text using recurrent neural networks.

Neural networks are a rapidly developing paradigm for information processing based loosely on how neurons in the brain processes information. A neural network consists of multiple layers of node, where each node performs a unit of computation, and passes the result onto the next node. Multiple nodes can pass inputs to a single node, and vice-versa. The neural network also contains a set of weights, which can be refined over time as the network learns from sample data. The weights are used to describe and refine the connection strengths between nodes.