Python is one of the fastest-growing platforms for applied machine learning. In this mini-course, you will discover how you can get started, build accurate models and confidently complete predictive modeling machine learning projects using Python in 14 days. This is a big and important post. You might want to bookmark it. Python Machine Learning Mini-Course Photo by Dave Young, some rights reserved.

A.I. Bots are computer programmes designed to simulate a human conversation. They're used to help with things like making a dinner reservation, adding an appointment to your calendar or suggesting the fastest route home. ChatBots are an increasingly common form of bot that live in messenging apps, iMessage or like WhatsApp. They've been making big waves in the tech community this year, and with Facebook's recent announcement that ChatBots are coming to Messenger, there's no stopping them. The idea of intelligent bots has been around a very long time. Exactly 70 years ago one of the World's first computers was built by Alan Turing, the British Mathematician and Computer Scientist.

Before exploring the skills we'll need in the next 10–20 years, it's helpful to look at a few jobs that didn't even exist just a decade ago in 2006. We didn't have'Social Media Managers' as the major platforms -- Facebook, Twitter, Instagram -- either didn't exist or hadn't broken free of a few college campuses. We didn't have'Cloud Computing Specialists'. Indeed, the term'Cloud Computing' is thought to be taken from a 2006 conference featuring Google CEO Eric Schmidt. And we didn't have YouTube content creators.

China's search engine giant Baidu jumped onto the bandwagon of artificial intelligence as it showcased a number of achievements and industry solutions such as voice recognition, graphics recognition and consumer profiling at the Baidu World Conference in Beijing today. "Most of these solutions would be available for our industry partners so that ordinary people can access these cutting edge technologies," Robin Li, chairman and CEO of Baidu told the keynote speech. Baidu Chief Scientist Andrew Ng announced the opening up of two open platforms, Baidu's deep learning research tools platform and its artificial intelligence portal to support a wide range of industries to enhance efficiency. It is also offering the software up to the global community of AI researchers, an approach adopted by many tech firms to attract top talents as well as to allow the company to shape the development of the research field. Graphics processing unit developer Nvidia's co-founder and CEO Jen Hsun Huang also announced at the conference it is to partner with Baidu to build a comprehensive autonomous driving platform that allows Baidu to get a self-driving vehicle on the road.

We have covered here at Equities many times the rising risk of cyber attacks and how new companies are coming out with unique products to prevent security hacks. One of the new companies we are following is SecBI (Security Business Intelligence). This Israel-based company was founded in 2014 under the leadership from experts at RSA. The company is headquartered in Be'er-Sheba as a part of the JCP Cyberlabs. SecBI's cyber-detection platform that combines advanced intelligent thinking-machine technology, cyber-security expertise and user feedback into a superior threat detector.

As the release of Spark 2.0 finally came, the machine learning library of Spark has been changed from the mllib to ml. One of the biggest change in the new ml library is the introduction of so-called machine learning pipeline. It provides a high level abstraction of the machine learning flow and greatly simplified the creation of machine learning process. In this tutorial, we will walk through the steps on how to create a machine learning pipeline and also explain what is under the hood in the pipeline. In this tutorial, we will demonstrate the process to create a pipeline in Spark to predict airline flight delay.

We have a great last minute deal on the Complete Machine Learning Bundle in our deals store today, you can save 94% off the normal price. The Complete Machine Learning Bundle normally costs 780 and you can get it for just 39.99 in the Geeky Gadgets Deals store. You can find out more details about this great deal on the Complete Machine Learning Bundle over at our deals store at the link below.

Artificial Intelligence (AI), long the subject of science fiction, is now becoming more and more widespread and is seen as an increasingly important computer science across multiple industries. In Financial Services in particular, Machine Learning and Natural Language Processing is increasingly used today to make sense of big, complex data in a wide range of areas. One such area is regulatory compliance. The use of AI – particularly Natural Language Understanding (NLU), a subset of Natural Language Processing – can help firms to realise a number of benefits, including improving the speed and efficiency with which they achieve compliance, and making that compliance much more robust. As we've seen over just the last couple of years with the introduction of MiFID I & II, UCITS, AIFMD and the like, there is a constant stream of documents being issued by regulators, which can each run to hundreds, or even thousands, of pages.

Disruption in the workforce is hardly a new phenomenon. Mechanisation of manufacturing, mass production and the advent of the internet and computers have all changed the way that work is done. Earlier waves of industrialisation have primarily affected low-skilled manual labour and past improvements in technology have typically made jobs at the lower end of the skills spectrum obsolete – for example, flight navigators but not pilots; typists but not data analysts. There is wide acceptance that this has led to productivity improvements and higher economic growth – new jobs were generated that led to improvements in standards of living. The benefits have overwhelmingly outweighed the costs and there has never been a better time to be a human being. The current wave, characterised by automation becoming smarter, machine-to-machine communication, artificial intelligence and continued technological improvements – and otherwise described at the fourth industrial revolution – still brings uncertainty and threatens a broader range of occupations and skill levels.

You may have seen our previous posts on machine learning -- specifically, how to let your code learn from text and working with stop words, stemming, and spam. So today, we're going to build our machine learning-based spam filter, using the tools we walked through in those posts: tokenizer, stemmer, and naive bayes classifier. We are going to work with bluebird promise library here, so if you are not used to promises, please take a look at the bluebird API reference. Before we begin, it's important to have good training data. You can download some here -- we are interested in two.