The Luddites feared machinery taking workers' jobs. Machines replacing jobs is only a negative prospect if there's no contingency plan. Otherwise, it should be pursued with all available powers. Have you heard the Word of Vyrd? John Henry Vyrd told us that our machines are awakening, and that the greatest wealth mankind has ever know will soon be afforded to us if only we were to take the next step.

Data Science in Python: Pandas Cheat Sheet -- This cheat sheet, along with explanations, was first published on DataCamp. Click on the picture to zoom in. To view other cheat sheets (Python, R, Machine Learning, Probability, Visualizations, Deel Learning, Data Science, and so on) click here. To read the article, click here. Will Trump Kill Statistician's Jobs? -- Today Trump met with leaders of pharmaceutical companies, to discuss "astronomical" drug prices and reduce regulations, so that drug companies can still make hefty profits while charging less for drugs.

Summary: In our recent article on "5 Types of Recommenders" we failed to mention Indicator-Based Recommenders. These have some unique features and ease of implementation that may be important in your selection of a recommender strategy. A few weeks ago in the midst of our series on recommenders we published an article "5 Types of Recommenders" in which we offered our view on the primary types of recommendation engines. We got a very nice comment from Ted Dunning suggesting that we'd missed an important one, Indicator-Based Recommenders. If you ever get a note from Ted Dunning you should pay attention.

This blog post will demonstrate how deep reinforcement learning (deep q learning) can be implemented and applied to play a CartPole game using Keras and Gym, in only 78 lines of code! I'll explain everything without requiring any prerequisite knowledge about reinforcement learning.

Imagine reliably asking Amazon Alexa, Amazon Echo Dot, Google Home, or a chatbot to run analytics queries against a big data platform. For example, "What were the top three revenue generating products last week?" Big Data at the tip of your tongue -- pun intended. The concept of conversing with a computer is very interesting and has been around for a while -- think Star Trek's "LCARS" and Hal from "A Space Odyssey". While we might be a long way off from those realities, recent advancements from Amazon, Google, Microsoft, IBM and other natural language and AI technologies have brought us closer.

For years, marketing was considered more art than science. But more recently, as marketing automation software has proliferated, marketers have had to blend the art of storytelling with the science of data. Then along comes artificial intelligence (AI) and machine learning, which promise to help marketers make sense of all that data. Some experts believe AI's impact on marketing will be hugely significant, that it could even change the nature of marketing entirely -- enabling brands to break through the noise and deliver a more personalized experience to customers. Not surprisingly, though, there are challenges ahead for organizations seeking to add AI to their marketing technology stack.

This is exactly what Qzone, one of China's largest online social network platforms owned by Tencent, YouTu Lab, an AI research lab under Tencent focusing on machine learning, and Vipshop Holdings Limited, China's leading online discount retailer for brands, have joined hands to accomplish. The three platforms have produced a new AI powered report that reveals the fashion preferences of China's "post-95" generation in terms of most popular colors, fabrics and patterns and inspired a new collection by famous Chinese designer Chi Zhang, named Designer of the year by Esquire China, to be launched at New York Fashion Week 2017. This is the first time that AI technology has been leveraged to identify fashion trends to guide the design of a major new collection for presentation at New York Fashion Week. By applying facial recognition technology to big data aggregated on Tencent's Qzone platform, YouTu Lab's algorithm identified the ages of "post-95" users accurately within three years. In order to analyze fashion preferences, the AI-powered technology was able to distinguish users' clothing from a multitude of varied backgrounds with 95% accuracy.

Man-versus-machine is a false dichotomy, pitting one against the other as two discrete, antagonistic entities. Machines will free up our time so we have fewer basic tasks to complete. We have already begun to see the start of this, with the creation of digital assistants like Siri and Alexa. Furthermore, machines will enable us to reimagine what our careers and working lives look like. It s worth remembering that the 40-hour workweek is a construct with a short history it was only in 1940 that it became codified through an amendment to the Fair Labor Standards Act.

We are dealing with plethora of data and information in the world today and expectation is to predict and forecast how we can gain competitive advantage based on the information that we have, to act in advance. We look forward to define and furnish various methods based on our gut feel, past historical data, simple mathematical averages, and many more to get an incredibly precise prediction. With advanced analytics and data science, we develop "always-on" forecasting models which enable our clients to take their decisions effectively. From intuition to traditional algorithms to machine learning, phases have been evolving over a period. Processes 2 & 3 are iterative and 6 & 7 are iterative. We try to look for answers to various questions in the process – is the goal / business objective descriptive or predictive in nature?

Natural language processing is an ever-growing interest area in the analytics application spectrum and is relevant to HR. In fact, it can revolutionize the quality of insights. In this article, we will explain you how. Did you know that text analysis has been the most prevalent productivity tool over the past 3 decades or so for HR? It is very familiar to HR. HR has been using Boolean keyword searches for identifying good resumes/ job applications for a long time already.