Gear up to speed and have Data Science & Data Mining concepts and commands handy with these cheatsheets covering R, Python, Django, MySQL, SQL, Hadoop, Apache Spark and Machine learning algorithms.

Every passing year marks the introduction of a technological advancement which affords some new form of progressive automation. Members of the millennial generation, like myself, are no strangers to the integration of robotic technology in daily life. I remember delighting in the introduction of self-checkout machines at the grocery store as a young kid, begging my grandmother to use the machines. Unfortunately, my old-fashioned grandmother never let me use the self-checkout, as she did not trust the technology to get the job done. New robotic technology has always made older generations understandably uneasy, especially since pop culture tends not to portray robots in the best light (who remembers The Stepford Wives or I, Robot?) Often, robotic technology is suspected of being too generalized, and unable to tailor tasks to an individual's specific needs.

Chatbots, also known as smart bots, are flavour of the month in the tech community at the moment, but like many technologies, they are not an entirely new concept. The main reason they are currently making big waves is because they have become a lot smarter. As technology evolves, so does technology's ability to provide humans with more efficient and effective ways to solve problems. Chatbots provide a mechanism for big brands and publishers to reach their users in new and innovative ways, and as traditional communication transitions to message-based communication, we see an increasing need for these smart bots in chat applications such as Slack, Facebook, WeChat and Kik. Smart bots, artificially intelligent driven software, have the ability to learn over time, enabling them to provide more intelligent responses to the end user. I think we are quite some time away from these smart bots turning into Skynet with plans of destroying the human race, but we are at the beginning of a new era in technology.

The market for Service Delivery Automation (SDA) is a fast moving one, both in terms of adoption as well as advances in technology. Many organizations have already tried and adopted SDA technologies, such as Robotic Process Automation (RPA) and cognitive automations, based on machine learning software. These organizations are looking beyond Proofs of Concept (PoC) and trials to wider adoption of SDA across their organizations. An SDA Center of Excellence (CoE) enables organizations to develop their SDA capabilities and competencies in a controlled and centralized manner. It helps organization to stay abreast of developments in the fast-moving world of SDA.

Catastrophic climate change, nuclear war and natural disasters such as super volcanoes and asteroids could also pose a deadly risk to mankind, researchers said. It may sound like the stuff of sci-fi films, but experts said these apocalyptic threats are more likely than many realise. The report Global Catastrophic Risks, compiled by a team from Oxford University, the Global Challenges Foundation and the Global Priorities Project, ranks dangers that could wipe out 10% or more of the human population. It warns that while most generations never experience a catastrophe, they are far from fanciful, as the bouts of plague and the 1918 Spanish flu that wiped out millions illustrated. Sebastian Farquhar, director at the Global Priorities Project, told the Press Association: "There are some things that are on the horizon, things that probably won't happen in any one year but could happen, which could completely reshape our world and do so in a really devastating and disastrous way. "History teaches us that many of these things are more likely than we intuitively think."Many of these risks are changing and growing as technologies change and grow and reshape our world. But there are also things we can do about the risks."

It is known that data mining algorithms are not perfect and they can fail under certain conditions. K-Means is an example of that triviality but there is a good alternative, K-Medoids. In a previous post, "Machine Learning: A Brief Breakdown" we already mentioned that K-Means is the cluster analysis algorithm par excellence and it is one of the most important data mining and machine learning techniques; even psanchezcri used it to analyze the direction of a financial time series, in his post "Returns clustering with K-means algorithm". Nevertheless, it's difficult to find discussions about the algorithm's unexpected results in certain cases. The algorithm documentation is too broad in Internet, so the main objective of this post is to focus on showing a financial example of the problem.

Azure Media Services, Microsoft's collection of cloud-based tools for video workflows, is about to get a lot smarter. As the company announced at the annual NAB show in Las Vegas today, Media Services will now make use of some of the tools Microsoft developed for its machine learning services for video, as well. This means Media Services can now automatically select the most interesting snippets from a source video, for example, to give you a quick summary of what the full video looks like. In addition, Microsoft is building face detection into these tools and the company is including its ability to detect people's emotions (something the company's Cognitive Services already do for still images). Using this, you could easily see how people reacted to a speech at an event, for example.

OpenAI, the nonprofit artificial intelligence research company established last year with backing from several Silicon Valley figures, today announced its first product: a proving ground for algorithms for reinforcement learning, which involves training machines to do things based on trial and error. OpenAI is releasing tools you can run locally to test out algorithms in various "environments" -- including Atari games like Air Raid, Breakout, and Ms. Pacman -- and a Web service for sharing test results. The system automatically scores evaluations and also seeks to have results reviewed and reproduced by other people. "We originally built OpenAI Gym as a tool to accelerate our own RL research. We hope it will be just as useful for the broader community," OpenAI's Greg Brockman and John Schulman wrote in a blog post. To be sure, there are other online places for showing off algorithms, including Algorithmia.

Bayesian models are rooted in Bayesian statistics, and easily benefit from the vast literature in the field. In contrast, deep learning lacks a solid mathematical grounding. Instead, empirical developments in deep learning are often justified by metaphors, evading the unexplained principles at play. These two fields are perceived as fairly antipodal to each other in their respective communities. It is perhaps astonishing then that most modern deep learning models can be cast as performing approximate inference in a Bayesian setting. The implications of this statement are profound: we can use the rich Bayesian statistics literature with deep learning models, explain away many of the curiosities with these, combine results from deep learning into Bayesian modelling, and much more.

Deep Learning allows computational models composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection, and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large datasets by using the back-propagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about dramatic improvements in processing images, video, speech and audio, while recurrent nets have shone on sequential data such as text and speech. Representation learning is a set of methods that allows a machine to be fed with raw data and to automatically discover the representations needed for detection or classification.