A famous television series kicked off with its first episode titled "Winter is Coming." This phrase ended up carrying more weight than just the arrival of a season in the fictional series. In real life, some might say another monumental event is nearly upon us: Black Friday. References to Black Friday began as early as the 1950's in the United States. It wasn't until the 1980's that it came to refer to the retail shopping period following the Thanksgiving holiday, with one explanation suggesting the color indicated this being the time at which retail companies moved from operating at a loss (or "in the red") to profitability ("in the black").

This was the subject of a question asked on Quora: What are the top 10 data mining or machine learning algorithms? Some modern algorithms such as collaborative filtering, recommendation engine, segmentation, or attribution modeling, are missing from the lists below. Algorithms from graph theory (to find the shortest path in a graph, or to detect connected components), from operations research (the simplex, to optimize the supply chain), or from time series, are not listed either. And I could not find MCM (Markov Chain Monte Carlo) and related algorithms used to process hierarchical, spatio-temporal and other Bayesian models. My point of view is of course biased, but I would like to also add some algorithms developed or re-developed at the Data Science Central's research lab: These algorithms are described in the article What you wont learn in statistics classes.

Bottom line: Machine learning makes it possible to discover patterns in supply chain management data by relying on algorithms that quickly pinpoint the most influential factors to a supply networks' success, while constantly learning in the process. Discovering new patterns in supply chain data has the potential to revolutionize any business. Machine learning algorithms are finding these new patterns in supply chain data daily, without needing manual intervention or the definition of taxonomy to guide the analysis. The algorithms iteratively query data with many using constraint-based modeling to find the core set of factors with the greatest predictive accuracy. Key factors influencing inventory levels, supplier quality, demand forecasting, procure-to-pay, order-to-cash, production planning, transportation management and more are becoming known for the first time.

About the speaker: Kevin Chen is currently a self-taught ML practitioner concentrating on anomaly detection, time-series, streaming data, and (later) predictive analytics. He has a diverse technology and domain background spanning both enterprise and deep-tech. Previously, Kevin was a blockchain researcher/advocate involved with projects including IOTA and Fetch.AI. Before blockchain, he was in the financial sector as a back-end developer and data analyst at Citigroup and Aristeia Capital respectively. Kevin graduated from UVA with a Bachelors in CS. https://github.com/Kevin-Chen0

Automation has always made sense for managing aspects of complex PPC accounts. Our industry has lived through many iterations of excessive manual machinations, followed by equally perverse third-party automations devised to relieve us of toil. Eventually, either the platform (Google Ads) comes up with an elegant native solution, or all parties settle on a common-sense approach to what used to be superfluous busywork and gamesmanship. Take, for example, auction dynamics. Before Google AdWords ruled the roost, a PPC platform called Overture made advertisers' bids visible.

Recently, I got asked about how to explain p-values in simple terms to a layperson. I found that it is hard to do that. P-Values are always a headache to explain even to someone who knows about them let alone someone who doesn't understand statistics. I went to Wikipedia to find something and here is the definition: In statistical hypothesis testing, the p-value or probability value is, for a given statistical model, the probability that, when the null hypothesis is true, the statistical summary (such as the sample mean difference between two groups) would be equal to, or more extreme than, the actual observed results. And my first thought was that might be they have written it like this so that nobody could understand it.

Learn how to put your machine learning models into production. Deployment of machine learning models, or simply, putting models into production, means making your models available to your other business systems. By deploying models, other systems can send data to them and get their predictions, which are in turn populated back into the company systems. Through machine learning model deployment, you and your business can begin to take full advantage of the model you built. When we think about data science, we think about how to build machine learning models, we think about which algorithm will be more predictive, how to engineer our features and which variables to use to make the models more accurate.

In Southeast Asia, e-commerce is big business, with Singapore, Malaysia, the Philippines, Indonesia and Thailand generating US$14.8 billion in online sales throughout 2016. According to a 2019 study from Facebook and Bain & Company, ASEAN's digital consumers' spending will triple by 2025. Within the e-commerce sector, online retailers are already embracing artificial intelligence (AI) applications such as chatbots, to deliver a more personal experience for shoppers online. According to a 2018 article by Rene Millman titled'Adoption of AI booming in Southeast Asia,' the adoption rate of AI in the region grew to 14 percent in 2018. The article, citing an IDC report'Asia Pacific Enterprise Cognitive/AI Survey,' revealed that 37 percent of companies would put AI adoption plans in place in the next five years.

SAN JOSE, CALIFORNIA: The technology market has passed the tipping point with more than half of organisations today having adopted intelligent automation, as Robotic Process Automation (RPA) and Artificial Intelligence (AI) shift from emerging technologies to mainstream business solutions, according to a new analyst report by Futurum Research, commissioned by Automation Anywhere, an automation technology firm. The'Report for the State of RPA and Smart Automation' interviewed more than 1,000 business executives in North America and found that while 75.3% believe automation will make them more competitive, significant disparities exist between industries – with public sector and, surprisingly, technology companies lagging significantly when it comes to adoption. While more than half of businesses in North America have already implemented some type of automation solution, such as RPA and AI, the research uncovered notable differences between industries. For instance, nearly 9 out of 10 manufacturing organisations have already adopted some form of intelligent automation, compared to less than 3 in 10 public sector organisations. Despite these identified barriers to overcome, 9 in 10 organisations that have not yet implemented RPA and AI-based automation solutions report having sufficient internal technical competencies to do so, showing that technical implementation is no longer a significant hurdle for most organisations.

Researchers have developed a new artificial intelligence (AI) tool that can predict the life expectancy of heart failure patients, an advance that may allow clinicians to make more informed decisions while caring for heart patients. The researchers, including those from the University of California (UC) at San Diego in the US, said while predicting mortality is important in patients with heart failure, current strategies for evaluating this risk are only modestly successful and can be subjective. They developed a risk score that determined low- and high-risk of death by identifying eight variables collected from the majority of patients with heart failure. Using these inputs, the researchers said, the newly developed model could accurately predict life expectancy 88 per cent of the time, and performed substantially better than other popular published models. "This tool gives us insight, for example, on the probability that a given patient will die from heart failure in the next three months or a year," said Eric Adler, co-author of the study from UC San Diego.