This article is part of "AI education", a series of posts that review and explore educational content on data science and machine learning. With so many books on Python machine learning, making a choice is becoming increasingly difficult. You're investing both your time and money to learn something that can open new career paths for you. It would a disappointment to get halfway through a 700-page machine learning book to realize it's not for you. Having read and reviewed many books on Python machine learning, I can attest that every volume is unique in its own right.

In this age of big data, companies worldwide need to sift through the avalanche of information at their disposal to enhance their products, services and overall profitability. Many companies rely on programming languages like Python and the advancements made in artificial intelligence (AI) and data science to get that job done. Right now, you can save hundreds on The Ultimate Python & Artificial Intelligence Certification Bundle, featuring nine in-depth courses and 38 hours of video content that catches you up to speed on everything Python, AI and data science.

Of all the areas of life where artificial intelligence will have an impact, the biggest might well be education. This is because learning is so important, and also because current provision often leaves a lot to be desired. This is not generally the fault of teachers. They are the active ingredient in today's education system, but they are expensive, and not scalable. In most countries they are under-valued, and burdened by absurd paperwork.

Sir David Hand gave a brilliant plenary talk and set the stage for a great panel discussion by cautioning us to remember that thinking is required and to be aware of all the dark data out there -- the data that we don't see, but that we need to take into account. Dark Data: Why What You Don't Know Matters is his latest book (see a blog post about it; if you haven't read it, you can get a sample excerpt). The panelists included Cameron Willden, statistician at W.L. Gore, who supports engineers and scientists across many different product lines; Sam Gardner, founder of Wildstats Consulting, with more than 30 years of experience doing statistical problem solving for government and industry; and JMP's Jason Wiggins, a 20-year US Synthetic veteran with expertise in process optimization, measurement systems analysis and predictive modeling/data mining. We ran out of time before we could answer all the questions from the livestream audience, but our panelists have kindly agreed to provide answers to many of them, further sharing the wisdom from their collective experiences. The questions are grouped by topic -- there were so many, we are doing two posts.

Nearly 30 local governments are planning to or are interested in introducing an artificial intelligence system designed to assess the seriousness of school bullying cases in hopes of better responding to them, a source close to the matter said Thursday. Otsu Municipal Government, which came under fire for the way it handled a high-profile bullying case in 2011, has teamed up with information technology services provider Hitachi Systems Ltd., to develop the AI system, which predicts how a case of bullying has the potential to become serious based on an analysis of past cases. School bullying has long been a concern in Japan, with education ministry data showing that elementary, junior and high schools as well as special-needs schools nationwide reported 612,496 cases in the year through March, up 68,563 from a year earlier. When a new case of bullying is reported, information on the incident, such as time, place and perpetrator, is fed into the system, which then searches its database to come up with an estimate of how serious the case is, expressed as a percentage. In all, about 50 pieces of data are used for analysis.

The 61st International Mathematical Olympiad, or IMO, begins today. It may go down in history for at least two reasons: Due to the COVID-19 pandemic it's the first time the event has been held remotely, and it may also be the last time that artificial intelligence doesn't compete. Indeed, researchers view the IMO as the ideal proving ground for machines designed to think like humans. If an AI system can excel here, it will have matched an important dimension of human cognition. "The IMO, to me, represents the hardest class of problems that smart people can be taught to solve somewhat reliably," said Daniel Selsam of Microsoft Research.

Visualizing malware programs as images and feeding them into artificial intelligence helps to find patterns in computer malware that might have taken years for humans to identify, according to an article published by two University of North Georgia (UNG) computer science faculty members and a recent graduate. Dr. Sara Sartoli, an assistant professor, and Dr. Yong Wei, a professor, published the article with recent UNG graduate Shane Hampton. They will present the paper virtually at the IEEE International Conference on Machine Learning and Applications, set for Dec. 14-17. "Recent industry reports show organizations are increasing their investments in artificial-intelligence-based solutions to fight cyber-attacks," Sartoli said. "And they agree that one of the biggest benefits is to process the large amount of data points quickly."

In early 2018, officials at University College London were shocked to learn that meetings organized by "race scientists" and neo-Nazis, called the London Conference on Intelligence, had been held at the college the previous four years. The existence of the conference was surprising, but the choice of location was not. UCL was an epicenter of the early 20th-century eugenics movement--a precursor to Nazi "racial hygiene" programs--due to its ties to Francis Galton, the father of eugenics, and his intellectual descendants and fellow eugenicists Karl Pearson and Ronald Fisher. In response to protests over the conference, UCL announced this June that it had stripped Galton's and Pearson's names from its buildings and classrooms. After similar outcries about eugenics, the Committee of Presidents of Statistical Societies renamed its annual Fisher Lecture, and the Society for the Study of Evolution did the same for its Fisher Prize. In science, these are the equivalents of toppling a Confederate statue and hurling it into the sea. Unlike tearing down monuments to white supremacy in the American South, purging statistics of the ghosts of its eugenicist past is not a straightforward proposition. In this version, it's as if Stonewall Jackson developed quantum physics. What we now understand as statistics comes largely from the work of Galton, Pearson, and Fisher, whose names appear in bread-and-butter terms like "Pearson correlation coefficient" and "Fisher information." In particular, the beleaguered concept of "statistical significance," for decades the measure of whether empirical research is publication-worthy, can be traced directly to the trio. Ideally, statisticians would like to divorce these tools from the lives and times of the people who created them. It would be convenient if statistics existed outside of history, but that's not the case.

Artificial intelligence (AI) is everywhere these days, making inanimate objects increasingly smart. It's designed by humans for humans, to enrich and facilitate our everyday lives. As a matter of fact, AI is now the brain behind your smartphone, car, music streaming service, banking app, freezer, and travel agency. If the main keyword for AI is "smart", then how come we don't talk more about this burgeoning technology in the context of knowledge, learning, and education? AI has everything we need to revolutionize the industry, enrich and facilitate the learning experience of students and adult learners, and boost the knowledge retention rates across the board.

Open Deep Learning and Reinforcement Learning lectures from top Universities like Stanford University, MIT, UC Berkeley. This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. Students will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow. Course concludes with a project proposal competition with feedback from staff and panel of industry sponsors. Experience in Python is helpful but not necessary.