Top datasets to actualize machine learning and data training tutorial -Big Data Analytics News

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"A Breakthrough in machine learning would be worth ten Microsofts" – Bill Gates Yes, due to many obvious reasons, Bill Gates is right and we will prove it in this blog. Though the term, machine learning was tossed by Arthur Samuel in 1959 while working at the IBM, the actual serviceability of it started popping up after 2010. So, Dave Waters compares the advancement of machine learning with the baby – "A baby learns to crawl, walk and then run. We are in the crawling stage when it comes to applying machine learning." Recently, machine learning market has witnessed exceptional growth and it is estimated to reach $21 billion by 2024.


Machine learning may find fraud victims before the scammers do

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LAS VEGAS--It's become a common analogy for the use of predictive analysis in business technology: Wayne Gretzky became the best hockey player of his generation not because he skated to where the puck was, but because he skated to where the puck was going. Similarly, financial institutions are hoping to get ahead of the growing and seemingly insurmountable problem of payment card fraud not just by looking at who cyber-attackers are going after currently but who they are likely to defraud in the near future. At the Black Hat USA conference here last week, a pair of researchers -- one from Royal Bank of Canada and the other from a service provider that focuses on dark web intelligence -- presented on their joint effort to use machine learning, predictive analytics and transactional data together to get a handle on which cardholders might be the next victims of cyber-crime. With the vast stores of payment card, transactional, personal, demographic and historical fraud data to work from, it would seem that card-issuing banks already have a lot of information with which to work to help them determine the direction of fraudulent activity. The problem with having so much data is it is hard to find the right information at the right time.


Impact of Data Analytics in Stock Market Analysis -Big Data Analytics News

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Probably, most people got their first taste of Data Analytics is the Hollywood blockbuster, 'Moneyball', wherein the coach Brad Pitt selected players for his baseball team by using data analytics to identify undervalued players. Oakland Athletics lost its star players after losing to the New York Yankees; the team needed to rebuild its roster with a limited budget. Brad Pitt was recruited as the General Manager of Oakland Athletics to rebuild the team. Brad Pitt used data analytics to recruit players and selected players on the basis of their on-base percentage. Using data analytics, Brad Pitt formed a completely new team by signing players at extremely low contracts prices, and this team went to win the Western American League.


Big data couldn't get the World Cup results right

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Goldman Sachs' statistical model for the World Cup sounded impressive: The investment bank mined data about the teams and individual players, used artificial intelligence to predict the factors that might affect game scores and simulated 1 million possible evolutions of the tournament. The model was updated as the games unfolded, and it was wrong again and again. It certainly didn't predict the final between France and Croatia. The failure to accurately predict the outcome of soccer games is a good opportunity to laugh at the hubris of elite bankers, who use similar complex models for investment decisions. Tom Pair, founder of the Upper Left Opportunities Fund, a hedge fund, tweeted recently: "Of course, past data don't always predict the future; Goldman Sachs never tells clients to make decisions solely on the basis of its models' findings.


Big data couldn't get the World Cup results right

#artificialintelligence

Goldman Sachs' statistical model for the World Cup sounded impressive: The investment bank mined data about the teams and individual players, used artificial intelligence to predict the factors that might affect game scores and simulated 1 million possible evolutions of the tournament. The model was updated as the games unfolded, and it was wrong again and again. It certainly didn't predict the final between France and Croatia. The failure to accurately predict the outcome of soccer games is a good opportunity to laugh at the hubris of elite bankers, who use similar complex models for investment decisions. Tom Pair, founder of the Upper Left Opportunities Fund, a hedge fund, tweeted recently: "Of course, past data don't always predict the future; Goldman Sachs never tells clients to make decisions solely on the basis of its models' findings.