Many individuals picture a robot or a terminator when they catch wind of Machine Learning (ML) or Artificial Intelligence (AI). However, they aren't something out of motion pictures, it is anything but a cutting edge dream. We are living in a situation with numerous cutting edge applications developed using machine learning, despite that there are certain challenges an ML practitioner might face while developing an application from zero to bringing them to production. Data plays a key role in any use case. For beginners to experiment with machine learning, they can easily find data from Kaggle, UCI ML Repository, etc.

Many individuals picture a robot or a terminator when they catch wind of Machine Learning (ML) or Artificial Intelligence (AI). However, they aren't something out of motion pictures, it is anything but a cutting edge dream. We are living in a situation with numerous cutting edge applications developed using machine learning, despite that there are certain challenges an ML practitioner might face while developing an application from zero to bringing them to production. Data plays a key role in any use case. For beginners to experiment with machine learning, they can easily find data from Kaggle, UCI ML Repository etc.

Five years ago, a team of researchers from Google announced a remarkable achievement in one of the world's top scientific journals, Nature. Without needing the results of a single medical check-up, they were nevertheless able to track the spread of influenza across the US. What's more, they could do it more quickly than the Centers for Disease Control and Prevention (CDC). Google's tracking had only a day's delay, compared with the week or more it took for the CDC to assemble a picture based on reports from doctors' surgeries. Google was faster because it was tracking the outbreak by finding a correlation between what people searched for online and whether they had flu symptoms. Not only was "Google Flu Trends" quick, accurate and cheap, it was theory-free. Google's engineers didn't bother to develop a hypothesis about what search terms – "flu symptoms" or "pharmacies near me" – might be correlated with the spread of the disease itself.

Wisconsin Gov. Scott Walker may be surging as an early favorite for the Republican presidential nomination -- but the numbers show the person leading among potential Iowa caucus-goers often doesn't win. In the Democratic field, Hillary Clinton is the most dominant position ever for a non-incumbent, with odds of clinching the nomination as high as 91 percent. Over at the 538 site launched by statistician Nate Silver, the race is already on to handicap the 2016 presidential contest, a reminder that the season of campaigning, polls, predictions and surveys is descending upon us. Which makes it a perfect time to think about statistics. Even this soon in the game, we'll start to see polling from all sides of the political spectrum, making us ponder which results are valid and which might be biased.