Machine learning is a technique of data analysis that automates analytical model building. It's a branch of artificial intelligence based on the concept that systems can learn from data, identify patterns, and make choices with least human intervention.Machine learning is required for tasks that are too complex for humans to code directly. Some tasks are so advanced that it's impractical - if not impossible - for humans to figure out all of the nuances and code for them explicitly. So, instead, we provide a large amount of information to a machine learning algorithm and let the algorithm work it out by exploring that data and checking out a model which will achieve what the programmers have set out to accomplish.How well the model performs is set by a cost function provided by the programmer and the task of the algorithm is to search out a model that minimizes the cost function.The most intuitive samples of machine learning are the various applications for reinforcement learning like self-driving cars and self-flying helicopters. In reinforcement learning, the algorithm is trained like a dog, receiving rewards once it gets things right and punishments when it gets it wrong. This technique was utilized by Stanford students nearly a decade ago to train a radio-controlled helicopter to do all varieties of incredible stunts, including flips and hovering upside down. You can’t explicitly code that kind of factor manually as there are too many variables to consider and too many nuances in the model.In this audiobook, you will learn about:Machine learningWays how machine learning worksAdvantages and disadvantages of machine learning as a career optionMachine learning's value for businessIntricacies of machine learning in data scienceMachine learning and fraud preventionMyths about machine learningArtificial intelligenceBig data analytics servicesApplications of big data analyticsAnd much more...
 by Luca R.
Joahnson, Nicole Skalij, Giada Riva ~ eBook PDF Kindle ePub Free){kind=link}
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