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5 Common ML Challenges Data Scientists Face

1) Communication: Unclear questions and outcome metrics A fundamental challenge facing data scientists has nothing to do with ensemble algorithms, optimization methods, or computing power. Communication – prior to any analysis or data engineering – is crucial to solving an ML problem quickly and painlessly. There are many, many questions ML can solve: this is

How business can be benefit with machine learning

If you’ve ever paid someone with PayPal, watched a movie recommended by Netflix or misspelled a word in a search engine and received the correct results anyway—you’ve benefited from machine learning. For years, forward thinking businesses have been exploring new ways to harness machine learning to improve the ways they serve their customers. Should your

Machine learning use cases for security

At its simplest level, machine learning is defined as “the ability (for computers) to learn without being explicitly programmed.” Using mathematical techniques across huge datasets, machine learning algorithms essentially build models of behaviors and use those models as a basis for making future predictions based on newly input data. It is Netflix offering up new

Typical explanation between AI and ML

Artificial Intelligence (AI) and Machine Learning (ML) are two very hot buzzwords right now, and often seem to be used interchangeably. They are not quite the same thing, but the perception that they are can sometimes lead to some confusion. So I thought it would be worth writing a piece to explain the difference. Both

Machine learning what it is & why it matters

Machine learning is a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look. The iterative aspect of machine learning is important because as models are exposed to new data, they are able to

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