Machine Leaning isn't a buzzword anymore. It's here to stay

Whether you realize it or not, ML is one of the biggest technology trends. It is increasingly touching more aspects of our everyday lives. Speech recognition, Amazon and Netflix recommendations, fraud detection, and financial trading are a few examples of Machine Learning commonly in use in today’s data-driven world.

While the concept of Machine Learning has been around for a long time, the ability to automate the application of complex mathematical calculations to Big Data has been gaining momentum over the last several years. The rapid evolution in Machine Learning has caused a subsequent rise in the use cases, demands—and, the sheer importance of ML in modern life.?This also means that there are many lucrative Machine Learning careers available.?

To better understand the uses of Machine Learning, consider some instances where it is applied - the self-driving Google car; cyber fraud detection; and, online recommendation engines from Facebook, Netflix, and Amazon. Machines can power all of these things by filtering useful pieces of information and piecing them together based on patterns to get the most accurate results.

Applications learn from previous computations and transactions and use “pattern recognition” to produce reliable and informed results. Typical results from Machine Learning applications we either see or don’t regularly include web search results, real-time ads, email spam filtering, network intrusion detection, and pattern and image recognition. All these are just the by-products of using Machine Learning to analyze massive volumes of data. Traditionally, data analysis was trial and error-based, an approach that becomes impossible when data sets are large and divergent. Machine Learning provides smart alternatives to analyzing vast volumes of data. By developing fast and efficient algorithms and data-driven models for real-time processing of data, Machine Learning can produce accurate results and analysis.

To define machine learning in simple terms, it is the science of getting machines to learn and act in a human way while also simultaneously learning from real-world interactions and sets of training data that we feed them. ML is not a new technology. The algorithms that drive today's pattern recognition and applications have been around for many years. However, it is only now that models are starting to interact with more complex data sets and learn from previous computations and predictions to produce reliable decisions and results thus creating profitable opportunities across your business.

Apple, Google, Facebook and Microsoft are just some of the tech giants that are leading the way with machine learning. From recommendation engines to facial recognition, machine learning is showing the right path for companies that are dealing in big data and big decisions. In a world where organizations need to stay one step ahead of the competition, this technology enables them to be more agile and conscious than ever before.

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