Machine Learning: What it is and Why it matters

Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.

Another recent development was that MIT researchers were working on object recognition through flexible machine learning.

Machine learning is starting to reshape how we live, and it’s time we understood what it is, and why it matters.

What is Machine Learning?

Machine learning is a core sub-area of artificial intelligence; it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves.

Machine learning is a method of data analysis that automates analytical model building.” In other words, it allows computers to find insightful information without being programmed where to look for a particular piece of information; instead, it does this by using algorithms that iterative learn from data.

Why Machine Learning?

To better understand the uses of machine learning, consider some instances where machine learning is applied: the self-driving Google car, cyber fraud detection, online recommendation engines-like friend suggestions on Facebook, Netflix showcasing the movies and shows you might like, and “more items to consider” and “get yourself a little something” on Amazon-are all examples of applied machine learning.

All these examples echo the vital role machine learning has begun to take in today’s data-rich world. Machines can aid in filtering useful pieces of information that help in major advancements, and we are already seeing how this technology is being implemented in a wide variety of industries.

Some Machine Learning Algorithms and Processes:

Other tools and processes that pair up with the best algorithms to aid in deriving the most value from big data include:

• Comprehensive data quality and management

• GUI for building models and process flows

• Interactive data exploration and visualization of model results

• Comparisons of different machine learning models to quickly identify the best one

• Automated ensemble model evaluation to identify the best performers

• Easy model deployment so you can get repeatable, reliable results quickly

• Integrated end-to-end platform for the automation of the data-to-decision process

Whether you realize it or not, machine learning is one of the most important technology trends-it underlies so many things we use today without even thinking about them. Speech recognition, Amazon and Netflix recommendations, fraud detection, and financial trading are just a few examples of machine learning commonly in use in today’s data-driven world.

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Contact: Claire Deschamps / Email: machinelearningandbigdata@gmail.com