Machine Learning (ML) is everywhere today! If you are using any of the apps or websites for Amazon, Google, Uber, Netflix, Waze, Facebook in your daily busy life, they are all internally supported by machine learning algorithms. Most of the top companies in every sector from Aviation to Oil and Gas, Banking to Retail, Ecommerce to Transportation have products and projects using machine learning tools and technologies. Machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it.
Wikipedia defines machine learning as “a subfield of computer science (CS) and artificial intelligence (AI) that deals with the construction and study of systems that can learn from data, rather than follow only explicitly programmed instructions.” As per IBM – Machine learning is the science of how computers make sense of data using algorithms and analytic models. As per SAS – 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.
As per Stanford, Machine learning is the science of getting computers to act without being explicitly programmed. It enables cognitive systems to learn, reason and engage with us in a more natural and personalized way. The most powerful form of machine learning being used today, called “deep learning”, builds a complex mathematical structure called a neural network based on vast quantities of data. Designed to be analogous to how a human brain works, neural networks themselves were first described in the 1930s. But it’s only in the last three or four years that computers have become powerful enough to use them effectively. If deep learning will be as big as the internet, it’s time for everyone to start looking closely at it.
Machine Learning (ML) is a form of AI that facilitates a computer’s ability to learn and essentially teach itself to evolve as it becomes exposed to new and ever-changing data. The main components of ML software are statistical analysis and predictive analysis. These algorithms can spot patterns and find hidden insights based on observed data from previous computations without being programmed on where to look. They learn from every experience and interaction.
Some of the ML techniques are Linear Regression, K-means, Decision Trees, Random Forest, PCA, SVM and finally Artificial Neural Networks (ANN). Artificial Neural Networks is where the field of Deep Learning had its genesis from.
Internet of Things (IOT) will produce billions and billions of data points from billions of connected devices by 2020. Machine learning can help companies take the billions of data points they have and boil them down to what’s really meaningful. The future realization of IoT’s promise is dependent on machine learning to find the patterns, correlations and anomalies that have the potential of enabling improvements in almost every facet of our daily lives.
As per HBR, machine learning has tremendous potential to transform companies. Executives who want to get the most out of their companies’ data should understand what it is, what it can do, and what to watch out for when using it. It’s time to let the machines point out where the opportunities truly are! In our next blog, we will take a journey into the various use cases and implementation of ML in different industry.
Share with me your use cases of ML that you want to get included in my next blog!