Machine learning (ML) offers potential value to companies trying to leverage big data and helps them better understand subtle changes in behavior, preferences or customer satisfaction. Business leaders are beginning to appreciate that many things happening within their organizations and industries can’t be understood through a query. It isn’t the questions that you know, it’s the hidden patterns and anomalies buried in the data that can help or hurt you.
However, machine learning is not a simple process. As the algorithms ingest training data, it is then possible to produce more precise models based on that data. A machine learning model is the output generated when you train your machine learning algorithm with data.
Machine learning requires that the right set of data be applied to a learning process. An organization does not have to have big data to use machine-learning techniques; however, big data can help improve the accuracy of machine-learning models. With big data, it is now possible to virtualize data so it can be stored in the most efficient and cost effective manner, whether on-premises or in the cloud.
Machine learning techniques are required to improve the accuracy of predictive models. Depending on the nature of the business problem being addressed, there are different approaches based on the type and volume of the data.
Supervised learning typically begins with an established set of data and a certain understanding of how that data is classified. Supervised learning is intended to find patterns in data that can be applied to an analytics process. This data has labeled features that define the meaning of data.
Unsupervised learning is used when the problem requires a massive amount of unlabeled data. For example, social media applications, such as Twitter, Instagram and SnapChat, all have large amounts of unlabeled data.
Deep learning is a specific method of machine learning that incorporates neural networks in successive layers to learn from data in an iterative manner. Deep learning is especially useful when you’re trying to learn patterns from unstructured data. Deep learning complex neural networks are designed to emulate how the human brain works, so computers can be trained to deal with poorly defined abstractions and problems.
The advantage of machine learning is that it is possible to leverage algorithms and models to predict outcomes. The trick is to ensure that the data scientists doing the work are using the right algorithms, ingesting the most appropriate data (that is accurate and clean) and using the best performing models.
Interest in artificial intelligence (AI) and machine learning (ML) is soaring, but confusion and hype can mask the real benefits of these technologies. Organizations need to identify use cases that will produce value for them, especially in the areas of enhancing processes, detecting anomalies and enabling predictive analytics. Our team of experts at DataCrunch Lab can help you do that.