Tools for Making Machine Learning Easier and SmootherLearn new methods for using deep learning to gain actionable insights from rich, complex data.

During the past decade, enterprises have begun using machine learning (ML) to collect and analyze large amounts of data to obtain a competitive advantage. Now some are looking to go even deeper – using a subset of machine learning techniques called deep learning (DL), they are seeking to delve into the more esoteric properties hidden in the data. The goal is to create predictive applications for such areas as fraud detection, demand forecasting, click prediction, and other data-intensive analyses.

The computer vision, speech recognition, natural language processing, and audio recognition applications being developed using DL techniques need large amounts of computational power to process large amounts of data. To get the insights enterprises are looking for with DL, the underlying IT infrastructure needs to be deployed and managed as enterprise grade. New solutions are being developed that make it faster and easier for organizations to gain actionable insights from rich, complex data.

There are three types of ML: supervised machine learning, unsupervised machine learning, and reinforcement learning.

With supervised machine learning, the program is “trained” on a predefined set of criteria. For example, one may feed the program information on prior home sales prices based on neighborhood, number of bedrooms, and total square footage, and then ask it to predict what the sales price would be for new sales. While a good real estate agent knows how to price houses based on area, neighborhood, and similar factors, programming a computer to do that using standard techniques would be extremely cumbersome. Another example would be showing the computer predefined sets of data (like a collection of images of cats and dogs) to train it to properly identify other similar images.

Unsupervised machine learning means the program is given a large amount of data and must find nonlinear relationships within the data provided. An example of this might be looking at real estate data and determining which factors lead to higher prices in certain parts of the city. One major manufacturer is using this type of unsupervised machine learning to predict future demand for a variety of parts. In this way, parts would be available for installation before equipment has to be grounded. A human expert may know roughly what factors affect the demand for parts, but machine learning provides the additional data needed to automate that decision.

Reinforcement learning is when a computer program interacts with a dynamic environment in which it must perform a certain task. Examples include interacting dynamically with social media to collect data on the public sentiment on an issue. The computer can get information from data and predict future contributions in real time.

These machine learning methods work only if the problem is solvable with the available data. For example, one cannot use machine learning techniques to estimate an air fare based on whether the customer has a dog. If the data would not help a human expert solve the problem, it will not help the machine either.

 

This article originally appeared in data-informed.com.  To read the full article, click here.