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Machine Learning

Introduction to Machine Learning

By February 1, 2020December 28th, 2020No Comments

You’ve probably heard of AI, or Artificial Intelligence.  I think a good way to define AI is a machine mimicking or displaying “human intelligence”.  Basically, as it is today, AI is a computer that can do a task (or tasks) as well or better than a human being.  Examples are Style Transfer filters (think the app on your phone that turns your pictures into Picasso-style abstracts) or some of the language translation in real time applications you see (think some of the new head phones which translate via your phone on the fly).  Another area of AI is Machine Learning (ML).  Let’s go through a brief introduction to Machine Learning.

What is Machine Learning (ML)?

So what does Artificial Intelligence have to do with Machine Learning (ML)?  Well, Machine Learning can be defined as an AI approach that learn through experience to recognize patterns in data.  Basically, a computer is taught patterns by examples using an algorithm, instead of recognizing the pattern by coding in rules that are followed in sequence.

The Machine Learning basics are:

  • Start with a training data set
  • Let the ML algorithm learn patterns
  • Provide new data to test ML algorithm and provide feedback

Using this method, an algorithm that starts not being very accurate can become very accurate at predictions.  From this basic process, ML can be implemented using more advanced techniques that allow recognition of patterns of patterns, helping machine learning algorithms more closely mimic a human brain (implementations are typically called Deep Learning).

How does it work?

As outlined prior, machine learning works by training an algorithm to do what you want.  So what does training mean?  The goal is to teach the algorithm the features or properties that are relevant to identifying the relevant information.  Choosing the appropriate features is absolutely vital.

For example, if you want to identify plants and animals, choosing the feature “living” is not particularly helpful.  However, “chlorophyll present” may be a very relevant feature.  That is just one feature.  Ideally, enough features are chosen to allow consistent identification of a plant or animal when presented with examples.  Note however, that one has to be careful not to provide too many features to train on, as this can lead the algorithm being unable to generalize when being used outside of training.

From this basic idea of using training data, ML can go into a variety of complex learning paradigms.  But at the core, these are all about the algorithm learning to understand inputs based on learned parameters.

How can it be used?

So with all this learning, what can ML do?  Four common outputs from an ML algorithm are:

  • Predictions of numerical values: Understanding the housing market in a given area and offering predictions for a home newly on sale
  • Classifying inputs: Understanding whether a given input is of class A (plant for example) or class B (animal for example)
  • Finding similar examples: Providing similar items for purchase a la Amazon.
  • Predicting next values in sequences: Helping with processing natural language or allowing a computer to speak

Where is it heading?

The most exciting part of Machine Learning is that we are just getting started.  ML as a concept has been around for many decades, but we’re seeing it evolve at an increasing rate.  This is due to improvements in processing power and access to the cloud.  The immediate pay off is an improvement in the consumer experience.  Everything from Google Photos to Amazon are incorporating ML more and more.  As we are becoming more connected, we are able to have ML assist us in all sorts of activities.  Better, more contextual shopping assistants.  Object identification in real time.   Real time language translation.  The possibilities are hard to quantify simply because it can be pervasive and impactful throughout our lives, individually and as a society!

This article is simply a short introduction to Machine Learning.  How is this relevant for DataGroomr?  We’re taking these concepts and applying them to cleaning your data, so that you don’t need to be an expert to have useful information at your fingertips!  Stay tuned for our next article on Machine Learning and Data!

Steven Pogrebivsky

Steve Pogrebivsky has founded multiple successful startups and is an expert in data and content management systems with over 25 years of experience. Previously, he co-founded and was the CEO of MetaVis Technologies, which built tools for Microsoft Office 365, Salesforce and other cloud-based information systems. MetaVis was acquired by Metalogix in 2015. Before MetaVis, Steve founded several other technology companies, including Stelex Corporation which provided compliance and technical solutions to FDA-regulated organizations.