In our last blog, we discussed the basics of data mining and its importance to your business. With the science of data becoming a staple in our daily lives and business endeavors, many confusing terms can be thrown around without much explanation of their meaning. Although the term “machine learning” is often used when talking about data mining, it can be difficult to understand their difference and its specific meaning.

What is Machine Learning?

Simply put, machine learning is a way of educating a computer to perform a more complex task than an analyst or data scientist may not be able to accomplish. The term itself can be rather complicated and vast, but with its growing popularity, machine learning can be used for an abundance of tasks.

You may be wondering—if it’s a popular way of performing and generating advanced algorithms, where can you see it being utilized? The answer is, almost everywhere! Whether you’re searching through Netflix’s “recommended for you” section or being pleasantly surprised by the minimal amount of time you have to wait for your Lyft or Uber ride. All this is thanks to machine learning. There are certain tasks that can be nearly impossible (or rather time-consuming) for an individual to review or create manually. Volumes of data are immense, and it’s far simpler to have Artificial Intelligence quickly learn from large pools of available data instead.

What’s is Its Purpose?

Although it has multiple purposes, its main use is to generate various algorithms that can help a computer “learn” from those volumes of data. Instead of having a human try to sift through hefty data to try and better solve a problem, a good algorithm can solve the same problem in far fewer steps and with minimal effort. How does that work exactly?

  • Machine Learning is the study of algorithms, statistics, and models.
  • Computer systems use the three above to perform specific tasks.
  • Without a significant amount of human instruction, the computer can perform these tasks by relying on the given data.
  • By running data and past experience through their algorithm, they can learn distinct patterns, which in turn, strengthens the algorithm.

For example, if an algorithm’s purpose is to filter out a spam message, by “feeding” it a few such messages for it to recognize, it can identify the common factors and learn to filter them out on its own.

What’s the Overall Goal?

The basic goal of machine learning is for an algorithm to work independently and accurately. Through the study of machine learning, complex functioning algorithms can be generated and used to further efficiency in various programs. This can be done through:

  • Supervised Learning
  • Unsupervised Learning

As data mining and machine learning are often seen as two separate entities under the same umbrella—both used ultimately to make predictions based solely on generous amounts of data—it can be increasingly useful to use machine learning as a technique to increase the efficiency of data mining.