What is Machine Learning in Data Science?

Imagine a world where machines can automatically learn from their mistakes. It might seem like science fiction, but humanity has already developed machine learning capability. Machine learning is a form of automation that gives an AI agent the ability to learn based on a preferred outcome. The best way to think of machine learning is as an artificial intelligence agent that gets closer to its goal after each try. Like humans, these machine learning agents start as incompetent at their job but eventually learn and get better through trial and error. They can iteratively get better at a task much faster than human beings, making them more efficient at learning new lessons. Their role in data science is crucial since they can spot patterns and relay information much more rapidly than a human being can.

Machine learning in data science can be simplified as teaching an AI agent how to spot essential patterns in data and relate those patterns to a human operator. In data science, the processing of vast lakes of data is simply impossible for a human being to achieve. With live streams of data coming in from Internet-of-Things (IoT) devices along with many other inputs, trying to resolve all of that data would be impossible. AI agents trained in machine learning are better equipped to handle these data streams, allowing for real-time insights on collected data and a better ability to deal with the vast volumes of data received each second.

What is Machine Learning and How Is It Used?

According to SAS, machine learning is a data analysis method that relies on analytical model-building and comparison. In essence, machine learning is predicated on the idea of pattern recognition. The ability to spot patterns is nothing new; humanity has evolved because of its ability to recognize patterns and associate them with good or bad outcomes. In the case of a machine learning agent, the patterns it tries to spot are based on massive volumes of data that would swamp a regular user. Machine learning came about to automate teaching an AI agent what it should and shouldn’t do. AI agents are ideal for iterative processes, and researchers quickly figured out that machine learning could use this iterative approach.

Machine learning has ingrained itself into our society so intimately that some find it exciting and others find it scary. Facial recognition, for example, is a technology that depends on machine learning and artificial intelligence to function. Another excellent example of systems that learn is social media news feeds. By tapping into the likes and dislikes of a user, the algorithm can “learn” what its users like and dislike. Based on this information, it can better provide a feed that users will more likely be interested in seeing, fulfilling its purpose.

When Is Machine Learning a Good Idea?

Ideally, we can devise an algorithm to perform a task once we have data. An algorithm is simply a collection of instructions that give us a result. In many cases, it’s easy to create an algorithm that would provide us with results that make sense once we have a relationship between the different data points. But what if we don’t know how those data points are related? What if those data points change with each new influx of data? How do we create an algorithm that dynamically adapts to the evolving data? Machine learning seeks to solve this problem by offering a dynamic way to adjust to new information. When new data is added to a machine-learning algorithm’s data set, it recalculates the “formula” it’s using to get its results and gives something that better incorporates this new data.

Types of Machine Learning

Machine learning can happen in three distinct ways:

  • Supervised Learning: Researchers use supervised learning to teach an AI agent to recognize patterns based on a data set. Data goes into the model, adjusting its output accordingly until getting the desired results. Typical implementations of this model include spam filtering in your inbox and neural network generation. It works best with clearly labeled and structured data.
  • Unsupervised Learning: When a data collection is unstructured and doesn’t have any method to its location or sorting, unsupervised learning can help categorize it in a way that makes sense to the AI agent. Unsupervised learning is exciting because it doesn’t need human intervention and the algorithm usually spots patterns that humans might have missed. It’s typically used in determining the best cross-selling strategies for businesses and image recognition.
  • Semi-supervised Learning: This methodology combines the best of both worlds. The algorithm is trained on a structured data set at the start and then fed a series of unstructured data. Based on what it learned from the structured sets, it can categorize and spot patterns in unstructured data. If an organization doesn’t have enough structured data, this can be a godsend to help them get their machine learning algorithms working correctly.

Where is Machine Learning Most Commonly Spotted?

Machine learning appears in several different industries, including:

  • Finance: From trying to determine the price of stocks and bonds to detecting fraud, the financial industry has adopted machine learning in a big way.
  • Legal: Court cases have a phase known as discovery, which requires deep research into issues that have already happened. Machine learning makes this process much simpler.
  • Agriculture: AI agents can sport problems in the growth of crops and inform farmers reliably what they should improve. Elements like plant diseases and crop rotation could be handled at the AI level.
  • Healthcare: There have been advances in healthcare with machine learning agents being able to give solid diagnoses to patients. Doctors still supervise the process and advise the algorithm.
  • Transportation: Logistical concerns such as traffic flow can be taken into account via an AI agent and a solution developed that finds the most efficient transport route.

The Importance of Machine Learning

Why do companies involve in machine learning? ML is actually more cost-efficient for running a business. By investing in ML training, a company can manage its need for qualified personnel. AI agents tend to work more efficiently than humans, making them a better fit for some positions. The massive volumes of data that come into a business daily are impossible for a human to process, but an AI agent has no problem with this task. Finally, AI agents are better suited to adapt to changes in data. When changes in the data set happen, they are reflected in how the algorithm handles incoming data. There’s no need for manual adjustment, as the algorithm takes this automatically.

Machine learning is already a massive field full of potential. Newcomers need to have the proper training and experience to make a difference in this industry. Legends of Tech has prepared an intensive series of courses that best outfit someone who wants to be a part of this developing field to truly make a difference. If you’re interested in the area of machine learning or other AI-based courses, check us out today and sign up!

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