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27 June 2023 - 5 minutes

The Future of Machine Learning

Looking to discover what’s in the future of machine learning? We’ve got you covered.

Juliette Carreiro - Tech Writer

Data Science & Machine Learning

We’re sure you’re well aware of the recent rise of machine learning and are probably wondering what we can expect in the future. We’re not fortune tellers or particularly skilled at predicting the future, but thanks to the recent rise of machine learning technologies across almost every field, we have a pretty good idea of what to expect. 

But before we dive into the future of machine learning, let’s start with the past and present. What is it? Why has it recently become such a phenomenon? What does the past tell us about the future?

What is Machine Learning?

Part of artificial intelligence and computer science, machine learning uses data and algorithms to imitate the way humans learn and act, slowly improving over time. Within machine learning as a whole, we can separate it into four categories: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.

  • Supervised learning: machines receive labeled data to predict future outcomes and behavior; analysts review these results after to ensure that everything is correct. 

    • Examples include spam detection, speech recognition, medical diagnosis, and fraud detection. 

  • Unsupervised learning: machines receive unlabeled data sets without any output variable, meaning they’re able to detect hidden patterns or categorized ungrouped data.

    • Examples include forecasting and predictive modeling, recommendation services, and anomaly detection.

  • Semi-supervised learning: using both labeled and unlabeled data, machines can use both data sets to draw conclusions and sort information, improving accuracy and performance.

    • Examples include medical image analysis, text classification, and fraud detection.

  • Reinforcement learning: the most advanced method of machine learning is reinforcement learning, where the machine learns how to make adjustments based on positive and negative feedback and improves over time.

    • Examples include personalized treatment plan development, autonomous vehicles, and games.

Why is machine learning important?

One of technology’s most important selling points is that it makes life easier for humans and that’s exactly what machine learning does. The ability to interpret large amounts of data and make logical decisions with minimal human intervention not only frees up lots of time that humans would have otherwise spent with that data, but also allows for incredibly large amounts of data to be processed quickly–a task that’s nearly impossible for humans.

Another crucial factor is the speed at which both technology and customer preferences are evolving. In order to maximize profits and efficiency, companies need to keep up with what people want and this requires making sense of tons of data. When properly implemented, machine learning can cut costs, improve quality of life for both employees and customers, and eliminate risks.

The origins of machine learning

Machine learning first came to be in the 1940s (if you’re interested in the origins of artificial intelligence, which encompasses the beginning of machine learning, check out our post below!) but didn’t really come to fruition until the 1990s, when the world’s first spam filter was released. And while this invention was certainly a relief to those of us who were overwhelmed with high numbers of spam emails, it was an indication of something even bigger: a massive advancement in computer science and the birth of an entirely new field. 

Today, machine learning has taken over the world; some examples of machine learning include:

  • Facial recognition

  • Product/show recommendations

  • Spam filters

  • Text prediction autofill

  • Social media optimization

The Future of Machine Learning

Now that we know what machine learning is and how it works, let’s dive into how it works and what we can expect to see in the future. There are four basic steps to machine learning:

  1. Choose your data: this is pretty obvious! Before you begin, make sure you know if you want to use labeled data (so that the machine delivers a certain outcome) or unlabeled data (you want the data to make autonomous decisions).

  2. Choose your algorithm: choose your algorithm depending on whether you use labeled or unlabeled data, the amount of data in the data set, and the desired result. 

  3. Train your algorithm: choose the proper variables and parameters for your algorithm so that you get your desired results; this can be done completely by the computer, freeing up human time and energy. 

  4. Improve the model: once the model has run a few times, check the output for any errors or issues and provide additional information or data to solve any existing problems.

The future is clear: as data continues to play a massive role in our lives, machine learning will become even more important. We have so many examples of recent innovation in machine learning that we could spend all day telling you about them, but we know you’re not interested in that! Instead, we’ll highlight some advances that make machine learning’s future so bright. 

Personalization and recommendations

One aspect where we can expect to see lots of progress is in the personalization and recommendation niche, where computers will get better and better at recognizing patterns, remembering user preferences, and understanding target markets. Just as Spotify always knows that perfect song to play next or Netflix the perfect show to recommend, this ability of machine learning enhances the user experience significantly.


Chatbots are great examples of machines that are constantly learning: they’re capable of remembering past conversations and experiences to adapt their answers and knowledge to meet customer needs. Not only does this free up humans to work on other projects where their brain power is needed, but also allows customers to find answers easily and quickly. 


You’ve heard of self-driving cars and thanks to machine learning, companies like Tesla have begun piloting the first models. With data from the surrounding vehicles and roads, autonomous vehicles are able to safely drive on their own, eliminating human error that can lead to accidents. 

Computer vision 

Imagine if a computer could look at an x-ray or blood test and accurately diagnose a patient. With machine learning’s advancements, we can expect a future where computers are able to instantly digest, understand, and identify images with extremely high accuracy. In addition to healthcare, this technology is also being developed for airport security. 

Does the future of machine learning sound promising to you? We agree! And if you’re interested in diving into this branch of tech, look no further. Ironhack’s bootcamps have just what you need. 

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