Tour of Real-World Machine Learning Problems

Real-world examples make the abstract description of machine learning become concrete.

In this post you will go on a tour of real world machine learning problems. You will see how machine learning can actually be used in fields like education, science, technology and medicine.

Each machine learning problem listed also includes a link to the publicly available dataset. This means that if a particular concrete machine learning problem interest you, you can download the dataset and start practicing immediately.

Real World Machine Learning

Real World Machine Learning
Photo by SMI Eye Tracking some rights reserved.

Most Popular Kaggle Datasets

These first 10 examples of machine learning problems were taken from the competitive machine learning website Kaggle.com. Popularity was based on the number of participating teams.

Most Popular Research Datasets

The next 10 machine learning problems are the most popular on the University California at Irvine Machine Learning Repository website that traditionally hosts machine learning datasets used by the machine learning research community.

Final World

We took a whirlwind tour of 20 real-world machine learning problems.

These are actual problems posed or investigated by science and business organizations around the world.

What’s even more exciting is that these diverse problems have publicly available datasets and are also widely studied and understood.

This means you can download the data right now and explore the problem by implementing your own model, or reproduce someone else’s from a paper or blog post.

6 Responses to Tour of Real-World Machine Learning Problems

  1. shivaprasad October 27, 2017 at 3:02 am #

    I am very much impressed by this article sir,really it helped like anything.thank you sir

  2. Paul January 18, 2018 at 9:10 am #

    Dear Mr. Jason,
    Hundreds of thousands of students decide to take up machine learning but more than half of this number get phased out due to the sheer fear of complexity of the subject but you on the other hand did a fantastic job explaining the subject with such ease. I just wanted to extend a warm gesture of gratitude. Thanks a lot for helping me and thousands of other like me. Thank you.

  3. Aimee November 28, 2018 at 11:57 am #

    Hi Jason! 🙂

    I’m planning on playing around with the poker data set above and was going to try it with LDA, CART and finally Gradient Boosted Decision Trees (GBDT) with XGBoost, but I’m concerned about the classification process since some hands could fit into more than one class. Ideally, you want to predict the best possible hand out of multiple possibilities so I wasn’t quite sure how this may be done. Logically, I guess, you’d somehow determine all possible classes a hand could fit in and then use the class with the greatest value as the final answer since the classes increase as the hand improves. Any suggestions on this approach? What other models would you suggest trying for multi-class classification?

    Thanks! Love your books so far!!! 😀

    • Jason Brownlee November 28, 2018 at 2:52 pm #

      Sounds like an intersting problem, sorry, I’m not familiar with it. I’m hesitant to make suggestions.

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