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This guide will walk you through 8 machine learning projects that are fun for beginners. These projects are the best investment of your time. Learning will make you happy, keep you motivated, and help you move faster. No amount of theory will replace practical experience. You can be lulled into believing you are masterful by reading lessons and textbooks. However, once you start to apply the material, you may find it more difficult than you thought.

Projects allow you to quickly improve your applied ML skills and allow you to explore a new topic. You can also add projects to your portfolio. This makes it easier to find a job, get a better career, or negotiate higher pay.

These are eight fun machine learning projects that beginners can complete. Each project can be completed in one weekend. If you like them, you can expand the scope of your work to include more projects. Jumpstart your data science journey today! Enter your email to receive Quant alpha our 4-part crash course in data science and applied machine learning. This is what we affectionately call the “machine learning gladiator,” but it’s not a new concept. This is one way to build intuition about machine learning quickly. This project aims to apply out-of-the-box models to diverse datasets. This project is amazing for three main reasons.

You’ll first develop intuition about the model-to-problem match. What models are resilient to missing data? What models can handle categorical features? You can read books to get the answers, but seeing the model in action is much more effective. This project will also teach you how to prototype models quickly. It’s difficult to predict which model will be the most successful in real life without trying them.

Two of the most successful stories in modern artificial intelligence are neural networks and deep learning. These networks have led to significant advances in image recognition, text generation, and even self-driving cars.

You should have a manageable data set to get involved in this exciting field. The classic entry point is the MNIST Handwritten Digit Classification Challenge. It is more difficult to work with image data than flat, relational data. The MNIST data can be used by beginners and fit on one computer.

Although handwriting recognition is difficult, it doesn’t require a lot of computational power. We recommend that you start with the tutorial’s first chapter. This tutorial will show you how to create a neural network that solves the MNIST problem with high accuracy.

We are fortunate to have the Enron email database. It includes 500 000 emails from 150 ex-Enron employees, most of them senior executives. It is also the largest public database of actual emails, making it even more valuable.

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