Discover Machine Learning Without (Too Much) Jargon

Data Science Basics| Infographic| Data analysis | | Robert Kelley

According to Google Trends, interest in the term machine learning (ML) has increased by more than 300 percent since Dataiku was founded in 2013. We’ve watched ML go from the the realm of a relatively small number of data scientists to the mainstream of analysis and business.

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And while this has resulted in a plethora of innovations and improvements among our customers and for organizations worldwide, it has also provoked reactions ranging from curiosity to anxiety among people everywhere.

Discover the fundamentals of machine learning with our free guidebook.

Infographics: An Illustrated Way to Learn

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Machine learning doesn't have to be intimidating. But when people are intimidated by it, they tend to avoid it. Unfortunately, avoiding machine learning isn't really all that possible these days. We have enough anxiety in our daily lives, right? Feeling familiar with these terms and concepts will make you more comfortable with many of the trends shaping analytics and business at large.

We started making these infographics a few months ago, and even seasoned data scientists have found them to be valuable and instructive. So whether it's your first or your 500th time exploring machine learning, we hope you'll find the guidebook valuable.

Go Further

We decided to make this guide because we’ve noticed that there aren’t too many resources out there that answer the question, “What is machine learning?” while using a minimum of technical terms. Actually, the basic concepts of machine learning aren’t very difficult to grasp when they’re explained simply.

In this guidebook, we’ll start with some definitions and then move on to explain some of the most common algorithms used in machine learning today, such as linear regression and tree-based models. Then we’ll dive a bit deeper into how we go about deciding how to evaluate and fine-tune these models. Next, we’ll take a look at clustering models, and then we’ll finish with some resources that you can explore if you want to learn more.

If you’re ready to dive into machine learning, get the complete guidebook for a tour of the major topics of the field. You'll be surprised by just how sophisticated it gets but also by how easily you'll be able to grasp it.

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