Machine Learning (ML) may have piqued your interest but you haven’t dipped your toes in its shiny waters just yet. Why not? Maybe all that’s holding you back from diving into ML (and potentially making a career pivot towards this emerging technology) is that you don’t know where to begin. That’s where this guide comes in!

According to a report by Research and Markets, the global machine learning market is expected to grow from $1.4 billion in 2017 to $8.8 billion by 2022. There’s an increasing demand for machine learning talent across the board, so it’s a fruitful field to pursue if you’re passionate about it. To start, refresh yourself on the following 3 areas of study or learn them for the first time if you haven’t already: multivariable calculus, linear algebra, and modular coding.

Once you have a mastery of these 3 skills, you’re ready to dive into ML itself. There are a wide variety of resources out there to help you learn machine learning depending on how you want to approach it. Here are some of the best ones out there:

Machine Learning Resources

1. Top 10 Algorithm Tour

When you start to get into Machine Learning, you’ll be introduced to the “No Free Lunch” theorem pretty early. Basically, it states that no singular algorithm works best for every problem. If you want to familiarize yourself with the top 10 algorithms that are useful for machine learning newbies, this is a great guide.

2. Introduction to State of the Art

If you want courses that have approachable curriculum and teach you state-of-the-art machine learning, Jeremy Howard’s fast.ai courses are a great resource. Go through at least Course 1 and Course 2, doing all of the exercises that accompany them. This will put you ahead of the game on model-building and help you learn some techniques that some current industry practitioners don’t even know.

3. A Great MOOC with a Paid Certificate 

This notoriously challenging 11 week Machine Learning course from Stanford and Andrew Ng is 100% free. Although you can pay for a certificate at the end if you want one, this course will take you from Machine Learning novice to a deep understanding of the implementation of Machine Learning that can catapult you into successfully applying it in new ways that can change the world.

4. The ML Self-Starter Route

If you’re more of a self-starter and not a fan of MOOCs (which often model themselves after traditional college courses), EliteDataScience has a great self-starter guide that will take you from Machine Learning zero to Machine Learning hero. This option also allows you to learn at your own pace and may work better in conjunction with your full-time job.

5. Deploy Your First Projects

It’s time for you to take ML models and apply them to data in a way that is callable and performant. Apply the theory you’ve learned to different problems by using Kaggle, which provides a variety of datasets rated by their popularity and contextualized with different projects that other people have already built on them. If you can’t find what you’re looking for there, you can also check this Github repository for awesome public data resources to experiment with.

Another resource is Project Pro. It’s an Online Training Course and Project for data science, Big Data, and ML. They provide a library of verified, end-to-end project solutions in Machine Learning and Big Data that you can reuse. You can save money and time by reusing curated, verified, pre-solved projects.

Also, you can release projects faster with end-to-end reusable project solutions. And, if you can afford to run your model on the Cloud, Paperspace is a great resource for an intro to running ML on the Cloud.

ML is gaining steam and will provide a lot of opportunities for technical professionals who have mastered it. Use this guide to go from ML beginner to a new level mastery, which could help you transition to ML positions and change the trajectory of your career.