The First Steps You Need To Embrace Machine Learning

The demand for competent machine learning engineers is getting bigger with each passing minute.

Machine learning is one of the hottest technologies today. Given the huge automation and analysis advantages that come with it, it’s understandable that more and more companies are getting interested in adopting machine learning solutions. The interest is so high that the machine learning market is expected to grow USD 11.16 billion by 2024.

It’s not just the companies that are so interested in the technology. The demand for competent machine learning engineers is getting bigger with each passing minute. So, a lot of people (from Python engineers to newcomers) are trying to get into the machine learning field. And you might even be one of them!

The thing is — doing so can be a daunting task. There are a lot of tools, frameworks, and models. There’s the statistical and mathematical side of it all that often intimidates people. And then, there are so many resources out there, from online courses and articles to opinions on forums.

All of those are possible gateways for you to get into machine learning. There aren’t exact recipes that detail how you can become a solid machine learning developer. You have to find the path that makes sense for you. With that being said, the machine learning developers at BairesDev know that there are some general tips any newcomer can follow when taking the first steps into this exciting field. Here they are.

Put Yourself In The Right Mindset

This may feel inconsequential to some, but it’s absolutely essential to learn anything in programming (especially something as complex as machine learning). What does the right mindset look like? It varies from person to person but there are two crucial elements that are always present.

The first is patience. There’s so much to learn in machine learning that, without patience, you’ll be setting up yourself for failure. The second one is curiosity. Since there are so many things to cover, you’ll have to keep your excitement going, going down peculiar wormholes or engaging in discussions with strangers to learn more.

Then there are the things that change from one person to another. For instance, I personally feel that coding comes first and theory (especially the one about math and statistics) comes as needed. Thus, I learn while I practice, which keeps me engaged (rather than reading and reading about concepts before getting the chance to apply them). But, hey, that’s me. Al you need to get your learning going is to be patient and keep an engaged mindset.

Learn Python And Machine Learning Basic Concepts And Tools

If you already know Python, Java, R, or any of the other programming languages best suited for machine learning, then great! You can go on and start with some of the resources I mention below. But if you have no programming experience (or the programming language you do know isn’t a good fit for machine learning), then you need to start with Python.

Why Python? Because it’s easy to learn, there are a gazillion resources for you to better understand it, and because there are many machine learning libraries, frameworks, and tools for it. Spending a couple of months learning and coding with Python is a great way to start your machine learning career. Then, you can start diving into machine learning specifics with frameworks like NumPy, pandas, and matplotlib.

As you tinker with all that, you’ll surely be picking up programming and machine learning concepts, but if you need a more traditional approach, then Elements of AI is a great course to do so (it comes highly recommended by many people across the industry, so you’ll be in great hands). You should also throw in some machine learning and data science tools, especially Jupyter and Anaconda, two of the most used tools out there.

Complement Your Basic Knowledge

While all the above will give you the core tools you’ll need for a machine learning career, any developer worth their salt will tell you that it won’t be nearly enough. The programming world is highly dynamic and specialized, which is why you need to have an understanding of other key technologies and methodologies that complement the basic knowledge you just got.

Which are those? Well, basically anything you can think of but mainly you should focus on web development skills, deep learning techniques for Scikit-Learn and TensorFlow, debugging, and metaprogramming, just to name a few. You might feel that these aren’t necessary but that’s just not true.

More often than not, a machine learning project will force you to step out of your comfort zone and learn some of these things, so you might as well learn and practice with them beforehand. Naturally, you shouldn’t get into these without a robust knowledge of the core concepts of both Python and machine learning.

Learn, Practice, Get Out There

Ideally, the process above should take you 6 months (to say the least). During that time, you’ll have to put in a lot of hours to grasp a lot of concepts. As I said above, the best way to do so will depend on your particular preferences, though you won’t escape from having to practice — a lot. Spending a lot of time with different exercises and your own projects is the only way to hone your skills.

What’s more — you should share your work with other people to get even better. That’s right, sharing your code with others via GitHub can be a great way to reinforce your knowledge and open yourself to new things. And if you feel confident enough, you can even join a team to start collaborating with a machine learning project in a junior capacity.

The idea is to get out there and see how all this programming thing works in real life. You can always learn concepts through courses, ebooks, YouTube videos, and whatnot. But the only way you’ll be an attractive machine learning prospect for those companies obsessed with this technology is by practicing non-stop. Good luck!