Machine learning is one of the most in-demand tech skills these days. The reason for this is because it is one of the primary sources for automation, along with artificial intelligence (AI). Besides, since it has a high demand, the salaries for machine learning engineers are usually very high – above $100,000. So if you’re trying to build a career as a machine learning, you’re probably wondering if you could self-learn this skill. We’re here to let you know the answer.
Although machine learning is a vast field, and you’ll probably need to learn about several subjects, this is the type of skill that you can learn by yourself if you have its prerequisites. You surely won’t need to have a bachelor’s degree in computer science or mathematics, but it could be beneficial. However, there are some great courses out there you can use to learn this skill in less than six months.
How to Learn Machine Learning without a Degree:
Take a course
When the machine learning trend started, there weren’t many schools that taught this discipline, the only way to make a career as a machine learning engineer was by taking a bachelor’s degree in math or computer science. However, today you have so many options you won’t even have to spend five years to learn all the skills you need.
Some of the best schools to learn machine learning are Galvinize and Springboard. Both schools are recognized as some of the best places to learn machine learning or data science. With them, you’ll learn all of the fundamental knowledge about statistics, math, and Python.
Machine learning engineering is a profession that creates algorithms to identify patterns in large datasets to predict outcomes and analyze trends. But machine learning is like building the human brain or building a machine that can learn without human intervention. So most of the skills you need to become a machine learning engineer are related to math and programming. You’ll probably need to learn statistical fundamentals, algebra, and programming languages like Python or SQL. Besides this, it could also be beneficial for you to learn calculus because it’ll be an essential aspect of machine learning.
Another vital subject that you should learn is deep learning. This is a more complex programming dynamic that’s also used in AI. It is essential for creating products that simulate human activity.
Since you’ll be using programming skills in most of the process of machine learning, you’ll need to learn how to code. If you don’t want to dive into a course all at once, you can start by self-teaching how to program. The best thing to do would be to start with Python, which is the primary programming language in machine learning.
Python is one of the easiest programming languages to learn. The reason for this is because it has a pretty simple syntax. Some people even say that it’s as if you were writing in English because the commands are very similar to this programming language. There are many resources online that could help you learn Python by yourself.
Implement Machine Learning Algorithms and Build Your Own Projects
One of the best ways to find gaps in your knowledge is to start implementing machine learning algorithms in your own datasets. This way, you’ll be able to evaluate if you can efficiently build machine learning algorithms or you still need some practice. Besides, this is especially important if you’re trying to find a job in this field.
Once you start applying for work, recruiters will want to see your experience, and you need to have something to show them. You can work on creating your own projects and showcase your skills with them. If you don’t have any datasets to practice, you could find some on a website called Kaggle.
Having a degree in mathematics or computer science is truly beneficial to start building a career as a machine learning engineer. However, this is a profession that allows you to learn some things by yourself. So, you still need to have some sort of guidance or education to learn the most essential aspects of the field such as statistics, algebra, programming skills, and deep learning.