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You might not realize it, but machine learning is already a part of your daily life. Millions of online decisions are powered not by humans, but by algorithms, like the product recommendations you receive on Amazon, the surge price of your ride-share, or the content you see on your Twitter or Instagram feed.
While these algorithms might seem mysterious, or even scarily accurate in some cases, they have been derived from the hard work of data scientists and machine learning specialists. With a close relationship to the application of artificial intelligence and computational statistics, there has never been a better time to learn this in-demand skillset. Don’t take our word for it: Indeed named Machine Learning Engineer as the “best job in the U.S.,” with a 344 percent growth rate in job openings and average base salary of $146,085.
Keep reading if you’re interested to learn more about this booming industry sector and the best way to acquire skills to level up your career path in machine learning.
If you’re new to the field, the easiest way to think about ML is the use of statistics to find patterns within data. The “machine” aspect comes into play because it is computers detecting those patterns and improving user experience through artificial intelligence. Platforms attempt to extract as much information as possible from you— what you buy, what links you click, which content you react to— in order to make highly educated guesses about your future behavior or habits. This is where the practice of “big data” and automation comes in.
The two main techniques of machine learning are supervised and unsupervised learning. In the first, ML engineers develop an algorithm by building a model based on sample “training data,” which the computer uses to “teach” itself predicted outcomes. In the second, computers find unknown data patterns by finding a structure in unlabeled data, common in clustering tasks.
If this is all starting to sound a little abstract (or sci-fi!), that’s OK. Let’s dive into some practical applications of machine learning.
As you might have surmised from the amount of algorithms and recommendations you come across, there are a number of practical problems machine learning can solve. Just a few areas ML is put into practice are:
Getting started with machine learning can seem complex, but there are a number of tools and paths available for future ML engineers. To start on this career journey, many enter with a masters-level background in computer science, artificial intelligence, data science or software engineering. Below, we’ve broken down steps you can take on this path:
1.Brush Up on Basics
If you consider yourself a math wiz, machine learning might be for you! Working with linear models of this nature requires a background in linear algebra, statistics, probability and data analysis and visualization techniques. It’s best to become an expert in simple linear and logistic regression models before diving any further into this field.
2. Consider Educational Options
Although obtaining an undergrad and/or masters degree in one of the field above is a great way to break into tech, it’s no longer your only choice. You might want to consider a streamlined bootcamp program in data science or coding to build up your skills toolbox effectively and at a lower price. There are also plenty of self-guided resources you can look into if you’re interested in a more independent route.
3. Master Coding
As part of your educational journey, you will need to have a strong grasp on algorithms and models used by programming languages. For machine learning, it’s recommended you have a working knowledge of C++, Java, R, and especially Python, depending on the role you want. It will also be helpful for you to gain a mastery of complementary data science tools like Jupyter and Anaconda, as well as data visualization tools like pandas, NumPy and Matplotlib. Once again, a number of bootcamps provide concentrated instruction in these subjects.
4. Obtain Certifications
Earning an industry-standard certification from a major cloud computing provider is another great resume booster and way to signal to clients or employers that your skills meet a certain standard level. Keep in mind, these certifications alone don’t prove competence, so you will want to ensure you have the skills to back them up on the job. In the machine learning field, you can earn a certification in Microsoft Azure, Google Cloud Platform or Amazon Web Services Machine Learning Speciality.
5. Explore Careers
Towards the end of your time in training, you’ll want to consider which job position within the machine learning engineering field suits you best. Since the role so closely aligns with data science and software engineering, you might consider a job with one of those titles, and the ability to move into a more highly specialized role. It’s practically guaranteed that demand for ML roles will continue to increase in large and small organizations, with niches to explore like Business Intelligence Developer, Human-Centered Machine Learning Designer and Natural Language Processing Scientist.
Learning machine learning can be a years-long process, but the rewards of entering a top-growing and top-paying field are definitely worth it. There’s no doubt artificial intelligence and machine learning are set to change the way we interact with the world, and entering the field provides an exciting opportunity to shape the next generation of tech. Starting off on this path can be as easy as browsing our bootcamps in data science and coding and taking the first step towards your machine learning career!
