You’ve heard about it, but maybe you don’t know exactly what it is. Or you’re familiar with the concept, but want to know exactly what you can do with a career in machine learning. We’ll cover these doubts and much more in the article below.
What is Machine Learning?
Machine learning is a field of computer science devoted to training computers to learn and make predictions without being explicitly programmed to do so. Instead of relying on static instructions (think massive if-else trees), machine learning algorithms ingest, analyze, and interpret large amounts of training data to build models that can then be used to make predictions.
Even if you’re just getting into machine learning, we’re sure that you’ve definitely heard about it before. Let’s demystify the world of machine learning by first answering some commonly asked questions.
Is machine learning the same as artificial intelligence?
The terms “machine learning” and “artificial intelligence” are sometimes used interchangeably, but they do not mean the same thing:
AI is a field of computer science that encompasses the simulation of human intelligence in a large number of ways: natural language processing (ChatGPT), computer vision (Google Lens), and robotics (Boston Dynamics).
Machine learning, on the other hand, is an approach to AI that focuses on algorithms that seek to enable computers both to learn from data and to improve their performance over time.
Machine learning vs deep learning–are they the same?
No, machine learning and deep learning are not the same thing! Deep learning is a subset of machine learning that focuses on creating models using multi-layered neural networks. Exactly what those are is not in the scope of this blog, but you can think of it as a really complex algorithm that requires a lot of data and computational resources to train.
Do I need experience in web development to work in machine learning?
No! Most people who work in machine learning have no experience in web development. Machine learning is all about developing and using algorithms to produce models that can make decisions and predict outcomes–apart from both web developers and machine learning engineers writing code, the two have very little in common.
Should I study data science or machine learning?
Well, it all depends on what you want to do! Data science is the application of statistical and scientific methods to gain insights from data---and you can definitely use machine learning algorithms to achieve such ends. Machine learning, on the other hand, is more about techniques that enable computers to make predictions.
If you’re into practical applications, study data science; if you’re into theory, study machine learning.
Now that we’ve defined machine learning and answered some of your burning questions, let’s dive a little deeper and explore how machine learning affects our daily lives.
Real life applications of machine learning
While machine learning is theoretical in nature, there are thousands of real, practical applications for machine learning models in use in industry today. We’ve already named some companies and products that use machine learning earlier in this post: OpenAI’s ChatGPT, Google Lens, and Boston Dynamics. But these companies and products are not outliers; machine learning techniques can be applied in all kinds of contexts, such as:
Healthcare: here’s a real, human impact. Machine learning has been used for diagnosing patients, discovering new drugs, predicting patient outcomes, and improving treatment plans---helping millions of people.
Finance: this is the pinnacle of big data! We’ve seen machine learning used for algorithmic trading, fraud detection, risk assessment, and (controversially) credit scoring.
Transportation: this is where graph theory is in action. Machine learning algorithms thrive at solving problems surrounding route optimization, demand forecasting, and (you guessed it) autonomous driving.
Agriculture: it’s not just about growing crops! While machine learning can help with crop yield prediction, important advances have been made in optimizing resource allocation, detecting diseases in plants and livestock, and even genetically engineering newer, better crops.
City planning: Cities use machine learning to understand how cities will grow and execute proper city planning.
Sales: lots of companies use machine learning for sales purposes; for example, car companies use machine learning to understand how buyers will behave in the future.
Streaming services: music companies and streaming platforms build sophisticated models to personalize the customer experience to match desires and needs.
Working in Machine Learning
Have we convinced you that machine learning is the best path into tech for you?! That’s fantastic! It might seem like a complex field and, well, you’re right, it is! But there are lots of roles where a machine learning background is useful.
Roles in machine learning
Many people have preconceptions about what you can do with a machine learning degree or bootcamp under your belt. While machine learning is generally theoretical, the truth is that you’re not confined to doing research for the rest of your life. There are plenty of well-paying, practical roles that you can take on with machine learning in your toolbelt. Let’s go over a few of them:
Machine Learning Engineer: bet you didn’t see this one coming! The word engineer implies practical and machine learning engineers develop and deploy machine learning models to do any number of cool and interesting tasks.
Data Scientist: data scientists often do exploratory data analysis or hypothesis testing. Pattern recognition and machine learning go hand-in-hand; to that end, data scientists can use machine learning tools to analyze complex data sets, identify patterns, and then predict outcomes much faster than they would otherwise be able to.
AI Architect: while still technical, architects also take on a quasi-managerial role. They design and develop the overall strategies and infrastructure for implementing machine learning solutions for an organization.
AI Ethicist: here’s where we can find the intersection of machine learning and philosophy. AI ethicists are responsible for addressing issues of fairness, bias, and transparency in AI systems.
And those aren’t the only roles available! Machine learning consultants, data engineers, and research scientists all use machine learning skills on a regular basis. In the future, we’ll see almost every role either using machine learning tools (like software engineers using github copilot) or creating tools with machine learning no matter your industry and that’s why machine learning skills are so highly-demanded by hiring managers worldwide.
How to get into machine learning
There are so many ways to break into machine learning. Different people have different preferred ways of learning, so here’s four ways we think you can gain a foundational knowledge of machine learning, or learn machine learning, if you will:
Books: for those of us who learn best by reading, there are plenty of resources available. The two books we recommend for those just learning about machine learning are Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurelien Geron and Machine Learning for Absolute Beginners by Oliver Theobald.
Online Communities: as always, there’s a Reddit community for exactly what we need! Try joining and participating in r/MachineLearning and r/LearnMachineLearning. A quick online search can also put you in touch with like minded individuals who are starting out on their machine learning journey.
Bootcamps: There’s no better way to learn than in a safe, structured environment. Ironhack offers exciting bootcamps for roles all over the tech landscape. Supercharge your learning and jump start your career with an Ironhack bootcamp!
YouTube: deep in your learning journey and stuck on a specific problem? YouTube and other online videos can help you visually solve a problem or boast your knowledge and share solutions with the community.
Is machine learning for me?
Alright, we’ve captured your interest! We’re not surprised; machine learning is an incredibly interesting field that has practically limitless potential. What more could you ask for?! If you can answer yes to the following questions, getting into machine learning may be your next career step:
Do you like learning about statistics, coding, and new technologies?
Are you curious, creative, and want to find new ways to solve problems?
Do you grasp new concepts quickly and are passionate about the work you do?
Do you like data and want to use it to get answers?
You answered yes to all of these questions, right?! We thought so! And you’re in luck: at Ironhack, we've designed our bootcamps so that your dreams can become a reality and you can become the next great machine learning professional. Check out our course offerings today and don’t hesitate! This is your time to shine.
