There are lots of buzzwords out there: machine learning, deep learning, and much more. And while they may sound similar, they actually differ considerably, and we’re here to define both, talk about their similarities and differences, and make sure that you’re an expert by the end of this article.
But before we get there, it’s important to understand that both machine learning and deep learning are two approaches to enabling artificial intelligence.
What is Machine Learning?
Let’s get right into it: what’s our machine learning definition? Machine learning is a relatively broad term that encompasses methods designed to enable artificial intelligence using data. Machine learning algorithms generally fall under two categories: supervised learning and unsupervised learning. In both cases, large quantities of data are used to train models to extract meaningful patterns and relationships. The trained models can then be applied to new data or tasks.
Machine learning encompasses various algorithms and techniques, including the set of techniques that we refer to as “deep learning.” For the purposes of this article (and, indeed, this is how the two terms are used colloquially), we will consider machine learning and deep learning to be two different approaches to training models to make predictions and/or decisions based on data.
Real life examples of machine learning
Machine learning is used way more often than you might think! It used to be a niche skill, but with the advent of powerful tools such as PyTorch and TensorFlow, many more software engineers and data scientists have begun to use machine learning techniques in their projects. Here are some examples of how machine learning is making our lives better every day:
Spam detection: machine learning models can analyze email content to classify emails as either spam or non-spam, helping you to filter out unwanted messages from your already crowded inbox.
Recommendation systems: are you on social media? Do you use any streaming apps? If so, your preferences and behavior are being used by machine learning models to give you better recommendations.
Medical diagnosis: doctors now have access to tools that can take in symptoms, test results, and imaging to assist in disease diagnosis. How cool is that?
Fraud detection: banks employ machine learning models to constantly analyze user accounts and transactions for unusual behavior in order to prevent fraud such as identity theft or credit card scams.
What is Deep Learning?
Neural networks and deep learning go hand-in-hand. Deep learning is a subset of machine learning that uses tools called artificial neural networks to learn from data. Neural networks use multiple interconnected layers of artificial neurons (continuing the brain metaphor), or nodes, that are trained to synthesize higher-level patterns in data. Deep learning is seen as a more powerful form of machine learning as it can be used to extract meaningful insights from larger datasets and used to solve more complex problems—but deep learning models also require significantly more computational power and skill to train.
Real life examples of deep learning
We use deep learning when the problem is more complex and requires more training and processing than what standard machine learning technique would allow. But when we say complex, we don’t mean things like space travel! Even something that might be super easy for humans, such as recognizing shapes and objects, is actually an extremely difficult task for machines. Let’s consider an example.
Say you’re training a machine learning and a deep learning model to recognize a house. A machine learning model might recognize large, somewhat rectangular structures on the ground to be houses. A deep learning model, on the other hand, would recognize structures with windows, doors, and a roof to be houses. If you give both models a photo of a house that’s upside down, the machine learning model might not be able to deduce it’s a house because the ground and sky are not where it was expecting them to be, while the deep learning algorithm would see windows, doors, and roof and correctly conclude the object in the photo was indeed a house.
What else can deep learning models do? Here are some more examples:
Natural language processing: have you heard of ChatGPT? Languages are incredibly rich and complex, but deep learning models are able to consume and synthesize language like no other technique we’ve ever discovered.
Autonomous driving: the future is here! Many car manufacturers (not just Tesla!) are now offering autonomous driving software. When conditions are right, you’re able to let go of the steering wheel and just let the car take you where you need to go. Autonomous driving models are only going to get better and we predict that every car will soon offer the feature as standard.
Drug discovery: science is never easy and pharmaceutical companies are using deep learning models to analyst large chemical datasets to identify potential drug candidates and expedite the drug discovery process.
How Do I Become an Expert?
Becoming an expert in machine learning or deep learning requires a combination of experience, knowledge, curiosity, and skill. You certainly do not need to have a degree to be an expert, but schooling in the form of a bootcamp or academic courses definitely helps you to build a strong foundational knowledge to springboard your learning journey. Here are some steps we think you should take if you want to become a machine learning expert:
Develop a strong mathematics foundation: machine learning is a branch of computer science and, as such, requires a strong mathematics foundation. A mathematics degree is not a prerequisite to pursuing machine learning, but you should be comfortable with topics such as linear algebra, calculus, probability, and statistics. If you’re not comfortable just yet, don’t worry—you can still get started on your machine learning journey and learn the theory as you go.
Learn how to program: while machine learning is heavy on theory, it’s absolutely an applied field and the best way to practice machine learning or deep learning is with python. The machine learning community overwhelmingly uses Python and libraries such as Pandas, scikit-learn, TensorFlow, and PyTorch.
Practice your skills: there is nothing more important than practice. There are many free, publicly-available datasets on which you can practice machine learning techniques. Hands-on experience helps you deepen your understanding and develop more problem-solving skills.
Be active in the community: to be considered an expert, you have to always be up-to-date on the latest trends and findings. Connecting with professionals in the field, attending industry events, and reading academic papers are all ways to stay relevant.
Pursue advanced topics: once you have a solid foundation, you can explore advanced topics such as deep learning, reinforcement learning, computer vision, or natural language processing. Focus on what you’re interested in.
Which is Better: Machine Learning vs Deep Learning?
We can’t say which one is better or worse because the relative strengths and weaknesses of each approach greatly depend on your specific problem set. In general, machine learning is a good choice for problems that can be solved with relatively simple algorithms while deep learning is a good choice for problems that require more complex analysis and can benefit from the power of neural networks. Let’s take a look at some cases where you might want to use machine learning but not deep learning, and vice-versa.
Machine learning is better when…
You have a small dataset: machine learning algorithms can train models with much less data than you would think.
You need to interpret the results: deep learning models are often black boxes—if you need to understand the results of your model, you might be better with a machine learning approach.
You need to make predictions quickly: it can be much faster to train machine learning models as they require less data and less processing power.
Deep learning is better when…
You have a large dataset: deep learning algorithms require vast amounts of data.
You need to make accurate predictions: we’re not claiming that machine learning models are not accurate; however, if you need a precise prediction, you might be better with a deep learning model.
You need to solve a complex problem: some traditional machine learning algorithms might struggle with finding patterns in complex datasets. Consider a deep learning model instead!
In the end, the best choice of technique to use is dependent on the specific problem you are trying to solve. If you’re not sure about which approach would be best for your use/case, it would be a good idea to consult with a data scientist or a machine learning expert.
With so many innovations in such a wide array of industries, machine learning is probably the most exciting field to be in right now. If you’re eager to be at the forefront of innovation, want to unravel the potential of data, and wish to make a significant impact on the world, then we think there’s no better starting point than enrolling in one of Ironhack’s bootcamps to get your start into tech. Embrace the journey, seize the opportunity, and empower yourself with the skills and knowledge it takes to break into the world of machine learning. What are you waiting for?