Whether you’re aware of it or not, machine learning has become a part of our everyday lives. You can thank machine learning when your car’s backup camera spots a cat behind the tire, or Alexa plays the exact song you asked for.
It’s a fascinating area of data science that promises to become even more integral to almost every industry.
Interested in our Data Science Bootcamp?
What is Machine Learning?
Machine learning accelerates our ability to pull useful insights from massive amounts of data.
In order for machine learning to take place, computers are programmed with a goal (like classifying data points into specific categories). A bunch of data is dumped in, and the machine starts using algorithms to discover patterns in the information, and classify the data points.
But then it gets science fiction-y: those algorithms change and become more nuanced as the machine takes in more data.
It experiments by applying different rules to sort the data more accurately. Typically the machine is “trained” on test datasets before it’s given meaningful, real-life numbers to crunch.
So the algorithms improve based on all the information that’s coming in, without requiring a human to make sense of it all. The machine “learns” the best way to achieve a goal.
How Machine Learning is Changing Our World
With machine learning, a set of algorithms can actually make predictions based on all the data it’s taking in. It can do all of this much faster than the old-fashioned human brain can, and often with better accuracy.
You can blame machine learning for those ads that get you to spend on products you didn’t even know existed. The algorithm has “seen” millions of other Amazon orders that look just like yours. So it knows you’ll spring for the fancy wireless charger, in Walnut, with the built-in nest for your Airpods. They’re that good.
Deep learning takes this same basic concept even further. In deep learning, computers are built to resemble neural pathways, and can make sense of data in a more nuanced way. In this case, the machine is programmed with multiple layers of concepts that it applies to the data.
Deep learning requires less human intervention, since it accomplishes more of the “learning” and self-correcting on its own. But it requires even more data to be effective than machine learning does.
Difference Between Machine Learning & Artificial Intelligence
In machine learning, there’s a clear goal: the machine in question manipulates data, trying to get closer to one key result. It’s a specific application of artificial intelligence.
The concept of artificial intelligence is broader. Artificial intelligence encompasses any system where machines mimic human intelligence and decision-making.
Practical Applications of Machine Learning
Every major tech company, like Netflix, Amazon and Google, uses machine learning to determine how people use their products. It allows them to identify patterns in user data and further improve the experience. Machine learning is responsible for the crazy detour your Maps app throws your way during rush hour. It’s part of just about every sophisticated smart home device, home security system and social media app.
But machine learning goes beyond convenience. It has powerful implications for healthcare, and could change our ability to diagnose early stages of cancer or predict how new drugs interact.
As machine learning improves and we learn how to best harness its power for good, it will almost certainly affect every industry from car manufacturing to cybersecurity.
Careers in Machine Learning
If you’re trying to make a career out of machine learning, you’ll need a rock-solid foundation in coding and data analysis. Large tech companies may have specific machine learning positions, but you’ll likely start off as a data scientist or an engineer.
Here are some specific skills you’ll need to pursue a machine learning career:
- Computer science
- Deep understanding of statistics
- Data modeling
- Python or C++
Studying data science or data analytics will not only give you the hard skills you need to progress in machine learning––you’ll also learn how to think through problems logically, and troubleshoot when things don’t go as expected. Even if you’re working full-time, there are flexible programs out there to get you the skills you need.
Machine learning is an exciting field that will offer plenty of challenges and rewards as it develops. Prepare yourself for lots of heavy number-crunching and problem solving. On the plus side, with the rise in self-driving cars, automated everything and eerily targeted ads, your machine learning job outlook is looking pretty good.