Advances in technology allow businesses to gather enormous amounts of data – sometimes more than they know what to do with. Machine learning is a key to putting that information to work.
Machine learning helps businesses properly analyze all the data they collect, identify trends they might otherwise miss and turn swaths of data into information that can inform the decision-making process.
If you can employ machine learning concepts, you can position yourself as an essential part of a business. This guide will walk you through the basics and help you get started with machine learning using online resources.
Machine learning involves developing computer algorithms – guidelines that tell a computer how to perform a specific task – that can automatically learn and improve with more experience.
While machine learning has existed for some time, it has grown increasingly relevant in recent years, as businesses embrace digital experiences and generate considerable amounts of data. This data contains all sorts of information: what we buy, where we travel, what interests us. It can contain a detailed picture of a person or collection of people and their interests and motivations. This can be valuable for organizations, but without the right tools, those profiles and patterns remain hidden.
“Whenever there is a problem that needs to be solved even approximately, but (especially) when this problem has evaded our manual effort, it is worth considering machine learning,” says Kyunghyun Cho, associate professor of computer science and data science at New York University.
Padhraic Smyth, computer science professor in University of California–Irvine’s Donald Bren School of Information and Computer Sciences and associate director for the college’s Center for Machine Learning and Intelligent Systems, says machine learning techniques can be applied in a range of areas, from medical diagnosis to autonomous driving.
People interested in machine learning are often problem solvers. They are motivated by the challenge of finding patterns that others can’t see. They develop tools that can sift through huge datasets and find the commonalities. They want to turn the unknown into the known and help make better decisions and produce better results.
If that sounds like you, then learning machine learning could serve you well.
AI vs. Machine Learning: What’s the Difference?
Machine learning is a concept of applied artificial intelligence.
Smyth describes AI as a study that “tries to address the general problem of how we can make computers more intelligent and behave more like humans.” While “machine learning basically is about making computers learn from data.”
Other fields of study within AI involve machine learning concepts. Those include computer vision, which is teaching computers to understand images and video, and natural language processing, which is teaching computers to understand text. Both rely on machine learning concepts for problem solving.
Subsets within machine learning include deep learning, which Cho describes as “using a highly complicated differentiable computational network that can learn to capture a highly sophisticated mapping between high-dimensional, highly structured observations and targets.”
It is called “deep,” Cho says, because these computational networks often involve stacking modules or layers. Each one represents a different individual process that the network has been taught how to perform. They are then combined to create a deeper understanding of what the machine learning system is processing or looking for.
How Is Machine Learning Useful?
Machine learning is helpful for handling massive amounts of data. In particular, machine learning can be useful when we need to use data to predict something, Smyth says. Machine learning algorithms learn as more information is fed to them. The more data they process, the more they refine their output, theoretically producing better results over time.
As you might imagine, machine learning experts are in high demand in many fields. According to the jobs site Indeed, machine learning engineer careers are experiencing incredible growth, with a 344% increase in job postings in 2019. With an average base salary of $146,085, this position is not just sought-after, it’s also lucrative.
Machine learning requires a strong math and computer science foundation. It can seem overwhelming, especially for beginners. But with the right approach, you can build your skills, gain experience and potentially land a position in machine learning as you master the field.
Before You Get Started With Machine Learning…
Brush up on the underlying techniques and technologies. Cho recommends having an understanding of concepts like calculus, linear algebra, probability and statistics, and algorithms.
Also check out programming languages used in machine learning. Python, a high-level and general-purpose programming language, is a good place to start. It’s considered one of the more approachable programming languages and can provide machine learning beginners with a good entry point.
Similarly, R is an essential tool for machine learning engineers. The programming language is used for statistical computing, graphics and data analysis.
Resources and References to Get Started
- Learn Python, DataCamp via LearnPython.org, free.
- Learn Python 3, Codecademy, free for a basic account, premium plans start at $19.99 after a free trial.
- R Programming, Coursera, free to audit, $49 a month to subscribe.
- R Programming Tutorial – Learn the Basics of Statistical Computing, freeCodeCamp.org YouTube course, free.
- CS50’s Introduction to Computer Science, edX, free, certification available for $90.
Basic Machine Learning Concepts
As you get started with machine learning, you’ll start to run into some terminology that will grow familiar over time.
A model, for instance, is a “representation of what a machine learning system has learned from training data,” according to Google’s Machine Learning Glossary. Your model takes in data and creates a prediction. How it does that depends on what kind of machine learning you’re using.
Supervised learning is a type of machine learning that requires you to train a model using a dataset with labeled data – data that has been tagged with additional contextual information, like demographics or location. This learning technique continues until a model achieves a certain level of performance. Supervised learning might employ a classification model, which categorizes information based on the information it is provided, or a regression model, which tries to make predictions based on the inputs.
Meanwhile, unsupervised learning looks for patterns in data that hasn’t been tagged with contextual information. It may organize data into clusters.
Other types of machine learning algorithms include semisupervised learning, which uses unlabeled and labeled data, and reinforcement learning, which involves teaching a model “to maximize return when interacting with an environment,” according to Google’s glossary.
In addition to these types of learning, you’ll also start to see models come together in the form of neural networks. These networks, designed to replicate the way our own brains process information, consist of multiple ways to process data. Neural networks are also part of the underpinning technology that allows for deep learning.
Classes for Beginners
- Machine Learning, Coursera, free, $79 to earn a certificate.
- Introduction to Machine Learning Course, Udacity, free.
- Machine Learning Fundamentals, edX, free, certification available for $350.
- Introduction to Machine Learning Concepts, Cloud Academy, $49 per month after seven-day free trial.
- Machine Learning Fundamentals, DataQuest, starting at $24.50 per month.
Classes for Intermediate Learners
Put Your Knowledge of Machine Learning Into Practice
After gaining more experience, put your knowledge to work in a practical way. Build your own machine learning project. Pick a topic that interests you, one that has a considerable amount of data available that will allow you to train your model.
As you build your machine learning algorithm, you’ll start discovering patterns and finding new ways to interact with and understand your dataset. You can learn even more by sharing your work with others. Find an online community that might show an interest in your findings or that is focused on machine learning in general. Ask for feedback, and use these insights to find ways to fine-tune your model.
Resources and References to Get Started
- Advanced Machine Learning, edX, free, certification available for $149.
- Theoretical and Advanced Machine Learning with TensorFlow, TensorFlow, free.
- Applied Machine Learning – Beginner to Professional, Analytics Vidhya, $250.
- Applied Machine Learning for Everyone, Udemy, $94.99.
- Structuring Machine Learning Projects, Coursera, free to audit, $49 per month after free trial.
Machine learning is a field that requires a base of knowledge. Whether you need to take a course depends on how familiar you are with engineering and mathematics concepts. Cho says the starting point for each person “depends on how deeply you want to be aware of machine learning before using it to solve your problems.”
Many of the tools needed to learn machine learning techniques are available for free online, so it is feasible for self-starters with a background in these concepts to learn on their own. However, courses can provide more guidance and offer direction for those who are interested in pursuing specific applications of machine learning.
You can succeed in learning machine learning by applying techniques that help you learn other skills. Create study schedules, find study groups to collaborate with or work with a mentor. Find a technique that works for you and allows you to perform your best.
Machine learning is not easy to learn, but it is rewarding and can open up promising career opportunities.