Machine learning is a type of artificial intelligence in which computer systems “learn” how to make better decisions from data. In the era of big data, there’s a whole lot of data out there in the world. In fact, there’s so much data that humans can no longer efficiently analyze it. This is where machine learning comes in handy. With machine learning, humans can build and create models that effectively learn to make better decisions—without being explicitly programmed to do so—as they’re exposed to more and more data. Machine learning needs data to process and learn from, so it’s important to make sure your data maintenance processes are up to speed. High quality data leads to accurate insights.
Confused about how machine learning differs from AI? Here’s a quick explanation: AI is a broad term that refers to smart computers that are capable of performing tasks that would normally require human involvement. With machine learning, algorithms are learning from your data to make better predictions. The difference between AI and machine learning is that AI then uses those learnings to make a system act more human by applying what it has learned via automation.
Why Machine Learning Matters
Machine learning is gaining traction in the IT world because we need help sifting through the massive amounts of data that our systems are generating. Humans simply don’t have the time to look at all the data, identify patterns, and make the kinds of decisions that machine learning models can. And in fact, machine learning models are often able to identify patterns in data that humans might miss. Machine learning can do more than humans and machine learning can do it faster.
There are numerous practical applications for machine learning:
- Recommendation engines. Music apps like Spotify and Pandora can make artist recommendations for you based on what you’ve already listened to. Facebook can find people you might know based on your existing friends and the friends of friends.
- Credit checks. Credit card companies and banks can use your financial information to quickly and accurately determine your credit worthiness and mitigate their risks.
- Predictive maintenance. Manufacturers can predict when a piece of machinery will need maintenance based on the maintenance and repair that was required on similar machinery in the past.
- Spam filters. Spam filters can figure out whether or not a message is spam based on characteristics in the subject line, body, and return email address.
- Fraud detection. Banks can identify potentially fraudulent transactions using information about users and typical transactions for them, based on the amount of the transaction, the location where it originated, and other factors.
Any organization that has a large quantity of data can benefit from machine learning. Machine learning can help organizations in a wide range of industries, including social media, banking, and hospitals. As big data continues to grow, machine learning will continue to become more useful and necessary.
What are Machine Learning Techniques?
Machine learning algorithms can be trained using two primary techniques, supervised learning and unsupervised learning.
Supervised learning involves using an established set of data to train the machine learning algorithm to find patterns in it. In a supervised learning exercise, we expose the model to a collection of labeled data points called a training set. Training data must be labeled, or annotated, so the model can recognize the outcomes your model is designed to detect.
Supervised learning models can be developed using classification and regression techniques. A classification technique involves putting data into categories. For example, an individual has a good credit score or a bad one. A regression technique involves predicting constantly changing information, for example, the changes in temperature in a building.
Unsupervised learning involves using unlabeled data and is designed to help the algorithm find unexpected or unknown patterns in the data. This type of learning is an iterative process. One example of unsupervised learning would be spam detection. It wouldn’t be possible to label all of the variables in a test batch of emails, so unlabeled data is used to train the algorithm.
Clustering is a common type of unsupervised learning technique. It is used to find hidden patterns or groupings in data. Clustering attempts to group data points into meaningful clusters, which means the elements in each cluster are similar to each other and different from the elements in the other clusters.
Which data is machine learning using?
While a machine learning model might initially interact with a set of training data that has been specifically prepared, ultimately the model will be using data from your own systems. You know the saying ‘garbage in, garbage out,’ right? That applies to machine learning. If you want your models to learn properly, you have to make sure that your data is in good shape. Data in the real world can get messy for a variety of reasons, and you can end up with duplicate or incomplete records.
It’s essential to do some data cleansing and transformation before using your data for any kind of analysis or machine learning. This is where the right tools come in handy. It’s not possible to clean and transform your data manually. And if you’re going to rely on your data to make important business decisions, it’s essential to make sure your data is as high quality as possible. Some common transformations you might want to do before using data for machine learning are: removing unused or repeated columns, changing data type, addressing missing data, and removing string formatting and non-alphanumeric characters. An ETL tool or data loader can be helpful for transforming and loading data to prepare it for machine learning purposes.
Want to Learn More About AI or Machine Learning?
Machine learning can help your business process and understand data insights faster – empowering users to make data-driven decisions across your organization. For machine learning to be successful, however, your data has to be high quality. As the quality of your data increases, you can expect the quality of our insights to increase as well. Transforming data for analysis can be challenging based on the growing volume, variety, and velocity of big data. This challenge will need to be overcome to unlock the potential of your data and to mobilize your business to move faster and outpace competitors. When you are ready for machine learning, Matillion’s purpose-built data transformation for machine learning can help you increase the ROI on your data, transforming your data so it is machine learning ready!
Learn more about machine learning and how it can work for you in the cloud.
To see how Matillion can help you prepare data for machine learning to unlock the potential of your data, request a demo.
Or get started with Matillion Data Loader for free. Matillion Data Loader makes it simple to replicate your data into a cloud data warehouse, allowing you to create a single source of truth for your data. Built as a SaaS-based data integration tool, Matillion Data Loader includes a number of integrations and gives you a 360-degree view of all your data sources.