Artificial intelligence is an umbrella term that includes many different technologies. These technologies include natural language generation, natural language processing, computer vision, and many more.
But no matter what you read or hear about artificial intelligence, you'll hear the term "machine learning."
Machine learning is a branch of artificial intelligence that provides computers with the ability to learn without being explicitly programmed.
It takes in data from inputs, like email, analytics, or CRM data. Then, it uses that information to find patters in data and/or make predictions about future outcomes.
It then learns from the success (or failure) of its pattern recognition and predictions. Using what it learns, it continually evolves. As a result, it gets smarter over time.
In order for a computer to be able to learn without being programmed, the computer has to be able to receive input, process the input, and then make decisions based on the input.
In order for a computer to be able to receive input, the computer must be able to be "trained". In other words, it must be able to receive data that already has the correct answer and be able to learn from that data. The computer must also be able to receive data that doesn't have the correct answer and be able to learn from that data.
Machine learning has been around for decades, but it's only in the last few years that it's been gaining mainstream attention.
The field of machine learning is broad and complex, and there are many different approaches to solving problems using machine learning.
The success and failure of these approaches depends on how well they perform, so it's important to know what different approaches to machine learning are available.
The four main categories of machine learning are supervised learning, unsupervised learning, reinforcement learning, and deep learning.
Supervised learning is when a program is provided with a set of training data and is then able to use that data to predict a result. Supervised learning requires that the data provided to the program be labeled in some way.
Unsupervised learning is when a program is provided with a set of training data and is able to identify patterns within that data without being given any labels. In unsupervised learning, the program is not able to make predictions, but it is able to identify trends and other information from the data that it's provided.
Reinforcement learning is when a program is given a set of actions to take and is then able to learn from those actions. Reinforcement learning is different from the other types of machine learning in that the program is not given any information about the correct answer. Instead, the program learns by trial and error.
Deep learning is a subset of machine learning that uses a multi-level approach to solving problems. Deep learning uses artificial neural networks to analyze data and solve problems.
Deep learning has become very popular recently, and many of the advances that we've seen in machine learning have been due to deep learning.
Machine Learning Applications
There are many applications of machine learning. Machine Learning can be applied to the field of social media, ecommerce, robotics, finance, and business.
Some of the applications of machine learning include:
- Social media: We use machine learning to filter, search and sort our social media feeds.
- Ecommerce: Machine learning is used to filter search results, recommend products and predict customer behavior.
- Robotics: Machine learning is used to control autonomous vehicles, such as self-driving cars. It is also used to control drones and other smart home devices.
- Finance: Machine learning is used to predict stock price, detect fraud and make trading decisions.
- Business: Machine learning is used to predict customer behavior, detect fraud, automate processes and improve decision making.
Machine learning can be used across different industries. There are many applications of machine learning and it is one of the most popular techniques used in data science.
Machine Learning for Marketers and Businesspeople
If you're a non-engineer, you likely won't need to know more than this unless you're looking to study AI seriously.
If you're a marketer, salesperson, or business executive, you'll never have to build a machine learning system.
But you will want to be able to talk to a machine learning engineer on your team. You'll want to be able to say: "Here's the problem I'm trying to solve. This is what I'm looking at as a potential use case. Can machine learning help me do this smarter?"
To do that, you only need to know the fundamentals:
That machine learning is taking the machines or software you use every day and making them smarter continuously.
Traditional automation is all human-powered. Humans write the rules that the machines follow. These machines only follow the rules humans set, and if conditions change or updates are needed, humans need to make them. Ironically, much business and marketing automation today is still largely manual and largely unintelligent.
There's no question it's helpful. But it only scratches the surface of what is now possible thanks to machine learning.
Systems that use machine learning can make recommendations based on the analysis of data. They can prescribe actions based on those recommendations. And they can even take some specific actions on their own.
In the background, the machine is learning and getting better all the time.
So, machine learning is pretty straightforward to understand if you want to use machine learning in business. And you should because it has hugely beneficial applications across all types of business functions and industries.
To benefit from some of those applications, you don't need to build machine learning models yourself. You don't have to truly understand data science.
You just need to know what machine learning is capable of doing. You need to be able to find use cases for it in your business and apply it. If you can, machine learning can give your business superpowers. But you have to get started now.
How to Get Started with Machine Learning
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As Chief Content Officer, Mike Kaput uses content marketing, marketing strategy, and marketing technology to grow and scale traffic, leads, and revenue for Marketing AI Institute. An avid writer, Mike has published hundreds of articles on how to use AI in marketing to increase revenue and reduce costs. Mike is the co-author of Marketing Artificial Intelligence: AI, Marketing and the Future of Business (Matt Holt Books, 2022). He is also the author of Bitcoin in Plain English, a beginner’s guide to the world’s most popular cryptocurrency.