This Is How Marketers Can Easily Understand Machine Learning
“Computers are incredibly fast, accurate, and stupid. Human beings are incredibly slow, inaccurate, and brilliant. Together they are powerful beyond imagination.” - Albert Einstein
You might think it’s impossible, as a marketer, to truly grasp a concept like machine learning. What if I told you it’s achievable?
Artificial intelligence is forecasted to have trillions of dollars in annual impact, yet many marketers struggle to understand what AI is and how to apply it to their marketing programs. At last, AI Academy for Marketers is here to help.
AI Academy is an online educational platform that helps you understand, pilot, and scale AI. We created this platform to help marketers gain access to affordable, AI-focused education that advances learning and aids in AI implementation.
Machine Learning 101 for Marketers is one of the Academy’s deep-dive Certification Courses for Members. In the Course, Jim Sterne, President of Target Marketing of Santa Barbara, dives into what machine learning is, how it works, how it can drive performance in marketing and sales, and how to implement it in your marketing strategy.
What to Expect from the Course
You don’t need a computer science degree to understand AI and apply it to your work. You do need to understand how it works, what it’s good for, and where you fit inーbecause, yes, there is a world where machines and humans work together harmoniously.
In this beginner-level, 2.5-hour Certification, you will:
- Learn what machine learning is (in plain English) and how it works.
- Discover how machine learning can be used to drive performance in marketing and sales.
- Gain actionable insights on how to get started with machine learning in your business.
Understanding Machine Learning
As Jim explains, machine learning is just a part of AI. It’s broken up into three categories:
- Supervised learning
- Unsupervised learning
- Reinforcement learning.
Supervised learning comprises the majority of machine learning. In this approach, there is a correct answer to your question, but the machine needs a lot of data to give you the right output. For example, when you ask the computer if this is a picture of a cat, it will be able to confirm that the photo is a cat. Although, it needs to have enough photos of cats already as input data to give you the correct prediction.
In unsupervised learning, the machine finds patterns in unlabeled data. The goal of unsupervised learning is to understand the deep, underlying patterns in order to learn more about the data. The problem with unsupervised learning is that the information you retrieve may or may not be useful. Although, looking for small anomalies that could correlate in any way could become a potential business opportunity. For example, you tell the machine, “These are my current customers; who will my next customers be based on this information?"
Reinforcement learning teaches the system how to make decisions through a series of “rewards” based on the results it produces. The machine optimizes over time by continuously learning. For example, if the result is more clicks to your site, then the machine is moving in the right direction as clicks are achieved.
Here’s another simple way to think about these concepts: In supervised learning, you know the answer. In unsupervised learning, you don’t know the answer and you’re looking for the machine to tell you. In reinforcement learning, there is no absolute right answer, but we reward the machine for improving toward a goal.
A Deeper Look: How Machine Learning “Learns”
After receiving your Certification, you will be able to speak knowledgeably about machine learning and know the necessary steps to implement it successfully.
Additional key takeaways include:
- AI is an umbrella term and machine learning is one of its functions.
- Machine learning is broken up into three categories: supervised, unsupervised, and reinforcement. If you know the differences between them, you’re in pretty good shape.
- Machine learning “learns” through decision trees/random forest, support vector machines, and neural nets/deep learning.
Decision trees break down the data until it reaches a prediction point. Decision trees become a random forest by taking the data from decision trees and choosing specific examples and variables. When you bring all of this together, the machine gives you a solution that is statistically more valuable than any of the individuals by themselves.
Support vector machines are brilliant at segmentation when there are thousands of variables to consider. Support vector machines excel at taking a large variety of data and grouping them in different ways to bring insight and categorize people. This will benefit the marketer because we will have a better understanding of how to market to these consumers.
Neural networks become deep learning. A neural network analyzes different inputs, assigns weights to them, and decides what the next node will do. Deep learning has lots of hidden nodes and layers that affect the outcome. Deep learning teaches the machine how to organize inputs to predict outputs. In the end, it all comes together for one decision.
Ready to discover these and other important AI concepts? Sign up for the AI Academy below.