A Quick Introduction to Machine Learning

How machine learning algorithms “learn”

Two ways that machine learning algorithms learn to predict an outcome are supervised and unsupervised learning. In supervised learning, we give the algorithm a mapping of inputs to the desired outcomes. The algorithm tries to figure out the relationship between them, so that for subsequent inputs, it can predict outcomes following the same logic as in the original mapping. An important requirement in supervised learning is to have data where both the inputs and the outcomes are known. This is called labeled data. In unsupervised learning, we don’t have data with the inputs and desired outcomes. Here, the algorithm looks for similarities and patterns in the data and tries to determine a strategy for categorizing the inputs. The algorithm will apply the same strategy to categorize subsequent inputs.

Machine learning methods: Image processing
One machine learning method is image processing. We already saw how this works in the satellite image and crop type example. This method is used to extract information from images, find patterns, segment an image or compress an image, so it takes up less storage space.

Machine learning methods: Natural language processing
Natural language processing is a method of translating between computer and human languages. The goal of natural language processing is to get a computer to read a line of text and understand the meaning, just as a person would. An example is a chatbot. It expects people to type “how do I” or “I can’t find” followed by keywords that refer to things one should be able to do or find on that particular website and then it provides the appropriate response. With each interaction the chatbot has, it learns to be more and more sophisticated in how it interprets what people type and how it phrases its responses.

Machine learning methods: Sentiment analysis
Sentiment analysis is a machine learning method that interprets the emotions within text to measure the inclination of people’s opinions, whether they’re positive, negative or neutral. An example is reading and interpreting people’s sentiments from reviews of dining experiences in restaurants.

Machine learning methods: Deep learning
Have you ever been shown an image and it’s all fuzzy and you’re supposed to guess what it is? Then gradually the resolution gets better and better. So first, you know, it’s a person and then you see, oh, it’s a woman, and then you recognize the unique physical characteristics that differentiate your sister from a stranger, even if they have the same height, hair and eye color. That’s how deep learning works. The algorithm makes many passes over the same data, gaining precision each time until it can predict what the image actually is. It works using structures of interconnected nodes that imitate the workings of a human brain. An example of deep learning is self-driving cars. The onboard cameras are constantly feeding deep learning algorithms in the car’s computer that analyze and interpret the images of its surroundings and adjust the speed and direction of travel so as to avoid collisions.