To explain machine learning, I think it is essential to begin with defining an algorithm. Simply put, an algorithm is a series of instructions that solve a specific problem. Algorithms receive input and work to produce the intended output.
In terms of computers, the instructions produced by the algorithm should tell the computer how to transform inputs from the world into relevant information. Those inputs are various forms of data, and the information outputted are findings for people, instructions for machines, or another input for the following algorithm in line.
- The input & output of the algorithm should be defined precisely
- Each step of the algorithm should be particular & unambiguous
- The algorithm should stop after a finite amount of steps are executed
An algorithm is the procedure of a computer inputting data and outputting data based on the set of given instructions.
Machine Learning is an element of Artificial Intelligence and Computer Science that emphasizes data and algorithms.
Machine Learning can be seen easiest as an algorithm that learns based on past decisions. For example, your movie recommendations on [whatever] streaming service result from machine learning displaying the movies you should like based on the past movies you’ve selected to watch. This algorithm becomes more accurate when you not only watch movies but give certain movies a thumbs-up or a positive review. Again, this can become even more precise if you only watch romance and comedies because now the algorithm won’t suggest a thriller, suspense, or horror movie.
Machine Learning is unique because it can produce accurate predictions based on the initial programmed instructions. This prediction can happen because the stored historical data makes up the algorithm’s input, not a user updating the code every day.
Machine Learning in Business
Machine Learning is most valued in the business world when it can give these businesses a viewpoint of their customer’s behavior tendencies. In addition, the returned data can also be used to track the responses of their new and old products, which creates helpful input when developing new products.
Some common examples of ML you’ve come across are in Google’s search engine, automatic chatbots, autofill for your web browsers, or even fraud detection. You often notice it most when you’re online shopping for a pair of Nike shoes, and now every ad you encounter is an ad for Nike or shoes in general.
Types of Machine Learning
There are four main approaches to Machine Learning: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. The data scientist in charge decides which method of learning they would like to use based on what type of data they want to produce or predict.
Supervised Learning: A data scientist provides labeled training data and defines the variables they want to be assessed. The input and output of the algorithm are specified from the beginning.
Unsupervised Learning: The algorithm scans through data sets seeking out relevant connections. Labeled data is not required since the algorithm looks for patterns to connect the data on its own.
Semi-Supervised Learning: As the name suggests, this model is a medium of the two previously defined learning methods. A data scientist may provide a small dose of labeled training data but the algorithm then freely explores the dataset to develop its unlabelled data and understanding.
Reinforcement Learning: A data scientist uses this model when specific rules for a machine to complete a multi-step procedure. While the algorithm is working through the problem set, the scientist provides positive or negative feedback, which makes up the reinforcement.
Key Take Away
Machine Learning is a computer accessing data and learning for itself. The goal is to curate enough data for the computer to learn automatically, so human intervention is unneeded or limited.
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