Introduction
You’ve probably heard about machine learning, but do you really understand what it is? In this post, we’ll take a look at what supervised learning is and how it works. Specifically, we’ll define supervised learning and discuss some examples of this type of data-driven approach that’s used to make predictions or decisions.
Machine Learning is the process of building a model to make predictions or decisions.
Machine Learning is a form of artificial intelligence (AI). The goal is to build a model that can make predictions or decisions based on input data. This means that you don’t have to write any code for the program yourself; instead, you give it some training data, and it builds its own model from there.
Machine learning models can be applied in many different fields–from medicine and finance to robotics and online advertising–but their basic principles are generally the same:
- Data + Algorithm = Model
- Model -> Prediction/Decision
Supervised Learning is where you have a set of labelled data and you want to learn from that data in order to make better predictions or decisions.
Supervised learning is where you have a set of labelled data and you want to learn from that data in order to make better predictions or decisions. You use the label of each example as the truth for what it should be, and then try to predict those labels for new examples.
The other type of machine learning is unsupervised learning, which uses unlabelled data, and tries to understand patterns within the data.
The other type of machine learning is unsupervised learning, which uses unlabelled data, and tries to understand patterns within the data.
Unsupervised learning is a type of machine learning where the algorithm tries to find patterns in the data but not necessarily make predictions. For example: if you want to figure out what kind of music someone likes based on their listening history – this would be an example of unsupervised learning because there are no labels for genres or artists attached to each song (like “this is metal,” “that’s jazz”).
The best way to think about supervised learning is if you had a large dataset with rows, with each row containing all the information you need about that observation (x). For example, we could have a dataset of human names (x), and their ages (y). A supervised learning algorithm would try to predict age based on name.
The best way to think about supervised learning is if you had a large dataset with rows, with each row containing all the information you need about that observation (x). For example, we could have a dataset of human names (x), and their ages (y). A supervised learning algorithm would try to predict age based on name.
Supervised learning algorithms are used in classification and regression problems. These are two types of problems that involve making predictions based on known data points. Classification involves predicting what class an object belongs to, while regression involves predicting numerical values of some kind (like the price of stocks or whether someone will vote for Donald Trump).
A specific type of supervised learning algorithm called artificial neural networks is used extensively by AI systems today because it allows computers to learn from examples without being explicitly programmed every step along the way like humans do when they learn new tasks through trial-and-error feedback loops from teachers or parents!
When training a supervised learning model, we will first use our training set to fit our model parameters and then use those parameters during prediction time to make accurate predictions (on new data points).
When training a supervised learning model, we will first use our training set to fit our model parameters and then use those parameters during prediction time to make accurate predictions (on new data points).
The process of fitting a supervised learning model is similar to how we would train any other kind of machine learning algorithm: we have some data that we want to predict something about, so we feed it into the algorithm along with some sort of cost function or objective function–the goal being for this function’s output value as close as possible without going over its bounds. This means that when you’re using these kinds of algorithms in practice, you’ll likely be using something like gradient descent where you’re trying minimize something like mean squared error or cross entropy loss between predicted values vs actual values from your dataset.
If we were doing classification, then our hypothesis function would just return 1 for any observation that matches our classification label and 0 otherwise; if we were doing regression, then our hypothesis function would return some value indicating how close the new observation is from 0 (indicating no impact) or 1 (indicating that this observation has 100{6f258d09c8f40db517fd593714b0f1e1849617172a4381e4955c3e4e87edc1af} impact).
If we were doing classification, then our hypothesis function would just return 1 for any observation that matches our classification label and 0 otherwise; if we were doing regression, then our hypothesis function would return some value indicating how close the new observation is from 0 (indicating no impact) or 1 (indicating that this observation has 100{6f258d09c8f40db517fd593714b0f1e1849617172a4381e4955c3e4e87edc1af} impact).
In order to find the optimal parameters for this function, we can use gradient descent:
Conclusion
Machine learning is a very broad topic, but it can be broken down into two main types: supervised and unsupervised learning. In this article we covered what these mean and the differences between them.
