Introduction to Machine Learning
by Namho · 34 things on NewTwos
- Learning objectives:
- Understand the different types of machine learning.
- Understand the key concepts of supervised machine learning.
- Learn how solving problems with ML is different from traditional approaches.
- Machine learning(ML) is the process of training a piece of software, called a model, to make usefulpredictions or gnenerate content from data.
- Types of ML Systems
- ML systems fall into one or more of the following categories based on how they learn to make predictions or generate content:
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Generative AI
- Supervised learning
- Supervised learning models can make prediction after seeing lots of data with the correct answers and then discovering the connections between the elements in the data that produce the correct answers. These ML systems are "supervised" in the sense that a human gives the ML system data with the known correct results.
- Regression
- A regression model predics a numeric value. For example, a weather model that predicts the amount of rain, in inches or millimeters, is a regression model.
- Classification
- Classification models predict the likelihood that something belongs to a category. Unlike regression models, whose output is a number, classification models output a value that states whether or not something belongs to a particular category.
- Classification models are divided into two groups: binary classification and multiclass classification. Binary classification models output a value from a class that contains only two value, for example, a model that outputs either rain or no rain. Multiclass classification models output a value from a calss that contains more than two values, for example, a model that can output either rain, hail, snow, or sleet.
- Unsupervised learning
- Unsupervised learning models make prediction by being given data that does not contain any correct answers. An unsupervised learning model's goal is to identify meaningful patterns among the data. In other words, the model has no hints on how to categorize each piece of data, but instead it must infer its own rules.
- A commonly used unsupervised learning model employs a technique called clustering. The model finds data points that demarcate natural groupings.
- Clustering differs from classification because the categorize aren't defined by you. For example, an unsupervised model might cluster a weather dataset based on temperature, revealing segmentations that define the seasons. You might then attempt to name those clusters based on your understanding of the dataset.
- Reinforcement learning
- Reinforcement learning models make predictions by getting rewards or penalties based on actions performed within an environment. A reinforcement learning system generates a policy that defines the best strategy for getting the most rewards.
- Generative AI
- Generative AI is a class of models that creates content from user input. For example, generative AI can create unique images, music compositions, and jokes; it can summarize articles, explain how to perform a task, or edit a photo.
- Generative AI can take a variety of inputs and create a variety of outputs, like text, images, audio, and video. It can also take and create combinations of these.
- We can discuss generative models by their inputs and outputs, typically written as "type of input"-to-"type of output". For example, the following is a partial list of some inputs and outpus for generative models:
- Text-to-text
- Text-to-image
- Text-to-video
- Text-to-code
- Text-to-speech
- Image and text-to-image