Machine Learning 101 At A Glance: What You Need To Know
Introduction
Machine learning is a tool in the AI toolbox that can be used for many tasks. It uses algorithms to build machines that can learn from data, without being explicitly programmed. Machine Learning techniques are used to build machines that can learn from data, without being explicitly programmed. ML algorithms are used for classification, prediction and clustering. Classification of objects into different categories is the most common use of ML algorithms. Prediction tasks involve predicting values of variables based on previous observations. Clustering involves grouping data according to similarities between observations
Machine Learning is a branch of Artificial Intelligence (AI).
Machine Learning is a subset of artificial intelligence, which is a broad field that includes many different subfields. Machine learning uses mathematical algorithms and statistical methods to find patterns in data, with the goal of making predictions about future events based on those patterns. The discipline has many applications including in business and industry, research, healthcare and even entertainment!
In this article we’ll cover:
- What is machine learning?
- Who invented it?
- When did it come about?
Machine Learning 101 At A Glance: What You Need To Know
Machine Learning techniques are used to build machines that can learn from data, without being explicitly programmed.
Machine Learning (ML) is a branch of artificial intelligence that allows computers to learn from data, without being explicitly programmed. It’s used to build machines that can learn from their own experience, previous data or environment.
The most popular ML algorithms are supervised learning algorithms which use labeled examples of input-output pairs (training set) in order to generate accurate predictions on new examples:
- Classification – predicting discrete values such as spam/not spam emails; or predicting whether a patient will be diagnosed with cancer based on their medical history and genetic information;
- Regression – predicting continuous values such as temperature at noon tomorrow;
- Clustering – grouping similar observations together into clusters according to some similarity metric like distance between observations in multi dimensional space
ML algorithms are used for classification, prediction and clustering.
- Classification is the process of assigning a label to an object. For example, you might want to classify emails as spam or not spam using machine learning algorithms.
- Prediction is the process of making a prediction about an object or event. For example, you could use machine learning models to predict whether someone will buy something based on their past purchases and demographics information like age, gender and location.
- Clustering is grouping objects into clusters based on their similarity so that similar objects belong in the same cluster while dissimilar ones belong into different ones (or none at all).
Classification of objects into different categories is the most common use of ML algorithms.
Classification of objects into different categories is the most common use of ML algorithms. Classification involves grouping data into different categories, based on their properties or characteristics.
For example, you could use machine learning to classify images as either indoor or outdoor scenes, based on their content; or you could use it to determine whether an image contains a human face or not (i.e., facial recognition).
Prediction tasks involve predicting values of variables based on previous observations.
Prediction tasks involve predicting values of variables based on previous observations. For example, you can use machine learning algorithms to predict stock prices, weather and traffic patterns, or even the probability of a user clicking a particular ad. Prediction tasks are the most common use case for machine learning algorithms and they’re used by companies across all industries: from healthcare to finance to retailing.
Prediction problems are often framed as classification problems (e.g., “Is this patient likely to have lung cancer?”) but there’s no reason why we couldn’t frame them as regression problems (e.g., “How much will this patient’s treatment cost?”). In both cases though we’re trying to find patterns in our data sets so that we can make better decisions about future outcomes based on past observations–hence why they’re called predictive analytics!
Clustering involves grouping data according to similarities between observations.
Clustering is a data mining technique that involves grouping similar objects together. It can be used for unsupervised learning, pattern recognition and market segmentation.
A clustering algorithm attempts to find natural groupings in the data set by identifying clusters of observations with similar values for one or more variables (observations). The goal of clustering is usually to discover groups that reflect some underlying structure in the underlying system being modeled by the data.[1]
Machine learning is a tool in the AI toolbox that can be used for many tasks.
Machine learning is a tool in the AI toolbox that can be used for many tasks. It’s not AI, it’s not a magic wand, and it’s not a silver bullet.
Machine learning algorithms are trained on datasets of data (often labeled) and then used to make predictions on new data sets based on what they learned from previous examples. Machine learning is often confused with deep learning–a subset of machine learning that uses neural networks or other complex mathematical models to process information–but both terms refer to specific types of algorithms used within broader categories called “artificial intelligence” or “machine intelligence.”
Conclusion
Machine learning is a powerful tool that can be used for many different tasks. It’s important to understand the basics of machine learning, so that you can make informed decisions about whether this technology is right for your business or organization.