What is machine learning?
Machine learning is a data science technique that allows computers to use existing data to forecast future behaviors, outcomes, and trends. Using machine learning, computers learn without being explicitly programmed.
Machine learning is considered a subcategory of artificial intelligence (AI). Forecasts or predictions from machine learning can make apps and devices smarter. When you shop online, machine learning helps recommend other products you might like based on what you've purchased. When your credit card is swiped, machine learning compares the transaction to a database of transactions and helps detect fraud. When your robot vacuum cleaner vacuums a room, machine learning helps it decide whether the job is done.
For a brief overview, try the video series Data Science for Beginners. Without using jargon or math, Data Science for Beginners introduces machine learning and steps you through a simple predictive model.
What is Machine Learning in the Microsoft Azure cloud?
Azure Machine Learning is a cloud predictive analytics service that makes it possible to quickly create and deploy predictive models as analytics solutions.
You can work from a ready-to-use library of algorithms, use them to create models on an internet-connected PC, and deploy your predictive solution quickly. Start from ready-to-use examples and solutions in the Cortana Intelligence Gallery.
Azure Machine Learning not only provides tools to model predictive analytics, but also provides a fully managed service you can use to deploy your predictive models as ready-to-consume web services.
What is predictive analytics?
Predictive analytics uses math formulas called algorithms that analyze historical or current data to identify patterns or trends in order to forecast future events.
Tools to build complete machine learning solutions in the cloud
Azure Machine Learning has everything you need to create complete predictive analytics solutions in the cloud, from a large algorithm library, to a studio for building models, to an easy way to deploy your model as a web service. Quickly create, test, operationalize, and manage predictive models.
Machine Learning Studio: Create predictive models
In Cortana Intelligence Gallery, you can try analytics solutions authored by others or contribute your own. Post questions or comments about experiments to the community, or share links to experiments via social networks such as LinkedIn and Twitter.
Use a large library of Machine Learning algorithms and modules in Machine Learning Studio to jump-start your predictive models. Choose from sample experiments, R and Python packages, and best-in-class algorithms from Microsoft businesses like Xbox and Bing. Extend Studio modules with your own custom R and Python scripts.
Operationalize predictive analytics solutions by publishing your own
The following tutorials show you how to operationalize your predictive analytics models:
- Deploy web services
- Retrain models through APIs
- Manage web service endpoints
- Scale a web service
- Consume web services
Key machine learning terms and concepts
Machine learning terms can be confusing. Here are definitions of key terms to help you. Use comments following to tell us about any other term you'd like defined.
Data exploration, descriptive analytics, and predictive analytics
Data exploration is the process of gathering information about a large and often unstructured data set in order to find characteristics for focused analysis.
Data mining refers to automated data exploration.
Descriptive analytics is the process of analyzing a data set in order to summarize what happened. The vast majority of business analytics - such as sales reports, web metrics, and social networks analysis - are descriptive.
Predictive analytics is the process of building models from historical or current data in order to forecast future outcomes.
Supervised and unsupervised learning
Supervised learning algorithms are trained with labeled data - in other words, data comprised of examples of the answers wanted. For instance, a model that identifies fraudulent credit card use would be trained from a data set with labeled data points of known fraudulent and valid charges. Most machine learning is supervised.
Unsupervised learning is used on data with no labels, and the goal is to find relationships in the data. For instance, you might want to find groupings of customer demographics with similar buying habits.
Model training and evaluation
A machine learning model is an abstraction of the question you are trying to answer or the outcome you want to predict. Models are trained and evaluated from existing data.
When you train a model from data, you use a known data set and make adjustments to the model based on the data characteristics to get the most accurate answer. In Azure Machine Learning, a model is built from an algorithm module that processes training data and functional modules, such as a scoring module.
In supervised learning, if you're training a fraud detection model, you use a set of transactions that are labeled as either fraudulent or valid. You split your data set randomly, and use part to train the model and part to test or evaluate the model.
Once you have a trained model, evaluate the model using the remaining test data. You use data you already know the outcomes for, so that you can tell whether your model predicts accurately.
Other common machine learning terms
- algorithm: A self-contained set of rules used to solve problems through data processing, math, or automated reasoning.
- anomaly detection: A model that flags unusual events or values and helps you discover problems. For example, credit card fraud detection looks for unusual purchases.
- categorical data: Data that is organized by categories and that can be divided into groups. For example a categorical data set for autos could specify year, make, model, and price.
- classification: A model for organizing data points into categories based on a data set for which category groupings are already known.
- feature engineering: The process of extracting or selecting features related to a data set in order to enhance the data set and improve outcomes. For instance, airfare data could be enhanced by days of the week and holidays. See Feature selection and engineering in Azure Machine Learning.
- module: A functional part in a Machine Learning Studio model, such as the Enter Data module that enables entering and editing small data sets. An algorithm is also a type of module in Machine Learning Studio.
- model: A supervised learning model is the product of a machine learning experiment comprised of training data, an algorithm module, and functional modules, such as a Score Model module.
- numerical data: Data that has meaning as measurements (continuous data) or counts (discrete data). Also referred to as quantitative data.
- partition: The method by which you divide data into samples. See Partition and Sample for more information.
- prediction: A prediction is a forecast of a value or values from a machine learning model. You might also see the term "predicted score." However, predicted scores are not the final output of a model. An evaluation of the model follows the score.
- regression: A model for predicting a value based on independent variables, such as predicting the price of a car based on its year and make.
- score: A predicted value generated from a trained classification or regression model, using the Score Model module in Machine Learning Studio. Classification models also return a score for the probability of the predicted value. Once you've generated scores from a model, you can evaluate the model's accuracy using the Evaluate Model module.
- sample: A part of a data set intended to be representative of the whole. Samples can be selected randomly or based on specific features of the data set.