Azure Machine Learning Studio
Azure Machine Learning Studio – represents the Microsoft platform we use to harness data for predictive analytics and machine learning. If we hear one more accountant mumbling about “machine learning” we may choke them…they use words with a clue as to what they mean. Let’s see if we can offer some insight. It is important to remember that there are two components to this, the analytic skills of the human being, and the data platform that does the heavy lifting. We are an Azure Cloud Platform partner which means we have the technology part of this down. Ask the mumbling accountant about the tech, then come back and talk to us. Let’s dive in or you can start with an overview video Data Science for Beginners
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.
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.
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.
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.
- 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.
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 dataset 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 of so that you can tell whether your model predicts accurately.
There you have it, a teaser on Azure Platform Machine Learning…there is plenty more where that came from, and for that, you need to reach out to a member of our Predictive Analytics Group.