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 Machine Learning
 What it is and why it matters
 Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
-    Importance     Today's World
-         Who Uses It     How It Works
 Evolution of machine learning
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 While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. Here are a few widely publicized examples of machine learning applications you may be familiar with:
-    The heavily hyped, self-driving Google car? The essence of machine learning.
+The heavily hyped, self-driving Google car? The essence of machine learning.
-    Online recommendation offers such as those from Amazon and Netflix? Machine learning applications for everyday life.
-    Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation.
+Online recommendation offers such as those from Amazon and Netflix? Machine learning applications for everyday life.
-    Fraud detection? One of the more obvious, important uses in our world today.
+Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule
+creation.
+Fraud detection? One of the more obvious, important uses in our world today.
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 What's required to create good machine learning systems?
     Data preparation capabilities.
     Algorithms – basic and advanced.
     Automation and iterative processes.
     Scalability.
     Ensemble modeling.
-Machine learning infographic
 Did you know?
     In machine learning, a target is called a label.
     In statistics, a target is called a dependent variable.
     A variable in statistics is called a feature in machine learning.
     A transformation in statistics is called feature creation in machine learning.
-Machine learning in today's world
-By using algorithms to build models that uncover connections, organizations can make better decisions without human intervention. Learn more about the technologies that are shaping the world we live in.
-White Paper
-Opportunities and challenges for machine learning in business
-This O'Reilly white paper provides a practical guide to implementing machine-learning applications in your organization.
-Read white paper
-Fact Sheet
-Expand your skill set
-Get in-depth instruction and free access to SAS Software to build your machine learning skills. Courses include: 14 hours of course time, 90 days free software access in the cloud, a flexible e-learning format, with no programming skills required.
-Machine learning courses
 Will machine learning change your organization?
-This Harvard Business Review Insight Center report looks at how machine learning will change companies and the way we manage them.
- Download report
-Article icon
 Applying machine learning to IoT
 Machine learning can be used to achieve higher levels of efficiency, particularly when applied to the Internet of Things. This article explores the topic.
-Read the IoT article
 Who's using it?
 Most industries working with large amounts of data have recognized the value of machine learning technology. By gleaning insights from this data – often in real time – organizations are able to work more efficiently or gain an advantage over competitors.
 Financial services
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 Analyzing data to identify patterns and trends is key to the transportation industry, which relies on making routes more efficient and predicting potential problems to increase profitability. The data analysis and modeling aspects of machine learning are important tools to delivery companies, public transportation and other transportation organizations.
-Learn More About Industries Using This Technology
 What are some popular machine learning methods?
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 Reinforcement learning is often used for robotics, gaming and navigation. With reinforcement learning, the algorithm discovers through trial and error which actions yield the greatest rewards. This type of learning has three primary components: the agent (the learner or decision maker), the environment (everything the agent interacts with) and actions (what the agent can do). The objective is for the agent to choose actions that maximize the expected reward over a given amount of time. The agent will reach the goal much faster by following a good policy. So the goal in reinforcement learning is to learn the best policy.
-    Humans can typically create one or two good models a week; machine learning can create thousands of models a week.
+Humans can typically create one or two good models a week; machine learning can create thousands of models a week.
-Thomas H. Davenport, Analytics thought leader
-excerpt from The Wall Street Journal
 What are the differences between data mining, machine learning and deep learning?
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-Machine learning infographic
 Machine Learning
 The main difference with machine learning is that just like statistical models, the goal is to understand the structure of the data – fit theoretical distributions to the data that are well understood. So, with statistical models there is a theory behind the model that is mathematically proven, but this requires that data meets certain strong assumptions too. Machine learning has developed based on the ability to use computers to probe the data for structure, even if we do not have a theory of what that structure looks like. The test for a machine learning model is a validation error on new data, not a theoretical test that proves a null hypothesis. Because machine learning often uses an iterative approach to learn from data, the learning can be easily automated. Passes are run through the data until a robust pattern is found.
-Deep learning infographic
 Deep learning
 Deep learning combines advances in computing power and special types of neural networks to learn complicated patterns in large amounts of data. Deep learning techniques are currently state of the art for identifying objects in images and words in sounds. Researchers are now looking to apply these successes in pattern recognition to more complex tasks such as automatic language translation, medical diagnoses and numerous other important social and business problems.
 How it works
 To get the most value from machine learning, you have to know how to pair the best algorithms with the right tools and processes. SAS combines rich, sophisticated heritage in statistics and data mining with new architectural advances to ensure your models run as fast as possible – even in huge enterprise environments.
-Algorithms: SAS graphical user interfaces help you build machine learning models and implement an iterative machine learning process. You don't have to be an advanced statistician. Our comprehensive selection of machine learning algorithms can help you quickly get value from your big data and are included in many SAS products. SAS machine learning algorithms include:
+Algorithms: SAS graphical user interfaces help you build machine learning models and implement an iterative machine learning process. You don't have to be an advanced statistician. Our comprehensive selection of machine learning algorithms can help you quickly get value from your big data and are included in many SAS products.
+SAS machine learning algorithms include:
 Neural networks
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 Tools and Processes: As we know by now, it’s not just the algorithms. Ultimately, the secret to getting the most value from your big data lies in pairing the best algorithms for the task at hand with:
 Comprehensive data quality and management
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 An integrated, end-to-end platform for the automation of the data-to-decision process
-Do you need some basic guidance on which machine learning algorithm to use for what? This blog by Hui Li, a data scientist at SAS, provides a handy cheat sheet.