1. Machine Learning

2. Definition

3. Machine learning is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. In 1959, Arthur Samuel defined machine learningas a "Field of study that gives computers the ability to learn without being explicitly programmed".

4. Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data.

5. The process of machine learning is similar to that of data mining. Both systems search through data to look for patterns. However, instead of extracting data for human comprehension -- as is the case in data mining applications -- machine learning uses that data to improve the program's own understanding. Machine learning programs detect patterns in data and adjust program actions accordingly. For example, Facebook's News Feed changes according to the user's personal interactions with other users. If a user frequently tags a friend in photos, writes on his wall or "likes" his links, the News Feed will show more of that friend's activity in the user's News Feed due to presumed closeness.

6. Machine learning is a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look.

7. Examples of machine learning applications that you may be familiar with: The heavily hyped, self-driving Google car? The essence of machine learning. Online recommendation offers like 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. Fraud detection? One of the more obvious, important uses in our world today.

8. Why the increased interest in machine learning? Resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. Things like growing volumes and varieties of available data, computational processing that is cheaper and more powerful, and affordable data storage. All of these things mean it's possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale. The result? High-value predictions that can guide better decisions and smart actions in real time without human intervention. One key to producing smart actions in real time is automated model building. Analytics thought leader Thomas H. Davenport wrote in The Wall Street Journal that with rapidly changing, growing volumes of data, "... you need fast-moving modeling streams to keep up." And you can do that with machine learning. He says, "Humans can typically create one or two good models a week; machine learning can create thousands of models a week."

9. How is machine learning used today? Ever wonder how an online retailer provides nearly instantaneous offers for other products that may interest you? Or how lenders can provide near-real-time answers to your loan requests? Many of our day-to-day activities are powered by machine learning algorithms, including: Fraud detection. Web search results. Real-time ads on web pages and mobile devices. Text-based sentiment analysis. Credit scoring and next-best offers. Prediction of equipment failures. New pricing models. Network intrusion detection. Pattern and image recognition. Email spam filtering.

10. What are some popular machine learning methods? Two of the most widely adopted machine learning methods are supervised learning and unsupervised learning. Most machine learning – about 70 percent – is supervised learning. Unsupervised learning accounts for 10 to 20 percent. Semi-supervised and reinforcement learning are two other technologies that are sometimes used.