This lesson is a preface to the following lesson on machine learning and artificial intelligence in sensing. In this current lesson, we will be introduced to factors driving the need for machine learning to transform large sets of data into information that can be used to make smarter decisions. In this lesson, we will learn the meaning of terms, features, and how features can help convert raw data into prediction using machine learning. Using a few day-to-day examples, students will learn how all our lives are being touched by fusion of several exciting and emerging fields of technologies such as machine learning, artificial intelligence, and big data. Let's consider the question, why machine learning? As we discussed in the previous module, that is module one, we need three main components to enable intelligent manufacturing framework. First, sensors to collect data. Second, a data acquisition and management system to process, and store data. Third, machine learning and knowledge discovery component to process the stored data. We spoke about components number one and component number two, which is sensors collecting data and signal processing in the previous lessons. Now, let's discuss how the output of signal processing can be used by the machine learning and knowledge discovery component to make decisions. In this context, we will introduce the concept of machine learning. To understand the concept of machine learning, let's consider an example. You may have used Netflix to watch your favorite movie. You log in, search for a movie title, then select a movie, and you watch it halfway or in full. Then you sign out and go about your daily life. Once you log back again into Netflix system, have you noticed that the system gives you a list of recommended movies in a singular genre to the one you have watched previously? How does that occur? The system uses data from all previous system uses, and learns about our likes and dislikes, and makes prediction based on our past uses of Netflix. This is an example of machine learning working in our daily lives. In the Netflix example, some features used to make predictions are the age of the user, genre of the movie, and movie actors. Features are salient way to abstract and represent data that are useful to make predictions. There are several other examples of machine learning in action in our daily lives. For example, social networking profile recommendations features of LinkedIn and Facebook uses machine learning. The online shopping recommendations in Amazon and eBay are enabled by machine learning. The machine learning is also pivotal in implementation of automated office assistants such as Apple's Siri and Microsoft's Cortana. In general terms, we can explain the conceptual working model of machine learning as follows. Machine learning focuses on the development of computational models that can teach themselves to learn patterns or models from existing data and utilize the learned model when exposed to new data. In the advanced analysis course, we spoke about big data, high volume of data of wide variety being generated at high velocity, the three V's of big data. Machine learning systems search through big data to learn patterns in the data and use these patterns to make predictions or recommendation in light of new data. For example, recommending movie genres a user may like based on pattern learned from the past users is the use of machine learning.