So the process of calculating new features goes under the name of feature engineering. For example, that's when you calculate the average time between two user visits from the history of all the visits of the same user. In this case, they are called calculated features or engineered. Or features could also be inferred from other features. For example, think of the number of times a user visited your website, or the browser that they used, or the time that they visited your website at and so on. These features could be directly measurable. In other words, features are the properties we are using to categorize our data. In machine learning, a feature is an individual measurable property of something that is being observed. These numbers that describe a record are also called features. A record is a list of attributes that a data point has, often these are numbers but sometimes they are categories and they could be expressed in text. A row in a table corresponds to a data point. So, let's use an example to define some common vocabulary that will be used throughout the course. So that's also tabular data, data is arranged in tables and some of the columns of a table relates to columns of another table, but that's still called tabular data. You find tables also in databases or when you have a collection of files that are all interconnected with keys that have relations with one another. Essentially, it's a file where data is organized in rows and columns. There are many other formats which have a tabular nature. You can think of these as Excel spreadsheets, but very common are files called CSV, which stands for comma-separated values, or TSV, which is tab-separated values. It's called tabular because it can be represented in a table with rows and columns. Tabular data is the simpler data you can feed to a machine learning model. And then we will talk about features and feature engineering and how deep learning is useful for feature engineering. Then we're going to talk about where you can find it. First we are going to introduce what tabular data is. In this video, we're going to talk about tabular data. The Github repo for this course, including code and datasets, can be found here. It would be recommended to complete the Introduction to Data and Machine Learning course, before starting.Be able to explain the importance of deep learning.Learn and understand the functions of machine learning when confronted with structured and unstructured data.This course is part of the Data and Machine Learning learning paths from Cloud Academy. This course is made up of 8 lectures, accompanied by 5 engaging exercises along with their solutions. With many different data types, learn about its different formats, and we'll analyze the vital libraries that allow us to explore and organize data. Learn and study how to explain the reasons deep learning is so popular. Deep learning works very well with both structured and unstructured data, and it has had successes in fields like translation, and image classification, and many others. Traditionally, machine learning has worked really well with structured data but is not as efficient in solving problems with unstructured data. Learn the ways in which data comes in many forms and formats with the second course in the Data and Machine Learning series.
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