2. Data Acquisition

Collecting the required data means acquiring the data in a visual form. This data now becomes the base of your project/system. This is known as Data Acquisition. Remember that the data needs to be accurate and reliable as it ensures the efficiency of your project.

Data Acquisition is the process of collecting the required data from reliable sources for the project.

Data can be a piece of information or facts and statistics collected together for reference or analysis. Whenever we want an AI project to be able to predict an output, we need to train it first using data.
For example : If you want to make an Artificially Intelligent system which can predict the weather, you would feed the data of previous days for that locations into the machine. This is the data with which the machine can be trained. Now, once it is ready, it will predict the weather conditions efficiently.

Training Data & Testing Data

The previous/historic data used to train the AI project is known as "Training Data". And the predicted/resultant data set given by AI project that to be evaluate is known as the "Testing Data".

Note*: For better efficiency of an AI project, the Training data should be authentic and relevant to the problem statement scoped.

Data Features

Data Features refer to the type of data you want to collect.
For example : If an AI machine predict the salary of any employee, data features would be salary amount, increment percentage, increment period, bonus, etc.

Various Data Sources used to aquired reliable data

After mentioning the Data features, the question arises that "from where can we get this data?". There can be various ways in which you can collect data. Some of them are surveys, Web Scraping, Sensors, cameras, Observations, APIs (Application Program Interface) etc.