Math is the language of science used to discuss concepts efficiently. It underlies all science fields, from physics to computer science to biology. As AI and ML are within computer science, it should be no surprise that it too relies tremendously upon math.
Mathematics is at the core of AI and Machine Learning because it provides means of implementing how their goals can be reached.
Artificial intelligence heavily relies on smart algorithms, which again depends on many branches of Mathematics.
Data processing: Data processing transforms raw data into meaningful information. Data processing services require skilled professionals to apply different techniques for analyzing and processing data. For every business organization, data has become the most important tool to make critical decisions. Technology intervention has helped to increase the credibility of data analysis. Organizations slowly realize how data can help better the latest technologies.
Newer technologies such as Machine Learning are heavily dependent upon bulk data. As data is the core of these technologies, it has to be presented in a way or format which these technologies understand. If not provided in the correct format, the algorithms would provide incorrect analysis, and data will not be comprehended correctly.
The organization needs to understand the concept of analysis and focus time on data collection, cleaning, exploring, and changing it in the needed format. For the benefit of people working on Artificial Intelligence, ML, and other advanced technologies, a hierarchy needs to be followed in the proper data processing.
Basic Steps in Data Processing
- Data Collection: This is the first step where it requires the collection of data for building the right data set for the artificial intelligence or machine learning algorithm. Examples could be all users logging in to the website, and their details or could be some sensor in a unit through which data is being fed.
- Data Transformation: This step tries to establish a relationship between the variables and the value obtained and accordingly drop the insignificant ones. It is an important step to make the database strong.
- Data Analysis: This requires an in-depth understanding of data such as type, value, or any missing data required to work on the data.
- Data Training: After this step, the analytics can be started, which is the basis of artificial intelligence; their data is organized with labels.
- Experimentation: For the algorithms, the experimentation is done to remove any potential problems which might come due to an inexact idea about the results and the changes made.
Now once the data is cleaned and organized in the required manner, then correct things can be measured with data sets. AI can work, and users and companies can both gain benefit from this as they will learn new methods and experience hands-on AI algorithms.
This order results in data pipe-lining, which is the main feature of data analysis using advanced technologies. To work on such analytics more efficiently, the company can outsource data processing techniques, which would help in a mature ML/AI model development.
From the new age perspective of changes in the business of digital technologies, the developers and business people are attempting to be at par with the ML/AI process. Sometimes in a hurry to implement, they do not follow every required step of data processing. They end up missing one or two steps and sooner or later end up creating imperfect models. So, it is important to keep in mind the data processing steps in ML/AI.