4.2.5 Database and Machine Learning

Introduction

A database is a collection of related data stored in the form of tables. Various computer software is used to create databases to store data in an organized form so that it can be managed, retrieved, and stored efficiently. Traditionally, databases have a key role in the development of modern technology of computer science. Oracle, MS-Access, and MySQL are some well-known database management software used to create and manage databases.

Definition

Database: A collection of related data stored in the form of tables, managed by a Database Management System (DBMS).

Machine Learning: A subset of Artificial Intelligence that enables computers to learn from data without being explicitly programmed.

Main Idea

Apparently, database and machine learning seem to be very distinct disciplines of the computer science domain. However, they are interrelated closely. To understand the relationship between these two disciplines, the simplified information is given as follows:

Core Concept

Detailed Explanation

Data Storage

In a database, data is stored in a structured format such as a table. Machine learning algorithms require the same structured data to train their models. Datasets are organized in rows and columns.

Example: A database might store customer information in a table with columns like Name, Age, and Purchase History. Machine learning uses this same structured data to predict future purchases.

Data Preprocessing

A database stores data after normalization; therefore, data is clean without any duplication, data redundancy, and other errors. Machine learning also requires clean data. If the data is not clean, the performance/result of the algorithm is affected.

Example: If a database has duplicate customer records, machine learning models trained on this data may produce inaccurate predictions.

Data Retrieval

As data is stored in the tables in an organized manner, the retrieval of data through SQL queries is very easy, fast, and reliable. Data is organized in the form of a Dataset; therefore, it can be retrieved by using simple and easy algorithms of machine learning.

Example: SQL queries can quickly retrieve all customers who made a purchase in the last month. Machine learning algorithms can then analyze this data.

Data Integration

Data in a database is kept integrated. It is stored after removing duplicated values and other possible errors. Therefore, integrity of data in the database is assured. For comprehensive model training, integrated data is required. In this way, data provided by the database is equally useful for machine learning to train its algorithms.

Example: A database ensures that each customer has a unique ID, preventing duplication. Machine learning models rely on this integrity for accurate training.

Scalability

DBMS can handle various sizes of databases ranging from a student database of 50 students to the database of an entire country or even more. Machine learning requires scalable data storage solutions. In this way, it can implement big data applications. Number of records in a dataset varies from hundreds to thousands and even in millions in a single dataset.

Example: A database can store millions of records. Machine learning can process these large datasets to find patterns.

Real-time Data Processing

Database provides real-time data storage and retrieval support. Machine learning model requires real-time data to train the model for online data.

Example: A database can store real-time stock prices. Machine learning models can use this real-time data to make trading decisions.

Analytical Tools

Database provides various tools for data analysis, e.g., SQL Server and other software, etc. Database analytical tools are helpful for machine learning to provide fast and accurate solutions.

Example: SQL Server's analytics tools can preprocess data before it is fed to a machine learning model.

Intermediate Level

At the intermediate level, you should understand how databases and machine learning work together.

Workflow:

• Data Collection: Data is collected and stored in a database.
• Data Cleaning: The database ensures data is clean and normalized.
• Data Retrieval: SQL queries retrieve relevant data.
• Data Preparation: Data is organized into a dataset.
• Model Training: Machine learning algorithms train on the dataset.
• Prediction: The trained model makes predictions.
• Storage: Predictions and results can be stored back in the database.

Key point: Databases provide the data; machine learning provides the insights.

Important Points

• - Database stores data in structured tables.
• - Machine learning requires structured, clean data.
• - Database provides data retrieval through SQL.
• - Machine learning uses datasets for training.
• - Database ensures data integrity.
• - Machine learning requires integrated data.
• - DBMS handles scalable databases.
• - Machine learning requires scalable data storage.
• - Database supports real-time data.
• - Machine learning needs real-time data for online models.
• - Database provides analytical tools.
• - Machine learning benefits from these tools.

Remember

• - Database = stores data.
• - Machine Learning = learns from data.
• - Database = structured tables.
• - ML = datasets (rows and columns).
• - Database = clean data (normalization).
• - ML = requires clean data.
• - Database = SQL retrieval.
• - ML = dataset retrieval.
• - Database = integrated data.
• - ML = requires integrated data.
• - Database = scalable.
• - ML = requires scalable storage.
• - Database = real-time support.
• - ML = needs real-time data.
• - Database = analytical tools.
• - ML = benefits from tools.

Common Mistakes

• - Thinking that databases and machine learning are unrelated (they are closely related).
• - Assuming that machine learning can work without clean data.
• - Forgetting that databases provide the data that machine learning needs.
• - Thinking that machine learning replaces databases (they complement each other).
• - Ignoring the importance of data integrity for ML.
• - Assuming that all databases are the same (different DBMS have different features).
• - Not understanding that ML models need to be updated as data changes.

Quick Revision

• Database and machine learning are closely related.
• Database stores structured data in tables.
• Machine learning requires structured, clean data.
• Database provides data retrieval through SQL.
• Machine learning uses datasets for training.
• Database ensures data integrity.
• Machine learning requires integrated data.
• DBMS handles scalable databases.
• Machine learning requires scalable data storage.
• Database supports real-time data.
• Machine learning needs real-time data for online models.
• Database provides analytical tools.
• Machine learning benefits from these tools.
• Examples: Oracle, MS-Access, MySQL.

60-Second Revision

• Database = stores data; ML = learns from data.
• Database = structured; ML = datasets.
• Database = clean; ML = requires clean.
• Database = SQL; ML = algorithms.
• Database = integrated; ML = requires integrated.
• Database = scalable; ML = scalable storage.
• Database = real-time; ML = real-time data.
• Database = tools; ML = benefits.
• Warning: Machine learning cannot work well without clean, integrated data from a database.