Datasets in AI/ML
Data is the foundation of all AI and machine learning models. Without quality datasets, even the most advanced algorithms can't learn. This page gives you a simple introduction and a real dataset to download and explore.
📊 What is a Dataset?
A dataset is a collection of data, usually organized in a table with rows and columns. Each row is an instance (e.g., a person, a transaction), and each column is a feature (e.g., age, salary).
Structured Data
Tabular formats like CSV, Excel, SQL tables. Each column has a fixed data type.
Unstructured Data
Images, text, audio, video – not in rows/columns, but still useful with special techniques.
Labelled vs Unlabelled
Supervised learning needs labelled data; unsupervised learning finds patterns without labels.
🧠 Why Datasets Are Crucial in AI/ML
Machine learning models learn patterns from data. The quality and quantity of your dataset directly affect the model's performance.
- Training: The model adjusts its internal parameters based on the training dataset.
- Validation: A separate set to tune hyperparameters and avoid overfitting.
- Testing: The final evaluation on completely unseen data.
A common dataset split is 70% training, 15% validation, 15% test.
📥 Download the Salary Dataset
This simple CSV file contains Years of Experience and Salary – perfect for beginner regression tasks.
💡 How to use it?
- Open in Excel / Google Sheets for a quick view.
- Load with Python: import pandas as pd; df = pd.read_csv('Salary_dataset.csv')
- Practice simple linear regression: predict salary from years of experience.
🔍 What Can You Do With This Dataset?
- Plot YearsExperience vs Salary (scatter plot)
- Calculate mean, median, standard deviation
- Build a Linear Regression model
- Evaluate using R² and MSE
- Try polynomial regression for better fit
This is the same dataset often used in introductory ML courses. Master it, and you’ll be ready for more complex data.