Learn ML Datasets

Discover the essential datasets powering machine learning — from classic benchmarks like MNIST and Iris to modern NLP, computer vision, and tabular datasets. Learn how to find, load, create, and manage datasets for your ML projects.

Start Course → View All Lessons
8
Lessons
100+
Datasets Covered
~3hr
Total Time
100%
Free

What You'll Learn

A complete guide to datasets for every machine learning task.

📊

Classic Benchmarks

Master the foundational datasets every ML practitioner should know: Iris, MNIST, CIFAR, Titanic, and more.

🔍

Dataset Discovery

Find the right dataset from Hugging Face, Kaggle, UCI, Google Dataset Search, and other major sources.

🛠

Creating Datasets

Build your own datasets with data collection, annotation tools, synthetic data generation, and quality control.

✅

Best Practices

Handle imbalanced data, prevent leakage, version datasets with DVC, and ensure ethical data use.

Course Lessons

Follow the lessons in order or jump to any topic you need.

Beginner

1. Introduction

Why datasets matter, types of datasets, train/val/test splits, data leakage, bias, and licensing.

10 min read →
 Beginner

2. Classic Datasets

Iris, MNIST, CIFAR-10, Titanic, Boston Housing, and other foundational datasets with code to load each.

15 min read →
 Intermediate

3. Computer Vision Datasets

ImageNet, COCO, Pascal VOC, CelebA, Cityscapes, KITTI, and more for every vision task.

15 min read →
 Intermediate

4. NLP Datasets

GLUE, SQuAD, IMDB, AG News, CoNLL, WMT, MMLU, HumanEval, and other language benchmarks.

15 min read →
 Intermediate

5. Tabular Datasets

UCI datasets, Kaggle favorites, government open data, financial, and healthcare datasets.

12 min read →
 Intermediate

6. Dataset Sources

Where to find datasets: Hugging Face, Kaggle, UCI, Google Dataset Search, AWS, and more.

12 min read →
 Advanced

7. Creating Datasets

Data collection, annotation tools, crowdsourcing, synthetic data, augmentation, and publishing.

15 min read →
 Advanced

8. Best Practices

Handling imbalanced data, cross-validation, versioning with DVC, bias mitigation, and documentation.

10 min read →

Prerequisites

What you need before starting this course.

Before You Begin:
  • Basic Python programming
  • Familiarity with pandas DataFrames (helpful)
  • Understanding of basic ML concepts (training, testing, evaluation)
  • No prior dataset experience needed