Machine Learning vs Deep Learning: What’s the Difference?

Introduction

Machine learning and deep learning are often used interchangeably, but they have distinct differences. This article explores how they work and their key differences.

Understanding the Concepts

1. Machine Learning

• Uses algorithms to find patterns and make predictions from data.
• Requires human intervention for feature selection and tuning.

2. Deep Learning

• A subset of machine learning that uses neural networks to process complex data.
• Works with large datasets and learns features automatically.

Key Differences

• Data Requirement: Deep learning requires more data than traditional machine learning.
• Processing Power: Deep learning needs more computational resources.
• Feature Engineering: Machine learning requires manual feature extraction, while deep learning automates it.

Conclusion

Both machine learning and deep learning have their advantages. Machine learning is efficient for structured data, while deep learning excels in handling unstructured data like images and speech.