Get started with the core components of machine learning and learn to create supervised learning systems.

Zero to Hero in Python for Machine Learning

This module is a comprehensive introduction to the core components of machine learning for software engineers. We’ll be introduced to the most relevant components of the machine learning discipline. We’ll also cover the differences between traditional programming and machine learning through hands-on development in supervised learning. By the end of this module, we’ll have a working knowledge of modern techniques in the field of machine learning.

Fundamentals of Machine Learning

## Theory
Remember how we encoded labels while [Preparing the input matrices](https://www.educative.io/courses/fundamentals-of-machine-learning-for-software-engineers/our-own-mnist-library#Preparing-the-input-matrices)? Back then, we only recognized $5$ from other digits. So we encoded the labels by replacing $5$ with $1$, and other digits with $0$:

# Theory
Remember how we encoded labels while [Preparing the input matrices](https://www.educative.io/courses/fundamentals-of-machine-learning-for-software-engineers/our-own-mnist-library#Preparing-the-input-matrices)? Back then, we only recognized $5$ from other digits. So we encoded the labels by replacing $5$ with $1$, and other digits with $0$:

Learn about one-hot encoding, both theoretically and programmatically.

How Machine Learning Works

Our First Learning Program

Walking the Gradient

Hyperspace

A Discern Machine

Get Real

The Final Challenge

Conclusion

One Hot Encoding

Theory