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

## Revision
Before we move on, let’s practice with the code for a little while. That’s optional, but it’s a good way to revise these concepts.

Go through all the codes which we have covered in this chapter by launching the below app.


## Hands-on

Here’s an exercise: Modify the MNIST classifier to run on the sonar dataset. The **sonar dataset** (also known as the “mines vs rocks” dataset) contains the patterns generated by bouncing sonar signals off two different types of objects: _metal cylinders_, (which could potentially be mines), and _rocks_. Let;s see if we can train the classifier to differentiate  mines from rocks.



# Revision
Before we move on, let’s practice with the code for a little while. That’s optional, but it’s a good way to revise these concepts.

Go through all the codes which we have covered in this chapter by launching the below app.


# Hands-on

Here’s an exercise: Modify the MNIST classifier to run on the sonar dataset. The **sonar dataset** (also known as the “mines vs rocks” dataset) contains the patterns generated by bouncing sonar signals off two different types of objects: _metal cylinders_, (which could potentially be mines), and _rocks_. Let;s see if we can train the classifier to differentiate  mines from rocks.



Test our training algorithm in the sonar dataset and compare its accuracy with humans.


Playground (Minesweeper)

Test our training algorithm in the sonar dataset and compare its accuracy with humans.

How Machine Learning Works

Our First Learning Program

Walking the Gradient

Hyperspace

A Discern Machine

Get Real

The Final Challenge

Conclusion

Playground (Minesweeper)

Revision

Hands-on