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

## How is that possible?!
Machine learning can seem like magic. How can a computer recognize the objects in an image? How can a car drive itself?

Those feats are baffling, not just to the layman, but to many software developers. Even after several years of writing code, many developers are not well versed with machine learning.

To most developers, machine learning feels foreign. The field is teeming with math jargon, research conventions, and frankly, bad code. Instead of tutorials, people recommend consulting lectures and research papers. For many of us, machine learning is as intimidating as it is intriguing.

This course is an introduction for developers, written in their own language. After taking this course, you’ll be comfortable with the fundamentals and will be able to write machine learning programs. You probably would not be able to build your own self-driving car just yet, but at least you’ll know how machine learning works.

## About this course

Machine learning is a broad field, and one course cannot cover it all. We’ll focus on the three facets of machine learning that are most important today: 

1. Supervised learning 
2. Neural networks 
3. Deep learning 

We’ll look into those terms as we go through the course, but here’s a picture and a few quick definitions to get started:


# How is that possible?!
Machine learning can seem like magic. How can a computer recognize the objects in an image? How can a car drive itself?

Those feats are baffling, not just to the layman, but to many software developers. Even after several years of writing code, many developers are not well versed with machine learning.

To most developers, machine learning feels foreign. The field is teeming with math jargon, research conventions, and frankly, bad code. Instead of tutorials, people recommend consulting lectures and research papers. For many of us, machine learning is as intimidating as it is intriguing.

This course is an introduction for developers, written in their own language. After taking this course, you’ll be comfortable with the fundamentals and will be able to write machine learning programs. You probably would not be able to build your own self-driving car just yet, but at least you’ll know how machine learning works.

# About this course

Machine learning is a broad field, and one course cannot cover it all. We’ll focus on the three facets of machine learning that are most important today: 

1. Supervised learning 
2. Neural networks 
3. Deep learning 

We’ll look into those terms as we go through the course, but here’s a picture and a few quick definitions to get started:


Get introduced to machine learning and discover its key concepts.

How Machine Learning Works

Our First Learning Program

Walking the Gradient

Hyperspace

A Discern Machine

Get Real

The Final Challenge

Conclusion

Introduction

How is that possible?!