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

Welcome to our first stepping stone in machine learning. In this chapter, we’ll build a tiny supervised learning program. It will be a long haul from this program to our goal of image recognition. In fact, at first, it would not have anything to do with computer vision at all. However, we’ll improve its code over the next chapters until it gets sophisticated enough to tackle images.

We’ll base the first version of our program on a technique called linear regression. Remember from the previous chapter? Supervised learning is about approximating data with a function. In linear regression, that function is as simple as it could be, a straight line.

This chapter will touch on many different concepts to introduce linear regression. There’s a chance that we might feel slightly overwhelmed. Take it slow and easy. Even if it looks like we are just writing a short program, we are actually laying the foundation of our ML knowledge. What we learn here will stay useful throughout this chapter and beyond. Let’s start with a practical problem.


Explore supervised learning by solving a real-life problem and mapping the data onto 2D-graph.

How Machine Learning Works

Our First Learning Program

Walking the Gradient

Hyperspace

A Discern Machine

Get Real

The Final Challenge

Conclusion

Get to Know the Problem