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.


# The problem statement
Our friend owns a cozy little pizzeria in a busy metropolitan city. Every day at noon, they check the number of reserved seats and decide how much pizza dough to prepare for dinner. Too much dough, and it goes wasted, but too little, and they run out of pizzas. In either case, the restaurant loses money.

It’s not always easy to gauge the number of pizzas from the reservations. Many customers don’t reserve a table, or they eat something other than pizza. The owner knows that there is some kind of link between those numbers, in that more reservations generally mean more pizzas, but other than that, it’s not clear what the exact relation is.

The restaurant owner wants a program that looks at historical data, grasps the relation between reserved seats and pizzas and uses it to forecast tonight’s pizza sales from today’s reservations. Can we code such a program for them?

# Supervised pizza

Remember what we learned back in [Supervised Learning’s lesson?](https://www.educative.io/courses/fundamentals-of-machine-learning-for-software-engineers/supervised-learning) We can solve the pizza forecasting problem by training a supervised learning algorithm with a bunch of labeled examples. To get the examples, we ask the restaurant owner to jot down a few days’ worth of reservations and pizzas and collect those data in a file. Here’s what the first four lines of that file look like:

| Reservations  | Pizzas  |
| - | - |
|  13 | 33  |
| 2  |  16 |
| 14  |  32 |
| 23  |  51 |



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

The problem statement

Supervised pizza