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

## Background
Let’s look for a better `train()` algorithm. The job of `train()` is to find the parameters that minimize the loss, so let’s start by focusing on `loss()` itself:

```python
def loss(X, Y, w, b):
  return np.average((predict(X, w, b) - Y) ** 2)
```
Look at this function’s arguments. The $X$ and $Y$ contain the input variables and the labels, so they never change from one call of `loss()` to the next. To make the discussion simple, let’s also temporarily fix $b$ at $0$. So now the only variable is $w$.

How does the loss change as w changes? We put together a program that plots loss()` for w ranging from $-1$ to $4$, and draws a green cross on its minimum value. Let’s look at the following graph:

# Background
Let’s look for a better `train()` algorithm. The job of `train()` is to find the parameters that minimize the loss, so let’s start by focusing on `loss()` itself:

```python
def loss(X, Y, w, b):
  return np.average((predict(X, w, b) - Y) ** 2)
```
Look at this function’s arguments. The $X$ and $Y$ contain the input variables and the labels, so they never change from one call of `loss()` to the next. To make the discussion simple, let’s also temporarily fix $b$ at $0$. So now the only variable is $w$.

How does the loss change as w changes? We put together a program that plots loss()` for w ranging from $-1$ to $4$, and draws a green cross on its minimum value. Let’s look at the following graph:

Discover the math behind gradient descent to deepen our understanding by exploring graphical representations.

How Machine Learning Works

Our First Learning Program

Walking the Gradient

Hyperspace

A Discern Machine

Get Real

The Final Challenge

Conclusion

Gradient Descent

Background