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Machine learning is the future for the next generation of software professionals.

This course serves as a guide to machine learning for software engineers. You’ll be introduced to three of the most relevant components of the AI/ML discipline; supervised learning, neural networks, and deep learning. You’ll grasp the differences between traditional programming and machine learning by hands-on development in supervised learning before building out complex distributed applications with neural networks. You’ll go even further by layering networks to create deep learning systems. You’ll work with complex real-world datasets to explore machine behavior from scratch at each phase.

By the end of this course, you’ll have a working knowledge of modern machine learning techniques. Using software engineering, you’ll be prepared to build complex neural networks and wrangle real-world data challenges.

Fundamentals of Machine Learning for Software Engineers

Backpropagation is an application of the **chain rule**, it is _one of the fundamental rules of calculus_.  The chain rule calculates the gradient of any node y with respect to any other node $x$, we multiply the local gradient of all the nodes on the way back from $y$ to $x$. 

Let’s understand how the chain rule works on a couple of network-like structures: a simple one, and a more complicated one.


## The chain rule on a simple network

Let’s look at this simple network-like structure:


Backpropagation is an application of the **chain rule**, it is _one of the fundamental rules of calculus_.  The chain rule calculates the gradient of any node y with respect to any other node $x$, we multiply the local gradient of all the nodes on the way back from $y$ to $x$. 

Let’s understand how the chain rule works on a couple of network-like structures: a simple one, and a more complicated one.


# The chain rule on a simple network

Let’s look at this simple network-like structure:


Learn how to apply chain rule in backpropagation.

How Machine Learning Works

Our First Learning Program

Walking the Gradient

Hyperspace

A Discern Machine

Get Real

The Final Challenge

The Perceptron

Designing the Network

Building the Network

Training the Network

How Classifiers Work

Batchin’ Up

The Zen of Testing

Let’s Do Development

A Deeper Kind of Network

Defeating Overfitting

Taming Deep Networks

Beyond Vanilla Networks

Into the Deep

From the Chain Rule to Backpropagation

The chain rule on a simple network