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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

Part III of this course has been a build-up to a revolution. In this last chapter, we’ll finally examine the breakthrough known as deep learning.

 The story of deep learning began form 
 [The Perceptron](https://www.educative.io/collection/page/10370001/5351678229872640/6338454874488832): _the clash between connectionists and symbolists_, and how the connectionists were discredited and pushed into the basements of research.
In the 1970s, _ML researchers discovered how to train neural networks with backpropagation_. After years of struggle, they could finally overcome the limitations of perceptrons and tackle nonlinear data. For a while, it looked like connectionism was back—but as it happened, that new spark was also bound to fizzle out.

This chapter continues the story till _the birth of deep learning_. It also explains what makes deep learning so effective and improves upon earlier methods to the best of what we know today. Finally, the last lessons will point at a few trails we can walk after closing this course to delve deeper into this brave new world.

The Rise of Deep Learning

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

The Rise of Deep Learning