Explore machine learning's essentials for software engineers, delve into supervised learning, neural networks, and deep learning, and gain skills to tackle real-world data challenges effectively.

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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.

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

Diabetes Prediction Using Keras

Defeating Overfitting

Taming Deep Networks

Beyond Vanilla Networks

Into the Deep

Recognize Handwritten Digits Using a Deep Neural Network

The Rise of Deep Learning