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

# Revision
Before we move on, let’s practice with the code for a little while. That’s optional, but it’s a good way to revise these concepts.

Go through all the codes which we have covered in this chapter by launching the below app:

And now, prepare for a challenge. 

# Hands on
Let’s talk about the learning rate again. In the final example of this chapter, the learning rate was set to $0.001$. Try increasing the learning rate, and we might notice that the `loss` starts to increase at some point instead of decreasing. Why might this be?

If the picture doesn’t seem clear, try drawing the `loss` function on paper. What happens with a very large learning rate?

Practice the concepts you learned in the chapter and discover the effect of learning rate while training the classifier.

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

Playground (Basecamp Overshooting)

Revision

Hands on