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

All nodes in the layer are connected to the next layer.

All nodes in the layer are connected to the previous layer.


More than 50% of the nodes in the layer are connected to the next layer.

More than 50% of the nodes in the layer are connected to the previous layer.

What is a dense layer in a neural network?

What is a dense layer in a neural network?

Both number of nodes and activation function

What input arguments do we need to provide to create a dense layer in Keras?


What input arguments do we need to provide to create a dense layer in Keras?

Where should the **activation function** be mentioned in the network?


Where should the activation function be mentioned in the network?

In Keras, what does the term “fitting” mean?


In Keras, what does the term “fitting” mean?

Each layer should have the same number of nodes, weights, and activation function.

Each layer should have the same activation function.

Each layer may have its own activation function, weights and number of nodes.

In a deep network design, what can we say about the parameter's constraints on each layer?


In a deep network design, what can we say about the parameter’s constraints on each layer?

Test your knowledge on deep neural networks using Keras.

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

Quiz: A Deeper Kind of Network