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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 the components that directly impact the value of a particular node.

Weights and the activation function operate between the layers.

Where do we use the **softmax** activation function in the network?


Where do we use the softmax activation function in the network?

Which of the following is/are the possible output of a **softmax** function?

Which of the following is/are the possible output of a softmax function?

If the hidden layers give the following three logits as output:

$(1.5, 3, 0.5)$

How likely is the **softmax** output to be the 2nd class?


If the hidden layers give the following three logits as output:
<math><semantics><mrow><mo>(</mo><mn>1</mn><mi mathvariant="normal">.</mi><mn>5</mn><mo separator="true">,</mo><mn>3</mn><mo separator="true">,</mo><mn>0</mn><mi mathvariant="normal">.</mi><mn>5</mn><mo>)</mo></mrow><annotation encoding="application/x-tex">(1.5, 3, 0.5)</annotation></semantics></math>(1.5,3,0.5)
How likely is the softmax output to be the 2nd class?

By computing the gradients of input with respect to output at each stage.


By computing the gradients of outputs with respect to inputs from the end and go back to the beginning, and multiple them all.


By computing the gradients of outputs with respect to inputs from the end and go back to the beginning, and sum them all.

In backpropagation, how do we calculate the gradient of the network?

In backpropagation, how do we calculate the gradient of the network?

Test your knowledge regarding building and training neural networks by attempting the given quiz.


Quiz: Design, Build and Train the Network

Test your knowledge regarding building and training neural networks by attempting the given quiz.

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: Design, Build and Train the Network