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

What is the output of a **sigmoid** function?


What is the output of a sigmoid function?

What is the shape of the decision boundary drawn by a perceptron?

What is the shape of the decision boundary drawn by a perceptron?

Perceptron with a sigmoid activation function

What does a **vanilla perceptron** refer to?




What does a vanilla perceptron refer to?

Given an input matrix with dimensions $(m,n)$, and an output matrix with dimensions $(m,k)$, what should be the dimensions of the weight matrix?


Given an input matrix with dimensions <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>m</mi><mo separator="true">,</mo><mi>n</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(m,n)</annotation></semantics></math>(m,n), and an output matrix with dimensions <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>m</mi><mo separator="true">,</mo><mi>k</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(m,k)</annotation></semantics></math>(m,k), what should be the dimensions of the weight matrix?

What would be the output of a *WSS* (_weighted sum plus sigmoid_)?

What would be the output of a WSS (weighted sum plus sigmoid)?

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: The Perceptrons