The course will help you understand machine learning fundamentals by diving into theoretical concepts and algorithms of machine learning.

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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

Perceptrons are simple, and they can be assembled into larger structures like machine learning construction bricks. However, that simplicity comes with a distressing limitation that perceptrons work well on some datasets and fail badly on others. More specifically, perceptrons are a good fit for **linearly separable data**. Let’s see what linearly separable means, and why it matters.

## Linearly separable data

Let's look at this two-dimensional dataset:

Perceptrons are simple, and they can be assembled into larger structures like machine learning construction bricks. However, that simplicity comes with a distressing limitation that perceptrons work well on some datasets and fail badly on others. More specifically, perceptrons are a good fit for **linearly separable data**. Let’s see what linearly separable means, and why it matters.

# Linearly separable data

Let's look at this two-dimensional dataset:

Distinguish between linearly separable data and non-linearly separable data.

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

Where Perceptrons Fail

Linearly separable data