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

## Implementation of neural network using Keras

Let’s move on to the code as we have discussed each component in detail already. Here is the whole thing.

To save time, a decision boundary is created after 1000 `epochs`. However to improve the decision boundary, `epochs` must be increased as shown after the code widget for 30,000 `epochs`.

Please wait for 1 minute after the training phase completes and then click the app link to preview the decision boundary:


# Implementation of neural network using Keras

Let’s move on to the code as we have discussed each component in detail already. Here is the whole thing.

To save time, a decision boundary is created after 1000 `epochs`. However to improve the decision boundary, `epochs` must be increased as shown after the code widget for 30,000 `epochs`.

Please wait for 1 minute after the training phase completes and then click the app link to preview the decision boundary:


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

Keras in Action

Implementation of neural network using Keras