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

# Revision
Before we move on, let’s practice with the code for a little while. That’s optional, but it’s a good way to revise these concepts.

As we did cover perceptron and neural networks in this chapter, therefore, we’ll interact with both separately.
## Perceptron
We’ll start with perceptron, let’s test our app given below:

## Neural network
Let’s experience neural network’s behavior by testing the given app:

# Hands-on challenge
We can find an additional dataset named `circles.txt` in the below code widget. Edit the `plot_data.py` file so it loads this dataset and runs it to take a look at the data. Spoiler alert: `circles.txt` _is about as nonlinearly separable as they get_.

Edit `plot_perceptron_boundary.py` to load `circles.txt` and run it. What does the perceptron’s boundary look like? Do the same with `plot_neural_network_boundary.py` and `plot_neural_network_model.py`. How does the neural network fare on these data?

_**Note**: Try different numbers of iterations to observe the change in decision boundary, for example, starting from 1000 going up to 50,000 (as long as the terminal time out permits)._ 

>How to run 
#concept=code_widget#code_widget#concept#. 
>
>Moreover, you can copy any of the `plot_***.py` to `main.py` and observe the outcome by #key# varying the dataset: tweaking the highlighted lines from either of the plot_***.py files #key#.

Compare the decision boundaries created by perceptrons and neural networks by testing the codes given for various types of 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

Diabetes Prediction Using Keras

Defeating Overfitting

Taming Deep Networks

Beyond Vanilla Networks

Into the Deep

Recognize Handwritten Digits Using a Deep Neural Network

Playground

Revision

Perceptron