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 the code for a little while. That’s optional, but it’s a good way to revise these concepts.

Go through all the codes which we have covered in this chapter by launching the below app:


# Hands on
In this chapter, we applied L1 regularization to reduce overfitting on our four-layers neural network. Now it’s up to us to try a few other regularization
techniques. 
* How does early stopping work on this network? 
* What about
removing a few nodes from each layer?

Try out those techniques,  and keep an eye on the accuracy of the validation set. Maybe we’ll find a more accurate result than we did in this chapter. Do not worry if we do not! The point of this exercise is experimenting with regularization, not necessarily exceeding that 92% score.

Once we optimize the network’s accuracy on the validation set, there is one last thing that we can take care of. Do you remember what we talked about in 
 [A Testing Conundrum](https://www.educative.io/courses/fundamentals-of-machine-learning-for-software-engineers/the-development-cycle-of-neural-networks)? By improving the network’s performance on the validation set, we run the risk of overfitting the validation set. There is only one way to find out this. We _recover the Echidna test set that we have been ignoring until now and run a final test_.

Check out the network’s accuracy on the test set. Is it as good as the best accuracy we got on the validation set?




Apply regularization techniques to improve neural network performance.

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 (Keeping it simple)

Revision

Hands on