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
A CNN has a lot of hyperparameters  On top of the usual hyperparameters of fully connected layers, we also have to pick the number and size of filters in each layer. Moreover,   we also have to consider the additional parameters as mentioned in ["Convolutions Can Get Complicated"](https://www.educative.io/courses/fundamentals-of-machine-learning-for-software-engineers/the-building-blocks-of-cnns#Convolutions).

Our mission is to experiment with the hyperparameters of the CNN we built in this chapter. Tune those hyperparameters that we already know about, and maybe search online for the meaning of those we skipped. We probably would not be able to do very fine hyperparameter tuning because that would require training the CNN for a long time. However, we'll understand those options a little bit better and maybe get a sense of how they impact the network’s loss, accuracy, and training speed. Also, investigate how those hyperparameters impact the shape of a convolution’s output.

This is the last “Hands-on” section in this course. It does not set a specific goal. Deep learning is still a brave new world for people who like experimenting. This Hands-on asks you to start experimenting yourself.
This experiment is also a chance to explore the material that’s available on the internet so that you’ll be fully prepared when you finish this course and embark on a path to deep learning mastery. There’s a lot of information out there, so take it easy, and have fun exploring!


Test and create a convolutional neural network created using Keras on CIFAR-10.

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 (Hyperparameters Galore)

Revision

Hands-on