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

# Goal of the lesson
In the previous lessons, we built a binary classifier. Now we want to apply that program to MNIST.

The first step is to reshape MNIST’s images and labels into an input for our program. Let’s see how to do that.

# Prepare the input matrices

Our binary classifier program expects its input formatted as two matrices: a set of examples $X$ and a set of labels $Y$. Let’s start with the matrix of examples $X$.

$X$ is supposed to have one line per example and one column per input variable, plus a bias column full of **1s**. (Remember the bias column? We talked about it in the [Bye-bye, bias](https://www.educative.io/courses/fundamentals-of-machine-learning-for-software-engineers/put-it-all-together#Bye-bye,-bias) section)

To fit MNIST’s images to this format, we can reshape each image to a single line of pixels so that each pixel becomes an input variable. MNIST images are 28 by 28 pixels, so squashing them results in lines of 784 elements. And throw in the bias column, and that makes 785. So, X should look like a matrix of 60,000 lines (the number of examples) and 785 columns (one per pixel, plus the bias). Check out the following graph:

Interact with the sample code to create a custom MNIST library.

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

Our Own MNIST Library

Goal of the lesson

Prepare the input matrices