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

Look at the chart we just plotted. Is our line not quite the best approximation of the examples? The perfect line would have less slope and not pass by the origin. Give or take, it would cross the Pizzas axis around the value of 10.


# The bias factor

So far, we forced the line to pass by the origin to keep our model as simple as possible. It’s time to remove that constraint. To draw a line that is not constrained to pass by the origin, we need one more parameter in our model:

$$\large{\hat{y} = x * w + b}$$

This equation is the classic linear function that we might have studied in school. Most people would remember it as:

$$\large{y = m * x + b}$$

Where $m$ is called the slope and $b$ is called the y-intercept. Here, we’ll use the vocabulary of ML instead. We already called $w$ the weight, and we’ll call $b$ the bias.


Intuitively, the bias measures the shift of the line up or down the chart, as shown in the following graph. The line crosses the y-axis at a value equal to $b$. If the bias happens to be $0$, then we are back at our previous case, with a line passing by the origin.


Learn to add a bias to the previously built regressor.

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

Add a Bias

The bias factor