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

# Updating the loss function
Now that we have a brand-new loss function, let’s look up its gradient. Here is the partial derivative of the log loss with respect to the weight from the math textbooks:

$$\frac{\delta L}{\delta w} = \frac{1}{m} \sum_{i=1}^{m} x_i (\hat{y_i} - y_i)$$

This gradient might look familiar. In fact, it closely resembles the gradient of the mean squared error that we have used so far:

$$\frac{\delta MSE}{\delta w} = \frac{2}{m}\sum_{i=1}^{m}x(\hat{y_i}-y_i)$$

See how similar they are? This means that we can take our previous `gradient()` function:

Apply what we have learned about sigmoids by updating the loss function.

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

Update the Gradient

Updating the loss function