Programming Machine Learning »

You've decided to tackle machine learning — because you're job hunting, embarking on a new project, or just think self-driving cars are cool. It's easy to be intimidated, even as a software developer. The good news is that it doesn't have to be that hard. Master machine learning by writing code, from simple learning programs all the way to a deep learning system. Tackle the hard topics by breaking them down so they're easier to understand, and build your confidence by getting your hands dirty.

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On this date in 1915, Albert Einstein published his theory of general relativity. Following up on his theory of special relativity from a few years prior, this new theory upended the world of physics by changing the concepts of gravity, the way light moves in space, and the passage of time. Classical Newtonian physicists found themselves having to change their ways of thinking, which can be difficult if you thought that the way gravity works was settled a few hundred years earlier.

Machine learning is having a similar effect on software development today. It has grown exponentially in recent years, and the concepts can be intimidating and obscure. The breadth of the topic can make it difficult to know where to start, and the array of frameworks and libraries around machine learning obscures how it works, preventing real understanding. Take control with Programming Machine Learning, which starts at the beginning and teaches you through writing code and experimentation.

Learn how it all works by building it yourself.

Programming Machine Learning: From Zero to Deep Learning

Peel away the obscurities of machine learning, starting from scratch and going all the way to deep learning. Machine learning can be intimidating, with its reliance on math and algorithms that most programmers don't encounter in their regular work. Take a hands-on approach, writing the Python code yourself, without any libraries to obscure what's really going on. Iterate on your design, and add layers of complexity as you go.

Build an image recognition application from scratch with supervised learning. Predict the future with linear regression. Dive into gradient descent, a fundamental algorithm that drives most of machine learning. Create perceptrons to classify data. Build neural networks to tackle more complex and sophisticated data sets. Train and refine those networks with backpropagation and batching. Layer the neural networks, eliminate overfitting, and add convolution to transform your neural network into a true deep learning system.

Start from the beginning and code your way to machine learning mastery.

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