Introduction

Machine learning is a hot field and it’s only getting hotter. As the volume of accessible data and computing power grows every day, machine learning continues to permeate virtually every facet of modern life—both business and personal—making developers with up-to-speed ML skills more valuable every day. Luckily, with new and emerging ML tools that take on most of the math burden for you, learning those valuable ML skills is easier than ever.

For this sampler, I’ve chosen four chapters from three Manning books that give you a basic introduction to machine learning. The first two chapters are from my own book, Grokking Machine Learning, and they explain what machine learning is and how a machine learns, as well as the different kinds of machine learning and the types of tasks each is best suited for.

In a chapter from Machine Learning for Business by Doug Hudgeon and Richard Nichol, you’ll take a look at how machine learning is revolutionizing business and how you can use it to increase customer retention, identify business processes that are at risk of failure, and make informed decisions based on reliable market trend predictions. You’ll also explore how using ML to automate as much as possible in your business is key to significantly boosting productivity.

The last chapter comes from Human-in-the-Loop Machine Learning by Robert Munro. It highlights the important role humans play in the effectiveness of ML models. Humans and machines must work together on ML models if they are to be successful. For example, the role of humans is crucial in selecting the right data to review and in creating the training data that machines will ultimately learn from.

I believe this sampler provides a solid foundation for your machine learning education. If you’re interested in delving further into this rapidly growing field, any and all of the complete books sampled here are an excellent way to build on that foundation.