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Linear Regression - House Price Prediction

Linear regression is one of the world's oldest machine learning concepts. Invented in the early nineteenth century, it is still one of the more vulnerable methods of understanding the relationship between input and output.

The ideas behind linear regression is familiar to us all. We feel that some things are correlated with one another. Sometimes they are causal in nature. There exists a very fine line between correlation and causation. For example, summer sees more sales in ice creams and cold beverages, while winter sees more sales in hot cocoa and coffee. We could say that the seasons themselves cause the amount of sales—they're causal in nature. But are they really?

Without further analysis, the best thing we can say is that they are correlated with one another. The phenomenon of summer is connected to the phenomenon...

Key benefits

Explore ML tasks and Go’s machine learning ecosystem

Implement clustering, regression, classification, and neural networks with Go

Get to grips with libraries such as Gorgonia, Gonum, and GoCv for training models in Go

Description

Go is the perfect language for machine learning; it helps to clearly describe complex algorithms, and also helps developers to understand how to run efficient optimized code. This book will teach you how to implement machine learning in Go to make programs that are easy to deploy and code that is not only easy to understand and debug, but also to have its performance measured. The book begins by guiding you through setting up your machine learning environment with Go libraries and capabilities. You will then plunge into regression analysis of a real-life house pricing dataset and build a classification model in Go to classify emails as spam or ham. Using Gonum, Gorgonia, and STL, you will explore time series analysis along with decomposition and clean up your personal Twitter timeline by clustering tweets. In addition to this, you will learn how to recognize handwriting using neural networks and convolutional neural networks. Lastly, you'll learn how to choose the most appropriate machine learning algorithms to use for your projects with the help of a facial detection project. By the end of this book, you will have developed a solid machine learning mindset, a strong hold on the powerful Go toolkit, and a sound understanding of the practical implementations of machine learning algorithms in real-world projects.

Who is this book for?

If you’re a machine learning engineer, data science professional, or Go programmer who wants to implement machine learning in your real-world projects and make smarter applications easily, this book is for you. Some coding experience in Golang and knowledge of basic machine learning concepts will help you in understanding the concepts covered in this book.

What you will learn

Set up a machine learning environment with Go libraries

Use Gonum to perform regression and classification

Explore time series models and decompose trends with Go libraries

Clean up your Twitter timeline by clustering tweets

Learn to use external services for your machine learning needs

Recognize handwriting using neural networks and CNN with Gorgonia

Implement facial recognition using GoCV and OpenCV

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