Book Image

Julia Programming Projects

By : Adrian Salceanu
Book Image

Julia Programming Projects

By: Adrian Salceanu

Overview of this book

Julia is a new programming language that offers a unique combination of performance and productivity. Its powerful features, friendly syntax, and speed are attracting a growing number of adopters from Python, R, and Matlab, effectively raising the bar for modern general and scientific computing. After six years in the making, Julia has reached version 1.0. Now is the perfect time to learn it, due to its large-scale adoption across a wide range of domains, including fintech, biotech, education, and AI. Beginning with an introduction to the language, Julia Programming Projects goes on to illustrate how to analyze the Iris dataset using DataFrames. You will explore functions and the type system, methods, and multiple dispatch while building a web scraper and a web app. Next, you'll delve into machine learning, where you'll build a books recommender system. You will also see how to apply unsupervised machine learning to perform clustering on the San Francisco business database. After metaprogramming, the final chapters will discuss dates and time, time series analysis, visualization, and forecasting. We'll close with package development, documenting, testing and benchmarking. By the end of the book, you will have gained the practical knowledge to build real-world applications in Julia.
Table of Contents (19 chapters)
Title Page
Copyright and Credits
About Packt

Chapter 8. Leveraging Unsupervised Learning Techniques

Our supervised machine learning project was a success and we're well on our way to becoming experts in recommender systems. It's now time to leave behind the safety of our neatly tagged data and venture into the unknown. Yes, I'm talking about unsupervised machine learning. In this chapter, we'll train a model that will help us find hidden patterns in a mountain of data. And since we've come so far on our journey of learning Julia, it's time to take off the training wheels and take on our first client.

Just kidding—for now, we'll play pretend, but we'll indeed tackle a machine learning problem that could very well be one of the first tasks of a junior data scientist. We'll help our imaginary customer discover key insights for supporting their advertising strategy, a very important component of beginning their operations in San Francisco.

In the process, we'll learn about the following:

  • What unsupervised machine learning is and when and...