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

Unsupervised machine learning

In Chapter 7, Machine Learning For Recommender Systems, we learned about supervised machine learning. We used various features in the data (such as the user's ratings) to perform classification tasks. In supervised machine learning, we act a bit like a teacher—we provide a multitude of examples to our algorithm, which, once it gets enough data (and so its training is complete), is able to make generalizations about new items and infer their category or class.

But not all of the data lends itself to these kinds of tasks. Sometimes our data isn't labeled in any way. Imagine items as diverse as a website's traffic logs or the appointments made by customers at a dental clinic. These are just raw observations that aren't categorized in any way and don't contain any meaning. In such cases, data analysts employ unsupervised machine learning algorithms.

Unsupervised machine learning is used to discover hidden structures and patterns in otherwise unlabeled data. It is...