Book Image

Learn Python by Building Data Science Applications

By : Philipp Kats, David Katz
Book Image

Learn Python by Building Data Science Applications

By: Philipp Kats, David Katz

Overview of this book

Python is the most widely used programming language for building data science applications. Complete with step-by-step instructions, this book contains easy-to-follow tutorials to help you learn Python and develop real-world data science projects. The “secret sauce” of the book is its curated list of topics and solutions, put together using a range of real-world projects, covering initial data collection, data analysis, and production. This Python book starts by taking you through the basics of programming, right from variables and data types to classes and functions. You’ll learn how to write idiomatic code and test and debug it, and discover how you can create packages or use the range of built-in ones. You’ll also be introduced to the extensive ecosystem of Python data science packages, including NumPy, Pandas, scikit-learn, Altair, and Datashader. Furthermore, you’ll be able to perform data analysis, train models, and interpret and communicate the results. Finally, you’ll get to grips with structuring and scheduling scripts using Luigi and sharing your machine learning models with the world as a microservice. By the end of the book, you’ll have learned not only how to implement Python in data science projects, but also how to maintain and design them to meet high programming standards.
Table of Contents (26 chapters)
Free Chapter
1
Section 1: Getting Started with Python
11
Section 2: Hands-On with Data
17
Section 3: Moving to Production

Chapter 13

What is machine learning?

Machine learning is a discipline (a branch of artificial intelligence) that focuses on automatic model building. Machine learning algorithms allow us to automatically find patterns or a hierarchy in data (unsupervised learning), or even predict the property of a given sample after training on the prepared "training" dataset (supervised learning).

What is the difference between supervised and non-supervised learning?

Unsupervised learning algorithms operate on any given dataset with no special preparation required and aim to find patterns or structures without any prior knowledge. Supervised learning models are trained on a properly labeled "training set," which they do by building a generalized model, and then are able to infer values for the new data samples it hasn't seen before.

What are the drawbacks of k-means...