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Building Data Science Applications with FastAPI

Building Data Science Applications with FastAPI

By : Voron
4.7 (16)
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Building Data Science Applications with FastAPI

Building Data Science Applications with FastAPI

4.7 (16)
By: Voron

Overview of this book

FastAPI is a web framework for building APIs with Python 3.6 and its later versions based on standard Python-type hints. With this book, you’ll be able to create fast and reliable data science API backends using practical examples. This book starts with the basics of the FastAPI framework and associated modern Python programming language concepts. You'll be taken through all the aspects of the framework, including its powerful dependency injection system and how you can use it to communicate with databases, implement authentication and integrate machine learning models. Later, you’ll cover best practices relating to testing and deployment to run a high-quality and robust application. You’ll also be introduced to the extensive ecosystem of Python data science packages. As you progress, you’ll learn how to build data science applications in Python using FastAPI. The book also demonstrates how to develop fast and efficient machine learning prediction backends and test them to achieve the best performance. Finally, you’ll see how to implement a real-time face detection system using WebSockets and a web browser as a client. By the end of this FastAPI book, you’ll have not only learned how to implement Python in data science projects but also how to maintain and design them to meet high programming standards with the help of FastAPI.
Table of Contents (19 chapters)
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1
Section 1: Introduction to Python and FastAPI
7
Section 2: Build and Deploy a Complete Web Backend with FastAPI
13
Section 3: Build a Data Science API with Python and FastAPI

Classifying data with Naive Bayes models

Even though you probably hear a lot about super-advanced ML methods such as deep learning, it's important to say that simpler methods have existed for years and have proven to be very efficient in many situations. Generally, it's always a good idea when you start with a data science problem to try out simpler models that have fewer parameters and are easier to tune. This will quickly give you a baseline to compare with more advanced techniques.

In this section, we'll review Naive Bayes models, a group of fast and simple classification algorithms.

Intuition

Naive Bayes models rely on Bayes' theorem, which defines an equation to describe the probability of an event, given the probability of related events. In the context of classification, it gives us an equation to describe the probability of a label, , given a set of features. In our handwritten digit recognition problem, this would translate to "the probability...

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