Book Image

Mastering Numerical Computing with NumPy

By : Umit Mert Cakmak, Tiago Antao, Mert Cuhadaroglu
Book Image

Mastering Numerical Computing with NumPy

By: Umit Mert Cakmak, Tiago Antao, Mert Cuhadaroglu

Overview of this book

NumPy is one of the most important scientific computing libraries available for Python. Mastering Numerical Computing with NumPy teaches you how to achieve expert level competency to perform complex operations, with in-depth coverage of advanced concepts. Beginning with NumPy's arrays and functions, you will familiarize yourself with linear algebra concepts to perform vector and matrix math operations. You will thoroughly understand and practice data processing, exploratory data analysis (EDA), and predictive modeling. You will then move on to working on practical examples which will teach you how to use NumPy statistics in order to explore US housing data and develop a predictive model using simple and multiple linear regression techniques. Once you have got to grips with the basics, you will explore unsupervised learning and clustering algorithms, followed by understanding how to write better NumPy code while keeping advanced considerations in mind. The book also demonstrates the use of different high-performance numerical computing libraries and their relationship with NumPy. You will study how to benchmark the performance of different configurations and choose the best for your system. By the end of this book, you will have become an expert in handling and performing complex data manipulations.
Table of Contents (11 chapters)

Summary

Linear regression is one of the most common techniques for modeling the relationship between continuous variables. The application of this method is very widely used in the industry. We started modeling part of the book on linear regression, not just because it's very popular, but because it's a relatively easy technique and contains most of the elements which almost every machine learning algorithm has.

In this chapter, we learned about supervised and unsupervised learning and built a linear regression model by using the Boston housing dataset. We touched upon different important concepts such as hyperparameters, loss functions, and gradient descent. The main purpose of this chapter was to give you sufficient knowledge so that you can build and tune a linear regression model and understand what it does step by step. We looked at two practical cases where we...