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)

Unsupervised learning and clustering

Let's quickly review supervised learning with an example. When you are training machine-learning algorithms, you are able to observe and direct the learning by providing labels. Think about the following dataset, where each row indicates a customer and each column represents a different feature such as Age, Gender, Income, Profession, Tenure and City. Take a look at this table:

You may want to perform different kinds of analysis. One of them could be to predict which of the customers is likely to leave, namely, churn analysis. To do that, you need to label each customer based on their history to indicate which customers have left or stayed, as displayed here, in this table:


Your algorithm will learn the characteristics of customers based on their label. Algorithm will learn the characteristics of customers who left or stayed, and, when...