Book Image

Python for Data Science For Dummies - Second Edition

By : John Paul Mueller, Luca Massaron
Book Image

Python for Data Science For Dummies - Second Edition

By: John Paul Mueller, Luca Massaron

Overview of this book

Python is a general-purpose programming language created in the late 1980s — and named after Monty Python — that's used by thousands of people to do things from testing microchips at Intel to powering Instagram to building video games with the PyGame library. The book begins by discussing how Python can make data science easy. You’ll learn how to work with the Anaconda tool suite that makes coding in Python easy. You’ll also learn to write code using Google Colab. As you progress, you'll discover how to perform interesting calculations and data manipulations using various Python libraries, such as pandas and NumPy. You’ll learn how to create data visualizations with MatPlotLib. While learning the advanced concepts, you’ll learn how to wrangle data by using techniques, such as hierarchical clustering. Finally, you’ll learn how to work with decision trees and use machine learning to make predictions. By the end of the book, you’ll have the skills and the knowledge that’s needed to write code in Python and extract information from data.
Table of Contents (13 chapters)
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Chapter 19

Increasing Complexity with Linear and Nonlinear Tricks


Bullet Expanding your feature using polynomials

Bullet Regularizing your model

Bullet Learning from big data

Bullet Using support vector machines and neural network

Previous chapters introduce you to some of the simplest, yet effective, machine learning algorithms, such as linear and logistic regression, Naïve Bayes, and K-Nearest Neighbors (KNN). At this point, you can successfully complete a regression or classification project in data science. This chapter explores even more complex and powerful machine learning techniques including the following: reasoning on how to enhance your data; improving your estimates by regularization; and learning from big data by breaking it into manageable chunks.

This chapter also introduces you to the support vector machine (SVM), a powerful family of algorithms for classification and regression. The chapter touches on neural networks as well. Both SVMs and neural networks can perform the...