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Hands-On Data Analysis with Pandas - Second Edition

You're reading from Hands-On Data Analysis with Pandas - Second Edition

Product type Book

Published in Apr 2021

Publisher Packt

ISBN-13 9781800563452

Pages 788 pages

Edition 2nd Edition

Languages

Concepts

Author (1):

Stefanie Molin

Table of Contents (21) Chapters

Preface

Section 1: Getting Started with Pandas

Chapter 1: Introduction to Data Analysis

Chapter 2: Working with Pandas DataFrames

Section 2: Using Pandas for Data Analysis

Chapter 3: Data Wrangling with Pandas

Chapter 4: Aggregating Pandas DataFrames

Chapter 5: Visualizing Data with Pandas and Matplotlib

Chapter 6: Plotting with Seaborn and Customization Techniques

Section 3: Applications – Real-World Analyses Using Pandas

Chapter 7: Financial Analysis – Bitcoin and the Stock Market

Chapter 8: Rule-Based Anomaly Detection

Section 4: Introduction to Machine Learning with Scikit-Learn

Chapter 9: Getting Started with Machine Learning in Python

Chapter 10: Making Better Predictions – Optimizing Models

Chapter 11: Machine Learning Anomaly Detection

Section 5: Additional Resources

Chapter 12: The Road Ahead

Solutions

Other Books You May Enjoy

Appendix

Overview of the machine learning landscape

Machine learning is a subset of artificial intelligence (AI) whereby an algorithm can learn to predict values from input data without explicitly being taught rules. These algorithms rely on statistics to make inferences as they learn; they then use what they learn to make predictions.

Applying for a loan, using a search engine, sending a robot vacuum to clean a specific room with a voice command—machine learning can be found everywhere we look. This is because it can be used for many purposes, for example, voice recognition by AI assistants such as Alexa, Siri, or Google Assistant, mapping floor plans by exploring surroundings, determining who will default on a loan, figuring out which search results are relevant, and even painting (https://www.boredpanda.com/computer-deep-learning-algorithm-painting-masters/).

Machine learning models can be made to adapt to changes in the input over time and are a huge help in making decisions...

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Authors (1)

Stefanie Molin

Stefanie Molin is a data scientist and software engineer at Bloomberg LP in NYC, tackling tough problems in information security, particularly revolving around anomaly detection, building tools for gathering data, and knowledge sharing. She has extensive experience in data science, designing anomaly detection solutions, and utilizing machine learning in both R and Python in the AdTech and FinTech industries. She holds a B.S. in operations research from Columbia University's Fu Foundation School of Engineering and Applied Science, with minors in economics, and entrepreneurship and innovation. In her free time, she enjoys traveling the world, inventing new recipes, and learning new languages spoken among both people and computers.

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