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Hands-On Data Analysis with Pandas

You're reading from   Hands-On Data Analysis with Pandas A Python data science handbook for data collection, wrangling, analysis, and visualization

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Product type Paperback
Published in Apr 2021
Publisher Packt
ISBN-13 9781800563452
Length 788 pages
Edition 2nd Edition
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Author (1):
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Stefanie Molin Stefanie Molin
Author Profile Icon Stefanie Molin
Stefanie Molin
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Table of Contents (21) Chapters Close

Preface 1. Section 1: Getting Started with Pandas
2. Chapter 1: Introduction to Data Analysis FREE CHAPTER 3. Chapter 2: Working with Pandas DataFrames 4. Section 2: Using Pandas for Data Analysis
5. Chapter 3: Data Wrangling with Pandas 6. Chapter 4: Aggregating Pandas DataFrames 7. Chapter 5: Visualizing Data with Pandas and Matplotlib 8. Chapter 6: Plotting with Seaborn and Customization Techniques 9. Section 3: Applications – Real-World Analyses Using Pandas
10. Chapter 7: Financial Analysis – Bitcoin and the Stock Market 11. Chapter 8: Rule-Based Anomaly Detection 12. Section 4: Introduction to Machine Learning with Scikit-Learn
13. Chapter 9: Getting Started with Machine Learning in Python 14. Chapter 10: Making Better Predictions – Optimizing Models 15. Chapter 11: Machine Learning Anomaly Detection 16. Section 5: Additional Resources
17. Chapter 12: The Road Ahead 18. Solutions
19. 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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