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Index
Cover
Table of Contents
Python Data Science Essentials - Second Edition
Python Data Science Essentials - Second Edition
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
1. First Steps
Installing Python
Introducing Jupyter
Datasets and code used in the book
Summary
2. Data Munging
Data loading and preprocessing with pandas
Working with categorical and text data
Data processing with NumPy
Creating NumPy arrays
NumPy's fast operations and computations
Summary
3. The Data Pipeline
Building new features
Dimensionality reduction
The detection and treatment of outliers
Validation metrics
Testing and validating
Cross-validation
Hyperparameter optimization
Feature selection
Wrapping everything in a pipeline
Summary
4. Machine Learning
Linear and logistic regression
Naive Bayes
K-Nearest Neighbors
Nonlinear algorithms
Ensemble strategies
Dealing with big data
Approaching deep learning
A peek at Natural Language Processing (NLP)
An overview of unsupervised learning
Summary
5. Social Network Analysis
Graph algorithms
Graph loading, dumping, and sampling
Summary
6. Visualization, Insights, and Results
Wrapping up matplotlib's commands
Interactive visualizations with Bokeh
Advanced data-learning representations
Summary
1. Strengthen Your Python Foundations
Learn by watching, reading, and doing
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