- Why should we use a special stack of packages for data analysis?
- Why are NumPy computations so fast compared to normal Python?
- What is the use case and benefit of using Pandas over NumPy?
- What does sklearn stand for?
- Title Page
- Copyright and Credits
- About Packt
- Contributors
- Preface
- Section 1: Getting Started with Python
- Preparing the Workspace
- First Steps in Coding - Variables and Data Types
- Functions
- Data Structures
- Loops and Other Compound Statements
- First Script – Geocoding with Web APIs
- Scraping Data from the Web with Beautiful Soup 4
- Simulation with Classes and Inheritance
- Shell, Git, Conda, and More – at Your Command
- Section 2: Hands-On with Data
- Python for Data Applications
- Data Cleaning and Manipulation
- Data Exploration and Visualization
- Training a Machine Learning Model
- Improving Your Model – Pipelines and Experiments
- Section 3: Moving to Production
- Packaging and Testing with Poetry and PyTest
- Data Pipelines with Luigi
- Let's Build a Dashboard
- Serving Models with a RESTful API
- Serverless API Using Chalice
- Best Practices and Python Performance
- Assessments
- Other Books You May Enjoy
Questions
-
No Comment
..................Content has been hidden....................
You can't read the all page of ebook, please click here login for view all page.