Intro -- Preface -- Jupyter Fundamentals -- Introduction -- Basic Functionality and Features -- What is a Jupyter Notebook and Why is it Useful? -- Navigating the Platform -- Exercise 1: Introducing Jupyter Notebooks -- Jupyter Features -- Exercise 2: Implementing Jupyter's Most Useful Features -- Converting a Jupyter Notebook to a Python Script -- Python Libraries -- Exercise 3: Importing the External Libraries and Setting Up the Plotting Environment -- Our First Analysis - The Boston Housing Dataset -- Loading the Data into Jupyter Using a Pandas DataFrame -- Exercise 4: Loading the Boston Housing Dataset -- Data Exploration -- Exercise 5: Analyzing the Boston Housing Dataset -- Introduction to Predictive Analytics with Jupyter Notebooks -- Exercise 6: Applying Linear Models With Seaborn and Scikit-learn -- Activity 1: Building a Third-Order Polynomial Model -- Using Categorical Features for Segmentation Analysis -- Exercise 7: Creating Categorical Fields From Continuous Variables and Make Segmented Visualizations -- Summary -- Data Cleaning and Advanced Machine Learning -- Introduction -- Preparing to Train a Predictive Model -- Determining a Plan for Predictive Analytics -- Exercise 8: Explore Data Preprocessing Tools and Methods -- Activity 2: Preparing to Train a Predictive Model for the Employee-Retention Problem -- Training Classification Models -- Introduction to Classification Algorithms -- Exercise 9: Training Two-Feature Classification Models With Scikit-learn -- The plot_decision_regions Function -- Exercise 10: Training K-nearest Neighbors for Our Model -- Exercise 11: Training a Random Forest -- Assessing Models With K-fold Cross-Validation and Validation Curves -- Exercise 12: Using K-fold Cross Validation and Validation Curves in Python With Scikit-learn -- Dimensionality Reduction Techniques.

Exercise 13: Training a Predictive Model for the Employee Retention Problem -- Summary -- Web Scraping and Interactive Visualizations -- Introduction -- Scraping Web Page Data -- Introduction to HTTP Requests -- Making HTTP Requests in the Jupyter Notebook -- Exercise 14: Handling HTTP Requests With Python in a Jupyter Notebook -- Parsing HTML in the Jupyter Notebook -- Exercise 15: Parsing HTML With Python in a Jupyter Notebook -- Activity 3: Web Scraping With Jupyter Notebooks -- Interactive Visualizations -- Building a DataFrame to Store and Organize Data -- Exercise 16: Building and Merging Pandas DataFrames -- Introduction to Bokeh -- Exercise 17: Introduction to Interactive Visualization With Bokeh -- Activity 4: Exploring Data with Interactive Visualizations -- Summary -- Appendix A -- Index -- _GoBack -- Activity_A:_Web_Scraping_with_Jupyter_No -- _bookmark13.