Data Analytics and Machine Learning
A Practical Overview in Tableau, Excel, and Microsoft ML Studio (classic)
- Foreword: Why Data Science?
- Chapter 1: Introduction to Business Analytics
- Chapter 2: AI in Business
- Chapter 3: Data Mining Project Methodology
- Chapter 4: Visualization: Theory and Design
- Chapter 5: Tableau: Core Features
- Chapter 6: Tableau: Exploratory Data Analysis
- Chapter 7: Tableau: Clustering
- Chapter 8: Excel: Multivariate Prediction
- Chapter 9: AMLS: Introduction to Pipelines
- Chapter 10: AMLS: Data Cleaning and Preparation
- Chapter 11: AMLS: Algorithm Selection
- Chapter 12: AMLS: Selecting the Features
- Chapter 13: AMLS: Optimizing Model Fit and Performance
- Chapter 14: AMLS: Natural Language Processing
- Chapter 15: AMLS: Recommendation Engines
- Chapter 16: Project: Putting It All Together
- Supplement: Datasets
- Supplement: AMLS: Computer Vision (in progress)