Numsense! Data Science for the Layman
Numsense! Data Science for the Layman
No Math Added
About the Book
This book has been shifted to:
About the Authors
Table of Contents
- Foreword
- Preface
- Why Data Science?
-
1. Basics in a Nutshell
- 1.1 Data Preparation
- 1.2 Algorithm Selection
- 1.3 Parameter Tuning
- 1.4 Evaluating Results
- 1.5 Summary
-
2. k-Means Clustering
- 2.1 Finding Customer Clusters
- 2.2 Example: Personality Profiles of Movie Fans
- 2.3 Defining Clusters
- 2.4 Limitations
- 2.5 Summary
-
3. Principal Component Analysis
- 3.1 Exploring Nutritional Content of Food
- 3.2 Principal Components
- 3.3 Example: Analyzing Food Groups
- 3.4 Limitations
- 3.5 Summary
-
4. Association Rules
- 4.1 Discovering Purchasing Patterns
- 4.2 Support, Confidence and Lift
- 4.3 Example: Transacting Grocery Sales
- 4.4 Apriori Principle
- 4.5 Limitations
- 4.6 Summary
-
5. Social Network Analysis
- 5.1 Mapping out Relationships
- 5.2 Example: Geopolitics in Weapons Trade
- 5.3 Louvain Method
- 5.4 PageRank Algorithm
- 5.5 Limitations
- 5.6 Summary
-
6. Regression Analysis
- 6.1 Deriving a Trend Line
- 6.2 Example: Predicting House Prices
- 6.3 Gradient Descent
- 6.4 Regression Coefficients
- 6.5 Correlation Coefficients
- 6.6 Limitations
- 6.7 Summary
-
7. k-Nearest Neighbors and Anomaly Detection
- 7.1 Food Forensics
- 7.2 Birds of a Feather Flock Together
- 7.3 Example: Distilling Differences in Wine
- 7.4 Anomaly Detection
- 7.5 Limitations
- 7.6 Summary
-
8. Support Vector Machine
- 8.1 “No” or “Oh No”?
- 8.2 Example: Predicting Heart Disease
- 8.3 Delineating an Optimal Boundary
- 8.4 Limitations
- 8.5 Summary
-
9. Decision Tree
- 9.1 Predicting Survival in a Disaster
- 9.2 Example: Escaping from the Titanic
- 9.3 Generating a Decision Tree
- 9.4 Limitations
- 9.5 Summary
-
10. Random Forests
- 10.1 Wisdom of the Crowd
- 10.2 Example: Forecasting Crime
- 10.3 Ensembles
- 10.4 Bootstrap Aggregating (Bagging)
- 10.5 Limitations
- 10.6 Summary
-
11. Neural Networks
- 11.1 Building a Brain
- 11.2 Example: Recognizing Handwritten Digits
- 11.3 Components of a Neural Network
- 11.4 Activation Rules
- 11.5 Limitations
- 11.6 Summary
-
12. A/B Testing and Multi-Armed Bandits
- 12.1 Basics of A/B testing
- 12.2 Limitations of A/B testing
- 12.3 Epsilon-Decreasing Strategy
- 12.4 Example: Multi-Arm Bandits
- 12.5 Fun Fact: Sticking to the Winner
- 12.6 Limitations of an Epsilon-Decreasing Strategy
- 12.7 Summary
-
Appendix
- A. Overview of Unsupervised Learning Algorithms
- B. Overview of Supervised Learning Algorithms
- C. List of Tuning Parameters
- D. More Evaluation Metrics
- Glossary
- Data Sources and References
- About the Authors
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