Numsense!DataSciencefortheLayman_NoMathA.epub - (EPUB全文下载)

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Numsense! Data Science for the Layman
No Math Added

Annalyn Ng and Kenneth Soo

© 2017 Annalyn Ng and Kenneth Soo
ISBN for EPUB version: 978-981-11-2822-6
ISBN for MOBI version: 978-981-11-2700-7
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
Appe ............

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