封面
版权信息
Preface
1. Introduction to Scikit-Learn
Introduction
Introduction to Machine Learning
Scikit-Learn
Data Representation
Data Preprocessing
Scikit-Learn API
Supervised and Unsupervised Learning
Summary
2. Unsupervised Learning – Real-Life Applications
Introduction
Clustering
Exploring a Dataset – Wholesale Customers Dataset
Data Visualization
Mean-Shift Algorithm
DBSCAN Algorithm
Evaluating the Performance of Clusters
Summary
3. Supervised Learning – Key Steps
Introduction
Supervised Learning Tasks
Model Validation and Testing
Evaluation Metrics
Error Analysis
Summary
4. Supervised Learning Algorithms: Predicting Annual Income
Introduction
Exploring the Dataset
The Naïve Bayes Algorithm
The Decision Tree Algorithm
The Support Vector Machine Algorithm
Error Analysis
Summary
5. Supervised Learning – Key Steps
Introduction
Artificial Neural Networks
Applying an Artificial Neural Network
Performance Analysis
Summary
6. Building Your Own Program
Introduction
Program Definition
Saving and Loading a Trained Model
Interacting with a Trained Model
Summary
Appendix
1. Introduction to Scikit-Learn
2. Unsupervised Learning – Real-Life Applications
3. Supervised Learning – Key Steps
4. Supervised Learning Algorithms: Predicting Annual Income
5. Artificial Neural Networks: Predicting Annual Income
6. Building Your Own Program
更新时间：2021-06-18 18:24:05
完结共55章
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