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scikit-learn：Machine Learning Simplified

Raúl Garreta Guillermo Moncecchi Trent Hauck Gavin Hackeling 著

更新時間：2021-08-20 10:07:55

開會員，本書免費讀 >

Ifyouareaprogrammerandwanttoexploremachinelearninganddata-basedmethodstobuildintelligentapplicationsandenhanceyourprogrammingskills,thisisthecourseforyou.Nopreviousexperiencewithmachine-learningalgorithmsisrequired.

目錄(27章)

倒序

封面
版權頁
Credits
Preface
Part 1. Module 1
Chapter 1. Machine Learning – A Gentle Introduction
Chapter 2. Supervised Learning
Chapter 3. Unsupervised Learning
Chapter 4. Advanced Features
Part 2. Module 2
Chapter 1. Premodel Workflow
Chapter 2. Working with Linear Models
Chapter 3. Building Models with Distance Metrics
Chapter 4. Classifying Data with scikit-learn
Chapter 5. Postmodel Workflow
Part 3. Module 3
Chapter 1. The Fundamentals of Machine Learning
Chapter 2. Linear Regression
Chapter 3. Feature Extraction and Preprocessing
Chapter 4. From Linear Regression to Logistic Regression
Chapter 5. Nonlinear Classification and Regression with Decision Trees
Chapter 6. Clustering with K-Means
Chapter 7. Dimensionality Reduction with PCA
Chapter 8. The Perceptron
Chapter 9. From the Perceptron to Support Vector Machines
Chapter 10. From the Perceptron to Artificial Neural Networks
Index 更新時間：2021-08-20 10:07:55

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scikit-learn：Machine Learning Simplified