Applied machine learning with Python:
Saved in:
| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Milano
BUP, Bocconi University Press
March 2020
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| Edition: | First international edition |
| Subjects: | |
| Online Access: | FHD01 TUM01 |
| Physical Description: | 1 Online-Ressource (xvii, 182 Seiten) |
| ISBN: | 9788831322140 |
Staff View
MARC
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| 100 | 1 | |a Giussani, Andrea |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Applied machine learning with Python |c Andrea Giussani |
| 250 | |a First international edition | ||
| 264 | 1 | |a Milano |b BUP, Bocconi University Press |c March 2020 | |
| 300 | |a 1 Online-Ressource (xvii, 182 Seiten) | ||
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| 505 | 8 | |a APPLIED MACHINE LEARNING WITH PYTHON -- Contents -- List of Figures -- Preface -- Chapter 1. Introduction to Machine Learning -- 1.1 A simple supervised model: Nearest Neighbor -- 1.2 Preprocessing -- 1.3 Methods for Dealing with Imbalanced Data -- 1.4 Reducing Dimensionality: Principal Component Analysis -- Chapter 2. Linear Models for Machine Learning -- 2.1 Linear Regression -- 2.2 Shrinkage Methods -- 2.3 Robust Regression -- 2.4 Logistic Regression -- 2.5 Linear Support Vector Machine -- 2.6 Beyond Linearity: Kernelized Models | |
| 505 | 8 | |a Chapter 3. Beyond Linearity: Ensemble Methods for Machine Learning -- 3.1 Introduction -- 3.2 Ensemble Methods -- 3.3 Random Forests -- 3.4 Boosting Methods -- Chapter 4. An Introduction to Modern Machine Learning Techniques -- 4.1 Introduction to Natural language Processing -- 4.2 Introduction to Deep Learning -- Appendices -- Appendix A.A crash course in Python -- A.1 Building Blocks in Python -- A.2 Data Structure in Python -- A.3 Loops in Python -- A.4 Advanced Data Structure in Python -- A.5 Advanced Concepts on Functions -- A.6 Introduction to Object-Oriented Programming | |
| 505 | 8 | |a Appendix B. Mathematics behind the skip-gram model -- Index -- Bibliography -- Back Cover | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Python (Computer program language) | |
| 650 | 7 | |a Machine learning |2 fast | |
| 650 | 7 | |a Python (Computer program language) |2 fast | |
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Record in the Search Index
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| author | Giussani, Andrea |
| author_facet | Giussani, Andrea |
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| contents | APPLIED MACHINE LEARNING WITH PYTHON -- Contents -- List of Figures -- Preface -- Chapter 1. Introduction to Machine Learning -- 1.1 A simple supervised model: Nearest Neighbor -- 1.2 Preprocessing -- 1.3 Methods for Dealing with Imbalanced Data -- 1.4 Reducing Dimensionality: Principal Component Analysis -- Chapter 2. Linear Models for Machine Learning -- 2.1 Linear Regression -- 2.2 Shrinkage Methods -- 2.3 Robust Regression -- 2.4 Logistic Regression -- 2.5 Linear Support Vector Machine -- 2.6 Beyond Linearity: Kernelized Models Chapter 3. Beyond Linearity: Ensemble Methods for Machine Learning -- 3.1 Introduction -- 3.2 Ensemble Methods -- 3.3 Random Forests -- 3.4 Boosting Methods -- Chapter 4. An Introduction to Modern Machine Learning Techniques -- 4.1 Introduction to Natural language Processing -- 4.2 Introduction to Deep Learning -- Appendices -- Appendix A.A crash course in Python -- A.1 Building Blocks in Python -- A.2 Data Structure in Python -- A.3 Loops in Python -- A.4 Advanced Data Structure in Python -- A.5 Advanced Concepts on Functions -- A.6 Introduction to Object-Oriented Programming Appendix B. Mathematics behind the skip-gram model -- Index -- Bibliography -- Back Cover |
| ctrlnum | (OCoLC)1155083440 (DE-599)BVBBV046719768 |
| discipline | Informatik |
| discipline_str_mv | Informatik |
| edition | First international edition |
| format | Electronic eBook |
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| id | DE-604.BV046719768 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T14:33:15Z |
| indexdate | 2024-07-10T08:51:59Z |
| institution | BVB |
| isbn | 9788831322140 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032130009 |
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| physical | 1 Online-Ressource (xvii, 182 Seiten) |
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| publishDate | 2020 |
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| publisher | BUP, Bocconi University Press |
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| spelling | Giussani, Andrea Verfasser aut Applied machine learning with Python Andrea Giussani First international edition Milano BUP, Bocconi University Press March 2020 1 Online-Ressource (xvii, 182 Seiten) txt rdacontent c rdamedia cr rdacarrier APPLIED MACHINE LEARNING WITH PYTHON -- Contents -- List of Figures -- Preface -- Chapter 1. Introduction to Machine Learning -- 1.1 A simple supervised model: Nearest Neighbor -- 1.2 Preprocessing -- 1.3 Methods for Dealing with Imbalanced Data -- 1.4 Reducing Dimensionality: Principal Component Analysis -- Chapter 2. Linear Models for Machine Learning -- 2.1 Linear Regression -- 2.2 Shrinkage Methods -- 2.3 Robust Regression -- 2.4 Logistic Regression -- 2.5 Linear Support Vector Machine -- 2.6 Beyond Linearity: Kernelized Models Chapter 3. Beyond Linearity: Ensemble Methods for Machine Learning -- 3.1 Introduction -- 3.2 Ensemble Methods -- 3.3 Random Forests -- 3.4 Boosting Methods -- Chapter 4. An Introduction to Modern Machine Learning Techniques -- 4.1 Introduction to Natural language Processing -- 4.2 Introduction to Deep Learning -- Appendices -- Appendix A.A crash course in Python -- A.1 Building Blocks in Python -- A.2 Data Structure in Python -- A.3 Loops in Python -- A.4 Advanced Data Structure in Python -- A.5 Advanced Concepts on Functions -- A.6 Introduction to Object-Oriented Programming Appendix B. Mathematics behind the skip-gram model -- Index -- Bibliography -- Back Cover Machine learning Python (Computer program language) Machine learning fast Python (Computer program language) fast Erscheint auch als Druck-Ausgabe, International Edition 978-88-31322-04-1 |
| spellingShingle | Giussani, Andrea Applied machine learning with Python APPLIED MACHINE LEARNING WITH PYTHON -- Contents -- List of Figures -- Preface -- Chapter 1. Introduction to Machine Learning -- 1.1 A simple supervised model: Nearest Neighbor -- 1.2 Preprocessing -- 1.3 Methods for Dealing with Imbalanced Data -- 1.4 Reducing Dimensionality: Principal Component Analysis -- Chapter 2. Linear Models for Machine Learning -- 2.1 Linear Regression -- 2.2 Shrinkage Methods -- 2.3 Robust Regression -- 2.4 Logistic Regression -- 2.5 Linear Support Vector Machine -- 2.6 Beyond Linearity: Kernelized Models Chapter 3. Beyond Linearity: Ensemble Methods for Machine Learning -- 3.1 Introduction -- 3.2 Ensemble Methods -- 3.3 Random Forests -- 3.4 Boosting Methods -- Chapter 4. An Introduction to Modern Machine Learning Techniques -- 4.1 Introduction to Natural language Processing -- 4.2 Introduction to Deep Learning -- Appendices -- Appendix A.A crash course in Python -- A.1 Building Blocks in Python -- A.2 Data Structure in Python -- A.3 Loops in Python -- A.4 Advanced Data Structure in Python -- A.5 Advanced Concepts on Functions -- A.6 Introduction to Object-Oriented Programming Appendix B. Mathematics behind the skip-gram model -- Index -- Bibliography -- Back Cover Machine learning Python (Computer program language) Machine learning fast Python (Computer program language) fast |
| title | Applied machine learning with Python |
| title_auth | Applied machine learning with Python |
| title_exact_search | Applied machine learning with Python |
| title_exact_search_txtP | Applied machine learning with Python |
| title_full | Applied machine learning with Python Andrea Giussani |
| title_fullStr | Applied machine learning with Python Andrea Giussani |
| title_full_unstemmed | Applied machine learning with Python Andrea Giussani |
| title_short | Applied machine learning with Python |
| title_sort | applied machine learning with python |
| topic | Machine learning Python (Computer program language) Machine learning fast Python (Computer program language) fast |
| topic_facet | Machine learning Python (Computer program language) |
| work_keys_str_mv | AT giussaniandrea appliedmachinelearningwithpython |