DATA-CENTRIC MACHINE LEARNING WITH PYTHON the ultimate guide to engineering and deploying high-quality models based on good data
Join the data-centric revolution and master the concepts, techniques, and algorithms shaping the future of AI and ML development, using Python Key Features Grasp the principles of data centricity and apply them to real-world scenarios Gain experience with quality data collection, labeling, and synth...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Weitere Verfasser: | |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham, UK
Packt Publishing Ltd.
2024
|
| Ausgabe: | 1st edition. |
| Schlagworte: | |
| Online-Zugang: | lizenzpflichtig |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
MARC
| LEADER | 00000nam a22000002 4500 | ||
|---|---|---|---|
| 001 | ZDB-30-ORH-102205922 | ||
| 003 | DE-627-1 | ||
| 005 | 20240404083421.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 240404s2024 xx |||||o 00| ||eng c | ||
| 020 | |a 9781804612415 |c electronic bk. |9 978-1-80461-241-5 | ||
| 020 | |a 1804612413 |c electronic bk. |9 1-80461-241-3 | ||
| 020 | |a 9781804618127 |9 978-1-80461-812-7 | ||
| 035 | |a (DE-627-1)102205922 | ||
| 035 | |a (DE-599)KEP102205922 | ||
| 035 | |a (ORHE)9781804618127 | ||
| 035 | |a (DE-627-1)102205922 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rda | ||
| 041 | |a eng | ||
| 082 | 0 | |a 005.13/3 |2 23/eng/20240305 | |
| 100 | 1 | |a Christensen, Jonas |e VerfasserIn |4 aut | |
| 245 | 1 | 0 | |a DATA-CENTRIC MACHINE LEARNING WITH PYTHON |b the ultimate guide to engineering and deploying high-quality models based on good data |c Jonas Christensen, Nakul Bajaj, Manmohan Gosada ; foreword by Kirk D. Borne |
| 250 | |a 1st edition. | ||
| 264 | 1 | |a Birmingham, UK |b Packt Publishing Ltd. |c 2024 | |
| 300 | |a 1 online resource | ||
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 520 | |a Join the data-centric revolution and master the concepts, techniques, and algorithms shaping the future of AI and ML development, using Python Key Features Grasp the principles of data centricity and apply them to real-world scenarios Gain experience with quality data collection, labeling, and synthetic data creation using Python Develop essential skills for building reliable, responsible, and ethical machine learning solutions Purchase of the print or Kindle book includes a free PDF eBook Book Description In the rapidly advancing data-driven world where data quality is pivotal to the success of machine learning and artificial intelligence projects, this critically timed guide provides a rare, end-to-end overview of data-centric machine learning (DCML), along with hands-on applications of technical and non-technical approaches to generating deeper and more accurate datasets. This book will help you understand what data-centric ML/AI is and how it can help you to realize the potential of 'small data'. Delving into the building blocks of data-centric ML/AI, you'll explore the human aspects of data labeling, tackle ambiguity in labeling, and understand the role of synthetic data. From strategies to improve data collection to techniques for refining and augmenting datasets, you'll learn everything you need to elevate your data-centric practices. Through applied examples and insights for overcoming challenges, you'll get a roadmap for implementing data-centric ML/AI in diverse applications in Python. By the end of this book, you'll have developed a profound understanding of data-centric ML/AI and the proficiency to seamlessly integrate common data-centric approaches in the model development lifecycle to unlock the full potential of your machine learning projects by prioritizing data quality and reliability. What you will learn Understand the impact of input data quality compared to model selection and tuning Recognize the crucial role of subject-matter experts in effective model development Implement data cleaning, labeling, and augmentation best practices Explore common synthetic data generation techniques and their applications Apply synthetic data generation techniques using common Python packages Detect and mitigate bias in a dataset using best-practice techniques Understand the importance of reliability, responsibility, and ethical considerations in ML/AI Who this book is for This book is for data science professionals and machine learning enthusiasts looking to understand the concept of data-centricity, its benefits over a model-centric approach, and the practical application of a best-practice data-centric approach in their work. This book is also for other data professionals and senior leaders who want to explore the tools and techniques to improve data quality and create opportunities for small data ML/AI in their organizations. | ||
| 650 | 0 | |a Machine learning | |
| 650 | 0 | |a Python (Computer program language) | |
| 650 | 0 | |a Data mining | |
| 650 | 4 | |a Apprentissage automatique | |
| 650 | 4 | |a Python (Langage de programmation) | |
| 650 | 4 | |a Exploration de données (Informatique) | |
| 700 | 1 | |a Bajaj, Nakul |e VerfasserIn |4 aut | |
| 700 | 1 | |a Gosada, Manmohan |e VerfasserIn |4 aut | |
| 700 | 1 | |a Borne, Kirk D. |e MitwirkendeR |4 ctb | |
| 776 | 1 | |z 1804618128 | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |z 1804618128 |
| 856 | 4 | 0 | |l TUM01 |p ZDB-30-ORH |q TUM_PDA_ORH |u https://learning.oreilly.com/library/view/-/9781804618127/?ar |m X:ORHE |x Aggregator |z lizenzpflichtig |3 Volltext |
| 912 | |a ZDB-30-ORH | ||
| 951 | |a BO | ||
| 912 | |a ZDB-30-ORH | ||
| 049 | |a DE-91 | ||
Datensatz im Suchindex
| DE-BY-TUM_katkey | ZDB-30-ORH-102205922 |
|---|---|
| _version_ | 1818767371983126528 |
| adam_text | |
| any_adam_object | |
| author | Christensen, Jonas Bajaj, Nakul Gosada, Manmohan |
| author2 | Borne, Kirk D. |
| author2_role | ctb |
| author2_variant | k d b kd kdb |
| author_facet | Christensen, Jonas Bajaj, Nakul Gosada, Manmohan Borne, Kirk D. |
| author_role | aut aut aut |
| author_sort | Christensen, Jonas |
| author_variant | j c jc n b nb m g mg |
| building | Verbundindex |
| bvnumber | localTUM |
| collection | ZDB-30-ORH |
| ctrlnum | (DE-627-1)102205922 (DE-599)KEP102205922 (ORHE)9781804618127 |
| dewey-full | 005.13/3 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 005 - Computer programming, programs, data, security |
| dewey-raw | 005.13/3 |
| dewey-search | 005.13/3 |
| dewey-sort | 15.13 13 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| edition | 1st edition. |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>04687nam a22004812 4500</leader><controlfield tag="001">ZDB-30-ORH-102205922</controlfield><controlfield tag="003">DE-627-1</controlfield><controlfield tag="005">20240404083421.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240404s2024 xx |||||o 00| ||eng c</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781804612415</subfield><subfield code="c">electronic bk.</subfield><subfield code="9">978-1-80461-241-5</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">1804612413</subfield><subfield code="c">electronic bk.</subfield><subfield code="9">1-80461-241-3</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781804618127</subfield><subfield code="9">978-1-80461-812-7</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627-1)102205922</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KEP102205922</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ORHE)9781804618127</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627-1)102205922</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">005.13/3</subfield><subfield code="2">23/eng/20240305</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Christensen, Jonas</subfield><subfield code="e">VerfasserIn</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">DATA-CENTRIC MACHINE LEARNING WITH PYTHON</subfield><subfield code="b">the ultimate guide to engineering and deploying high-quality models based on good data</subfield><subfield code="c">Jonas Christensen, Nakul Bajaj, Manmohan Gosada ; foreword by Kirk D. Borne</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">1st edition.</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Birmingham, UK</subfield><subfield code="b">Packt Publishing Ltd.</subfield><subfield code="c">2024</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 online resource</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Join the data-centric revolution and master the concepts, techniques, and algorithms shaping the future of AI and ML development, using Python Key Features Grasp the principles of data centricity and apply them to real-world scenarios Gain experience with quality data collection, labeling, and synthetic data creation using Python Develop essential skills for building reliable, responsible, and ethical machine learning solutions Purchase of the print or Kindle book includes a free PDF eBook Book Description In the rapidly advancing data-driven world where data quality is pivotal to the success of machine learning and artificial intelligence projects, this critically timed guide provides a rare, end-to-end overview of data-centric machine learning (DCML), along with hands-on applications of technical and non-technical approaches to generating deeper and more accurate datasets. This book will help you understand what data-centric ML/AI is and how it can help you to realize the potential of 'small data'. Delving into the building blocks of data-centric ML/AI, you'll explore the human aspects of data labeling, tackle ambiguity in labeling, and understand the role of synthetic data. From strategies to improve data collection to techniques for refining and augmenting datasets, you'll learn everything you need to elevate your data-centric practices. Through applied examples and insights for overcoming challenges, you'll get a roadmap for implementing data-centric ML/AI in diverse applications in Python. By the end of this book, you'll have developed a profound understanding of data-centric ML/AI and the proficiency to seamlessly integrate common data-centric approaches in the model development lifecycle to unlock the full potential of your machine learning projects by prioritizing data quality and reliability. What you will learn Understand the impact of input data quality compared to model selection and tuning Recognize the crucial role of subject-matter experts in effective model development Implement data cleaning, labeling, and augmentation best practices Explore common synthetic data generation techniques and their applications Apply synthetic data generation techniques using common Python packages Detect and mitigate bias in a dataset using best-practice techniques Understand the importance of reliability, responsibility, and ethical considerations in ML/AI Who this book is for This book is for data science professionals and machine learning enthusiasts looking to understand the concept of data-centricity, its benefits over a model-centric approach, and the practical application of a best-practice data-centric approach in their work. This book is also for other data professionals and senior leaders who want to explore the tools and techniques to improve data quality and create opportunities for small data ML/AI in their organizations.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Python (Computer program language)</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Data mining</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Apprentissage automatique</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Python (Langage de programmation)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Exploration de données (Informatique)</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Bajaj, Nakul</subfield><subfield code="e">VerfasserIn</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Gosada, Manmohan</subfield><subfield code="e">VerfasserIn</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Borne, Kirk D.</subfield><subfield code="e">MitwirkendeR</subfield><subfield code="4">ctb</subfield></datafield><datafield tag="776" ind1="1" ind2=" "><subfield code="z">1804618128</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="z">1804618128</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="l">TUM01</subfield><subfield code="p">ZDB-30-ORH</subfield><subfield code="q">TUM_PDA_ORH</subfield><subfield code="u">https://learning.oreilly.com/library/view/-/9781804618127/?ar</subfield><subfield code="m">X:ORHE</subfield><subfield code="x">Aggregator</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-ORH</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">BO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-ORH</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-91</subfield></datafield></record></collection> |
| id | ZDB-30-ORH-102205922 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-18T08:48:50Z |
| institution | BVB |
| isbn | 9781804612415 1804612413 9781804618127 |
| language | English |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM |
| owner_facet | DE-91 DE-BY-TUM |
| physical | 1 online resource |
| psigel | ZDB-30-ORH |
| publishDate | 2024 |
| publishDateSearch | 2024 |
| publishDateSort | 2024 |
| publisher | Packt Publishing Ltd. |
| record_format | marc |
| spelling | Christensen, Jonas VerfasserIn aut DATA-CENTRIC MACHINE LEARNING WITH PYTHON the ultimate guide to engineering and deploying high-quality models based on good data Jonas Christensen, Nakul Bajaj, Manmohan Gosada ; foreword by Kirk D. Borne 1st edition. Birmingham, UK Packt Publishing Ltd. 2024 1 online resource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Join the data-centric revolution and master the concepts, techniques, and algorithms shaping the future of AI and ML development, using Python Key Features Grasp the principles of data centricity and apply them to real-world scenarios Gain experience with quality data collection, labeling, and synthetic data creation using Python Develop essential skills for building reliable, responsible, and ethical machine learning solutions Purchase of the print or Kindle book includes a free PDF eBook Book Description In the rapidly advancing data-driven world where data quality is pivotal to the success of machine learning and artificial intelligence projects, this critically timed guide provides a rare, end-to-end overview of data-centric machine learning (DCML), along with hands-on applications of technical and non-technical approaches to generating deeper and more accurate datasets. This book will help you understand what data-centric ML/AI is and how it can help you to realize the potential of 'small data'. Delving into the building blocks of data-centric ML/AI, you'll explore the human aspects of data labeling, tackle ambiguity in labeling, and understand the role of synthetic data. From strategies to improve data collection to techniques for refining and augmenting datasets, you'll learn everything you need to elevate your data-centric practices. Through applied examples and insights for overcoming challenges, you'll get a roadmap for implementing data-centric ML/AI in diverse applications in Python. By the end of this book, you'll have developed a profound understanding of data-centric ML/AI and the proficiency to seamlessly integrate common data-centric approaches in the model development lifecycle to unlock the full potential of your machine learning projects by prioritizing data quality and reliability. What you will learn Understand the impact of input data quality compared to model selection and tuning Recognize the crucial role of subject-matter experts in effective model development Implement data cleaning, labeling, and augmentation best practices Explore common synthetic data generation techniques and their applications Apply synthetic data generation techniques using common Python packages Detect and mitigate bias in a dataset using best-practice techniques Understand the importance of reliability, responsibility, and ethical considerations in ML/AI Who this book is for This book is for data science professionals and machine learning enthusiasts looking to understand the concept of data-centricity, its benefits over a model-centric approach, and the practical application of a best-practice data-centric approach in their work. This book is also for other data professionals and senior leaders who want to explore the tools and techniques to improve data quality and create opportunities for small data ML/AI in their organizations. Machine learning Python (Computer program language) Data mining Apprentissage automatique Python (Langage de programmation) Exploration de données (Informatique) Bajaj, Nakul VerfasserIn aut Gosada, Manmohan VerfasserIn aut Borne, Kirk D. MitwirkendeR ctb 1804618128 Erscheint auch als Druck-Ausgabe 1804618128 TUM01 ZDB-30-ORH TUM_PDA_ORH https://learning.oreilly.com/library/view/-/9781804618127/?ar X:ORHE Aggregator lizenzpflichtig Volltext |
| spellingShingle | Christensen, Jonas Bajaj, Nakul Gosada, Manmohan DATA-CENTRIC MACHINE LEARNING WITH PYTHON the ultimate guide to engineering and deploying high-quality models based on good data Machine learning Python (Computer program language) Data mining Apprentissage automatique Python (Langage de programmation) Exploration de données (Informatique) |
| title | DATA-CENTRIC MACHINE LEARNING WITH PYTHON the ultimate guide to engineering and deploying high-quality models based on good data |
| title_auth | DATA-CENTRIC MACHINE LEARNING WITH PYTHON the ultimate guide to engineering and deploying high-quality models based on good data |
| title_exact_search | DATA-CENTRIC MACHINE LEARNING WITH PYTHON the ultimate guide to engineering and deploying high-quality models based on good data |
| title_full | DATA-CENTRIC MACHINE LEARNING WITH PYTHON the ultimate guide to engineering and deploying high-quality models based on good data Jonas Christensen, Nakul Bajaj, Manmohan Gosada ; foreword by Kirk D. Borne |
| title_fullStr | DATA-CENTRIC MACHINE LEARNING WITH PYTHON the ultimate guide to engineering and deploying high-quality models based on good data Jonas Christensen, Nakul Bajaj, Manmohan Gosada ; foreword by Kirk D. Borne |
| title_full_unstemmed | DATA-CENTRIC MACHINE LEARNING WITH PYTHON the ultimate guide to engineering and deploying high-quality models based on good data Jonas Christensen, Nakul Bajaj, Manmohan Gosada ; foreword by Kirk D. Borne |
| title_short | DATA-CENTRIC MACHINE LEARNING WITH PYTHON |
| title_sort | data centric machine learning with python the ultimate guide to engineering and deploying high quality models based on good data |
| title_sub | the ultimate guide to engineering and deploying high-quality models based on good data |
| topic | Machine learning Python (Computer program language) Data mining Apprentissage automatique Python (Langage de programmation) Exploration de données (Informatique) |
| topic_facet | Machine learning Python (Computer program language) Data mining Apprentissage automatique Python (Langage de programmation) Exploration de données (Informatique) |
| url | https://learning.oreilly.com/library/view/-/9781804618127/?ar |
| work_keys_str_mv | AT christensenjonas datacentricmachinelearningwithpythontheultimateguidetoengineeringanddeployinghighqualitymodelsbasedongooddata AT bajajnakul datacentricmachinelearningwithpythontheultimateguidetoengineeringanddeployinghighqualitymodelsbasedongooddata AT gosadamanmohan datacentricmachinelearningwithpythontheultimateguidetoengineeringanddeployinghighqualitymodelsbasedongooddata AT bornekirkd datacentricmachinelearningwithpythontheultimateguidetoengineeringanddeployinghighqualitymodelsbasedongooddata |