Saved in:
| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Birmingham, UK
Packt Publishing
2018
|
| Edition: | Second edition. |
| Subjects: | |
| Links: | https://learning.oreilly.com/library/view/-/9781789347999/?ar |
| Summary: | Machine learning has gained tremendous popularity for its powerful and fast predictions with large datasets. However, the true forces behind its powerful output are the complex algorithms involving substantial statistical analysis that churn large datasets and generate substantial insight. This second edition of Machine Learning Algorithms walks you through prominent development outcomes that have taken place relating to machine learning algorithms, which constitute major contributions to the machine learning process and help you to strengthen and master statistical interpretation across the areas of supervised, semi-supervised, and reinforcement learning. Once the core concepts of an algorithm have been covered, you'll explore real-world examples based on the most diffused libraries, such as scikit-learn, NLTK, TensorFlow, and Keras. You will discover new topics such as principal component analysis (PCA), independent component analysis (ICA), Bayesian regression, discriminant analysis, advanced clustering, and gaussian mixture. By the end of this book, you will have studied machine learning algorithms and be able to put them into production to make your machine learning applications more innovative. |
| Item Description: | Previous edition published: 2017. - Online resource; title from title page (Safari, viewed October 2, 2018) |
| Physical Description: | 1 Online-Ressource (1 volume) illustrations |
| ISBN: | 9781789345483 1789345480 9781789347999 |
Staff View
MARC
| LEADER | 00000cam a22000002c 4500 | ||
|---|---|---|---|
| 001 | ZDB-30-ORH-047727128 | ||
| 003 | DE-627-1 | ||
| 005 | 20240228120552.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 191023s2018 xx |||||o 00| ||eng c | ||
| 020 | |a 9781789345483 |9 978-1-78934-548-3 | ||
| 020 | |a 1789345480 |9 1-78934-548-0 | ||
| 020 | |a 9781789347999 |9 978-1-78934-799-9 | ||
| 035 | |a (DE-627-1)047727128 | ||
| 035 | |a (DE-599)KEP047727128 | ||
| 035 | |a (ORHE)9781789347999 | ||
| 035 | |a (DE-627-1)047727128 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rda | ||
| 041 | |a eng | ||
| 082 | 0 | |a 006.31 |2 23 | |
| 100 | 1 | |a Bonaccorso, Giuseppe |e VerfasserIn |4 aut | |
| 245 | 1 | 0 | |a Machine learning algorithms |b popular algorithms for data science and machine learning |c Giuseppe Bonaccorso |
| 250 | |a Second edition. | ||
| 264 | 1 | |a Birmingham, UK |b Packt Publishing |c 2018 | |
| 300 | |a 1 Online-Ressource (1 volume) |b illustrations | ||
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Previous edition published: 2017. - Online resource; title from title page (Safari, viewed October 2, 2018) | ||
| 520 | |a Machine learning has gained tremendous popularity for its powerful and fast predictions with large datasets. However, the true forces behind its powerful output are the complex algorithms involving substantial statistical analysis that churn large datasets and generate substantial insight. This second edition of Machine Learning Algorithms walks you through prominent development outcomes that have taken place relating to machine learning algorithms, which constitute major contributions to the machine learning process and help you to strengthen and master statistical interpretation across the areas of supervised, semi-supervised, and reinforcement learning. Once the core concepts of an algorithm have been covered, you'll explore real-world examples based on the most diffused libraries, such as scikit-learn, NLTK, TensorFlow, and Keras. You will discover new topics such as principal component analysis (PCA), independent component analysis (ICA), Bayesian regression, discriminant analysis, advanced clustering, and gaussian mixture. By the end of this book, you will have studied machine learning algorithms and be able to put them into production to make your machine learning applications more innovative. | ||
| 650 | 0 | |a Machine learning | |
| 650 | 0 | |a Computer algorithms | |
| 650 | 2 | |a Algorithms | |
| 650 | 2 | |a Machine Learning | |
| 650 | 4 | |a Apprentissage automatique | |
| 650 | 4 | |a Algorithmes | |
| 650 | 4 | |a algorithms | |
| 650 | 4 | |a Computer algorithms | |
| 650 | 4 | |a Machine learning | |
| 966 | 4 | 0 | |l DE-91 |p ZDB-30-ORH |q TUM_PDA_ORH |u https://learning.oreilly.com/library/view/-/9781789347999/?ar |m X:ORHE |x Aggregator |z lizenzpflichtig |3 Volltext |
| 912 | |a ZDB-30-ORH | ||
| 912 | |a ZDB-30-ORH | ||
| 951 | |a BO | ||
| 912 | |a ZDB-30-ORH | ||
| 049 | |a DE-91 | ||
Record in the Search Index
| DE-BY-TUM_katkey | ZDB-30-ORH-047727128 |
|---|---|
| _version_ | 1835903175171768321 |
| adam_text | |
| any_adam_object | |
| author | Bonaccorso, Giuseppe |
| author_facet | Bonaccorso, Giuseppe |
| author_role | aut |
| author_sort | Bonaccorso, Giuseppe |
| author_variant | g b gb |
| building | Verbundindex |
| bvnumber | localTUM |
| collection | ZDB-30-ORH |
| ctrlnum | (DE-627-1)047727128 (DE-599)KEP047727128 (ORHE)9781789347999 |
| dewey-full | 006.31 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.31 |
| dewey-search | 006.31 |
| dewey-sort | 16.31 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| edition | Second edition. |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>02851cam a22004812c 4500</leader><controlfield tag="001">ZDB-30-ORH-047727128</controlfield><controlfield tag="003">DE-627-1</controlfield><controlfield tag="005">20240228120552.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">191023s2018 xx |||||o 00| ||eng c</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781789345483</subfield><subfield code="9">978-1-78934-548-3</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">1789345480</subfield><subfield code="9">1-78934-548-0</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781789347999</subfield><subfield code="9">978-1-78934-799-9</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627-1)047727128</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KEP047727128</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ORHE)9781789347999</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627-1)047727128</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">006.31</subfield><subfield code="2">23</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Bonaccorso, Giuseppe</subfield><subfield code="e">VerfasserIn</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Machine learning algorithms</subfield><subfield code="b">popular algorithms for data science and machine learning</subfield><subfield code="c">Giuseppe Bonaccorso</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">Second edition.</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Birmingham, UK</subfield><subfield code="b">Packt Publishing</subfield><subfield code="c">2018</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (1 volume)</subfield><subfield code="b">illustrations</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="500" ind1=" " ind2=" "><subfield code="a">Previous edition published: 2017. - Online resource; title from title page (Safari, viewed October 2, 2018)</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Machine learning has gained tremendous popularity for its powerful and fast predictions with large datasets. However, the true forces behind its powerful output are the complex algorithms involving substantial statistical analysis that churn large datasets and generate substantial insight. This second edition of Machine Learning Algorithms walks you through prominent development outcomes that have taken place relating to machine learning algorithms, which constitute major contributions to the machine learning process and help you to strengthen and master statistical interpretation across the areas of supervised, semi-supervised, and reinforcement learning. Once the core concepts of an algorithm have been covered, you'll explore real-world examples based on the most diffused libraries, such as scikit-learn, NLTK, TensorFlow, and Keras. You will discover new topics such as principal component analysis (PCA), independent component analysis (ICA), Bayesian regression, discriminant analysis, advanced clustering, and gaussian mixture. By the end of this book, you will have studied machine learning algorithms and be able to put them into production to make your machine learning applications more innovative.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Computer algorithms</subfield></datafield><datafield tag="650" ind1=" " ind2="2"><subfield code="a">Algorithms</subfield></datafield><datafield tag="650" ind1=" " ind2="2"><subfield code="a">Machine Learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Apprentissage automatique</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Algorithmes</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">algorithms</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computer algorithms</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield></datafield><datafield tag="966" ind1="4" ind2="0"><subfield code="l">DE-91</subfield><subfield code="p">ZDB-30-ORH</subfield><subfield code="q">TUM_PDA_ORH</subfield><subfield code="u">https://learning.oreilly.com/library/view/-/9781789347999/?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="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-047727128 |
| illustrated | Illustrated |
| indexdate | 2025-06-25T12:15:05Z |
| institution | BVB |
| isbn | 9781789345483 1789345480 9781789347999 |
| language | English |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM |
| owner_facet | DE-91 DE-BY-TUM |
| physical | 1 Online-Ressource (1 volume) illustrations |
| psigel | ZDB-30-ORH TUM_PDA_ORH ZDB-30-ORH |
| publishDate | 2018 |
| publishDateSearch | 2018 |
| publishDateSort | 2018 |
| publisher | Packt Publishing |
| record_format | marc |
| spelling | Bonaccorso, Giuseppe VerfasserIn aut Machine learning algorithms popular algorithms for data science and machine learning Giuseppe Bonaccorso Second edition. Birmingham, UK Packt Publishing 2018 1 Online-Ressource (1 volume) illustrations Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Previous edition published: 2017. - Online resource; title from title page (Safari, viewed October 2, 2018) Machine learning has gained tremendous popularity for its powerful and fast predictions with large datasets. However, the true forces behind its powerful output are the complex algorithms involving substantial statistical analysis that churn large datasets and generate substantial insight. This second edition of Machine Learning Algorithms walks you through prominent development outcomes that have taken place relating to machine learning algorithms, which constitute major contributions to the machine learning process and help you to strengthen and master statistical interpretation across the areas of supervised, semi-supervised, and reinforcement learning. Once the core concepts of an algorithm have been covered, you'll explore real-world examples based on the most diffused libraries, such as scikit-learn, NLTK, TensorFlow, and Keras. You will discover new topics such as principal component analysis (PCA), independent component analysis (ICA), Bayesian regression, discriminant analysis, advanced clustering, and gaussian mixture. By the end of this book, you will have studied machine learning algorithms and be able to put them into production to make your machine learning applications more innovative. Machine learning Computer algorithms Algorithms Machine Learning Apprentissage automatique Algorithmes algorithms |
| spellingShingle | Bonaccorso, Giuseppe Machine learning algorithms popular algorithms for data science and machine learning Machine learning Computer algorithms Algorithms Machine Learning Apprentissage automatique Algorithmes algorithms |
| title | Machine learning algorithms popular algorithms for data science and machine learning |
| title_auth | Machine learning algorithms popular algorithms for data science and machine learning |
| title_exact_search | Machine learning algorithms popular algorithms for data science and machine learning |
| title_full | Machine learning algorithms popular algorithms for data science and machine learning Giuseppe Bonaccorso |
| title_fullStr | Machine learning algorithms popular algorithms for data science and machine learning Giuseppe Bonaccorso |
| title_full_unstemmed | Machine learning algorithms popular algorithms for data science and machine learning Giuseppe Bonaccorso |
| title_short | Machine learning algorithms |
| title_sort | machine learning algorithms popular algorithms for data science and machine learning |
| title_sub | popular algorithms for data science and machine learning |
| topic | Machine learning Computer algorithms Algorithms Machine Learning Apprentissage automatique Algorithmes algorithms |
| topic_facet | Machine learning Computer algorithms Algorithms Machine Learning Apprentissage automatique Algorithmes algorithms |
| work_keys_str_mv | AT bonaccorsogiuseppe machinelearningalgorithmspopularalgorithmsfordatascienceandmachinelearning |