Low-code AI: a practical project-driven introduction to machine learning
Take a data-first and use-case-driven approach with Low-Code AI to understand machine learning and deep learning concepts. This hands-on guide presents three problem-focused ways to learn no-code ML using AutoML, low-code using BigQuery ML, and custom code using scikit-learn and Keras. In each case,...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Beijing ; Boston ; Farnham
O'Reilly
September 2023
|
| Ausgabe: | First edition |
| Schlagworte: | |
| Zusammenfassung: | Take a data-first and use-case-driven approach with Low-Code AI to understand machine learning and deep learning concepts. This hands-on guide presents three problem-focused ways to learn no-code ML using AutoML, low-code using BigQuery ML, and custom code using scikit-learn and Keras. In each case, you'll learn key ML concepts by using real-world datasets with realistic problems. Business and data analysts get a project-based introduction to ML/AI using a detailed, data-driven approach: loading and analyzing data; feeding data into an ML model; building, training, and testing; and deploying the model into production. Authors Michael Abel and Gwendolyn Stripling show you how to build machine learning models for retail, healthcare, financial services, energy, and telecommunications. You'll learn how to: Distinguish between structured and unstructured data and the challenges they present Visualize and analyze data Preprocess data for input into a machine learning model Differentiate between the regression and classification supervised learning models Compare different ML model types and architectures, from no code to low code to custom training Design, implement, and tune ML models Export data to a GitHub repository for data management and governance. |
| Beschreibung: | xiv, 309 Seiten Illustrationen 232 mm |
| ISBN: | 9781098146825 |
Internformat
MARC
| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV049495607 | ||
| 003 | DE-604 | ||
| 005 | 20240620 | ||
| 007 | t | ||
| 008 | 240110s2023 a||| |||| 00||| eng d | ||
| 020 | |a 9781098146825 |c pbk |9 978-1-098-14682-5 | ||
| 035 | |a (OCoLC)1424570126 | ||
| 035 | |a (DE-599)BVBBV049495607 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-29T | ||
| 082 | 0 | |a 006.3 |2 23 | |
| 100 | 1 | |a Stripling, Gwendolyn |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Low-code AI |b a practical project-driven introduction to machine learning |c Gwendolyn Stripling & Michael Abel |
| 250 | |a First edition | ||
| 264 | 1 | |a Beijing ; Boston ; Farnham |b O'Reilly |c September 2023 | |
| 300 | |a xiv, 309 Seiten |b Illustrationen |c 232 mm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 520 | 3 | |a Take a data-first and use-case-driven approach with Low-Code AI to understand machine learning and deep learning concepts. This hands-on guide presents three problem-focused ways to learn no-code ML using AutoML, low-code using BigQuery ML, and custom code using scikit-learn and Keras. In each case, you'll learn key ML concepts by using real-world datasets with realistic problems. Business and data analysts get a project-based introduction to ML/AI using a detailed, data-driven approach: loading and analyzing data; feeding data into an ML model; building, training, and testing; and deploying the model into production. Authors Michael Abel and Gwendolyn Stripling show you how to build machine learning models for retail, healthcare, financial services, energy, and telecommunications. You'll learn how to: Distinguish between structured and unstructured data and the challenges they present Visualize and analyze data Preprocess data for input into a machine learning model Differentiate between the regression and classification supervised learning models Compare different ML model types and architectures, from no code to low code to custom training Design, implement, and tune ML models Export data to a GitHub repository for data management and governance. | |
| 650 | 7 | |a COMPUTERS / Data Science / Machine Learning |2 bisacsh | |
| 650 | 7 | |a COMPUTERS / Business & Productivity Software / Business Intelligence |2 bisacsh | |
| 650 | 7 | |a COMPUTERS / Machine Theory |2 bisacsh | |
| 650 | 7 | |a COMPUTERS / Programming / Algorithms |2 bisacsh | |
| 650 | 7 | |a COMPUTERS / Artificial Intelligence / General |2 bisacsh | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Machine learning |x Industrial applications | |
| 650 | 4 | |a Electronic data processing | |
| 650 | 0 | 7 | |a Deep learning |0 (DE-588)1135597375 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |D s |
| 689 | 0 | 1 | |a Deep learning |0 (DE-588)1135597375 |D s |
| 689 | 0 | |5 DE-188 | |
| 700 | 1 | |a Abel, Michael |e Verfasser |4 aut | |
Datensatz im Suchindex
| _version_ | 1805078059476320256 |
|---|---|
| adam_text | |
| adam_txt | |
| any_adam_object | |
| any_adam_object_boolean | |
| author | Stripling, Gwendolyn Abel, Michael |
| author_facet | Stripling, Gwendolyn Abel, Michael |
| author_role | aut aut |
| author_sort | Stripling, Gwendolyn |
| author_variant | g s gs m a ma |
| building | Verbundindex |
| bvnumber | BV049495607 |
| ctrlnum | (OCoLC)1424570126 (DE-599)BVBBV049495607 |
| dewey-full | 006.3 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3 |
| dewey-search | 006.3 |
| dewey-sort | 16.3 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| edition | First edition |
| format | Book |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>00000nam a2200000 c 4500</leader><controlfield tag="001">BV049495607</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20240620</controlfield><controlfield tag="007">t</controlfield><controlfield tag="008">240110s2023 a||| |||| 00||| eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781098146825</subfield><subfield code="c">pbk</subfield><subfield code="9">978-1-098-14682-5</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1424570126</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV049495607</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-29T</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">006.3</subfield><subfield code="2">23</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Stripling, Gwendolyn</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Low-code AI</subfield><subfield code="b">a practical project-driven introduction to machine learning</subfield><subfield code="c">Gwendolyn Stripling & Michael Abel</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">First edition</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Beijing ; Boston ; Farnham</subfield><subfield code="b">O'Reilly</subfield><subfield code="c">September 2023</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">xiv, 309 Seiten</subfield><subfield code="b">Illustrationen</subfield><subfield code="c">232 mm</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1="3" ind2=" "><subfield code="a">Take a data-first and use-case-driven approach with Low-Code AI to understand machine learning and deep learning concepts. This hands-on guide presents three problem-focused ways to learn no-code ML using AutoML, low-code using BigQuery ML, and custom code using scikit-learn and Keras. In each case, you'll learn key ML concepts by using real-world datasets with realistic problems. Business and data analysts get a project-based introduction to ML/AI using a detailed, data-driven approach: loading and analyzing data; feeding data into an ML model; building, training, and testing; and deploying the model into production. Authors Michael Abel and Gwendolyn Stripling show you how to build machine learning models for retail, healthcare, financial services, energy, and telecommunications. You'll learn how to: Distinguish between structured and unstructured data and the challenges they present Visualize and analyze data Preprocess data for input into a machine learning model Differentiate between the regression and classification supervised learning models Compare different ML model types and architectures, from no code to low code to custom training Design, implement, and tune ML models Export data to a GitHub repository for data management and governance.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">COMPUTERS / Data Science / Machine Learning</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">COMPUTERS / Business & Productivity Software / Business Intelligence</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">COMPUTERS / Machine Theory</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">COMPUTERS / Programming / Algorithms</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">COMPUTERS / Artificial Intelligence / General</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield><subfield code="x">Industrial applications</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Electronic data processing</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Deep learning</subfield><subfield code="0">(DE-588)1135597375</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Maschinelles Lernen</subfield><subfield code="0">(DE-588)4193754-5</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Maschinelles Lernen</subfield><subfield code="0">(DE-588)4193754-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Deep learning</subfield><subfield code="0">(DE-588)1135597375</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-188</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Abel, Michael</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield></record></collection> |
| id | DE-604.BV049495607 |
| illustrated | Illustrated |
| index_date | 2024-07-03T23:20:23Z |
| indexdate | 2024-07-20T06:23:03Z |
| institution | BVB |
| isbn | 9781098146825 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034840837 |
| oclc_num | 1424570126 |
| open_access_boolean | |
| owner | DE-29T |
| owner_facet | DE-29T |
| physical | xiv, 309 Seiten Illustrationen 232 mm |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | O'Reilly |
| record_format | marc |
| spelling | Stripling, Gwendolyn Verfasser aut Low-code AI a practical project-driven introduction to machine learning Gwendolyn Stripling & Michael Abel First edition Beijing ; Boston ; Farnham O'Reilly September 2023 xiv, 309 Seiten Illustrationen 232 mm txt rdacontent n rdamedia nc rdacarrier Take a data-first and use-case-driven approach with Low-Code AI to understand machine learning and deep learning concepts. This hands-on guide presents three problem-focused ways to learn no-code ML using AutoML, low-code using BigQuery ML, and custom code using scikit-learn and Keras. In each case, you'll learn key ML concepts by using real-world datasets with realistic problems. Business and data analysts get a project-based introduction to ML/AI using a detailed, data-driven approach: loading and analyzing data; feeding data into an ML model; building, training, and testing; and deploying the model into production. Authors Michael Abel and Gwendolyn Stripling show you how to build machine learning models for retail, healthcare, financial services, energy, and telecommunications. You'll learn how to: Distinguish between structured and unstructured data and the challenges they present Visualize and analyze data Preprocess data for input into a machine learning model Differentiate between the regression and classification supervised learning models Compare different ML model types and architectures, from no code to low code to custom training Design, implement, and tune ML models Export data to a GitHub repository for data management and governance. COMPUTERS / Data Science / Machine Learning bisacsh COMPUTERS / Business & Productivity Software / Business Intelligence bisacsh COMPUTERS / Machine Theory bisacsh COMPUTERS / Programming / Algorithms bisacsh COMPUTERS / Artificial Intelligence / General bisacsh Machine learning Machine learning Industrial applications Electronic data processing Deep learning (DE-588)1135597375 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s Deep learning (DE-588)1135597375 s DE-188 Abel, Michael Verfasser aut |
| spellingShingle | Stripling, Gwendolyn Abel, Michael Low-code AI a practical project-driven introduction to machine learning COMPUTERS / Data Science / Machine Learning bisacsh COMPUTERS / Business & Productivity Software / Business Intelligence bisacsh COMPUTERS / Machine Theory bisacsh COMPUTERS / Programming / Algorithms bisacsh COMPUTERS / Artificial Intelligence / General bisacsh Machine learning Machine learning Industrial applications Electronic data processing Deep learning (DE-588)1135597375 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)1135597375 (DE-588)4193754-5 |
| title | Low-code AI a practical project-driven introduction to machine learning |
| title_auth | Low-code AI a practical project-driven introduction to machine learning |
| title_exact_search | Low-code AI a practical project-driven introduction to machine learning |
| title_exact_search_txtP | Low-code AI a practical project-driven introduction to machine learning |
| title_full | Low-code AI a practical project-driven introduction to machine learning Gwendolyn Stripling & Michael Abel |
| title_fullStr | Low-code AI a practical project-driven introduction to machine learning Gwendolyn Stripling & Michael Abel |
| title_full_unstemmed | Low-code AI a practical project-driven introduction to machine learning Gwendolyn Stripling & Michael Abel |
| title_short | Low-code AI |
| title_sort | low code ai a practical project driven introduction to machine learning |
| title_sub | a practical project-driven introduction to machine learning |
| topic | COMPUTERS / Data Science / Machine Learning bisacsh COMPUTERS / Business & Productivity Software / Business Intelligence bisacsh COMPUTERS / Machine Theory bisacsh COMPUTERS / Programming / Algorithms bisacsh COMPUTERS / Artificial Intelligence / General bisacsh Machine learning Machine learning Industrial applications Electronic data processing Deep learning (DE-588)1135597375 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | COMPUTERS / Data Science / Machine Learning COMPUTERS / Business & Productivity Software / Business Intelligence COMPUTERS / Machine Theory COMPUTERS / Programming / Algorithms COMPUTERS / Artificial Intelligence / General Machine learning Machine learning Industrial applications Electronic data processing Deep learning Maschinelles Lernen |
| work_keys_str_mv | AT striplinggwendolyn lowcodeaiapracticalprojectdrivenintroductiontomachinelearning AT abelmichael lowcodeaiapracticalprojectdrivenintroductiontomachinelearning |