Introducing machine learning:
"Machine learning offers immense opportunities, and Introducing Machine Learning delivers practical knowledge to make the most of them. Dino and Francesco Esposito start with a quick overview of the foundations of artificial intelligence and the basic steps of any machine learning project. Next...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
[Redmond]
Microsoft Press
[2020]
[New York] Pearson Education, Inc. |
| Series: | Professional
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Summary: | "Machine learning offers immense opportunities, and Introducing Machine Learning delivers practical knowledge to make the most of them. Dino and Francesco Esposito start with a quick overview of the foundations of artificial intelligence and the basic steps of any machine learning project. Next, they introduce Microsoft's powerful ML.NET library, including capabilities for data processing, training, and evaluation. They present families of algorithms that can be trained to solve real-life problems, as well as deep learning techniques utilizing neural networks. The authors conclude by introducing valuable runtime services available through the Azure cloud platform and consider the long-term business vision for machine learning." -- |
| Item Description: | Includes index |
| Physical Description: | xxvi, 365 Seiten Illustrationen, Diagramme 24 cm |
| ISBN: | 9780135565667 0135565669 |
Staff View
MARC
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| 245 | 1 | 0 | |a Introducing machine learning |c Dino Esposito, Francesco Esposito |
| 264 | 1 | |a [Redmond] |b Microsoft Press |c [2020] | |
| 264 | 1 | |a [New York] |b Pearson Education, Inc. | |
| 300 | |a xxvi, 365 Seiten |b Illustrationen, Diagramme |c 24 cm | ||
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| 505 | 8 | |a How humans learn -- Intelligent software -- Mapping problems and algorithms -- General steps for a machine learning solution -- The data factor -- The .NET way -- Implementing the ML.NET pipeline -- ML.NET tasks and algorithms -- Math foundations of machine learning -- Metrics of machine learning -- How to make simple predictions: linear regression -- How to make complex predictions and decisions: trees -- How to make better decisions: ensemble methods -- Probabilistic methods: Naive Bayes -- How to group data: classification and clustering -- Feed-forward neural networks -- Design of a neural network -- Other types of neural networks -- Sentiment analysis: an end-to-end solution -- AI cloud services for the real world -- The business perception of AI. | |
| 520 | 3 | |a "Machine learning offers immense opportunities, and Introducing Machine Learning delivers practical knowledge to make the most of them. Dino and Francesco Esposito start with a quick overview of the foundations of artificial intelligence and the basic steps of any machine learning project. Next, they introduce Microsoft's powerful ML.NET library, including capabilities for data processing, training, and evaluation. They present families of algorithms that can be trained to solve real-life problems, as well as deep learning techniques utilizing neural networks. The authors conclude by introducing valuable runtime services available through the Azure cloud platform and consider the long-term business vision for machine learning." -- | |
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Record in the Search Index
| DE-BY-862_location | 2000 |
|---|---|
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| DE-BY-FWS_katkey | 857865 |
| DE-BY-FWS_media_number | 083000518547 |
| _version_ | 1824553947858206720 |
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| author | Esposito, Dino 1965- Esposito, Francesco |
| author_GND | (DE-588)131792423 |
| author_facet | Esposito, Dino 1965- Esposito, Francesco |
| author_role | aut aut |
| author_sort | Esposito, Dino 1965- |
| author_variant | d e de f e fe |
| building | Verbundindex |
| bvnumber | BV046707271 |
| classification_rvk | ST 300 |
| contents | How humans learn -- Intelligent software -- Mapping problems and algorithms -- General steps for a machine learning solution -- The data factor -- The .NET way -- Implementing the ML.NET pipeline -- ML.NET tasks and algorithms -- Math foundations of machine learning -- Metrics of machine learning -- How to make simple predictions: linear regression -- How to make complex predictions and decisions: trees -- How to make better decisions: ensemble methods -- Probabilistic methods: Naive Bayes -- How to group data: classification and clustering -- Feed-forward neural networks -- Design of a neural network -- Other types of neural networks -- Sentiment analysis: an end-to-end solution -- AI cloud services for the real world -- The business perception of AI. |
| ctrlnum | (OCoLC)1164605526 (DE-599)BVBBV046707271 |
| discipline | Informatik |
| discipline_str_mv | Informatik |
| format | Book |
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| id | DE-604.BV046707271 |
| illustrated | Illustrated |
| index_date | 2024-07-03T14:30:12Z |
| indexdate | 2025-02-20T06:43:58Z |
| institution | BVB |
| isbn | 9780135565667 0135565669 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032117727 |
| oclc_num | 1164605526 |
| open_access_boolean | |
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| owner_facet | DE-29T DE-1050 DE-898 DE-BY-UBR DE-862 DE-BY-FWS DE-355 DE-BY-UBR DE-20 |
| physical | xxvi, 365 Seiten Illustrationen, Diagramme 24 cm |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | Microsoft Press Pearson Education, Inc. |
| record_format | marc |
| series2 | Professional |
| spellingShingle | Esposito, Dino 1965- Esposito, Francesco Introducing machine learning How humans learn -- Intelligent software -- Mapping problems and algorithms -- General steps for a machine learning solution -- The data factor -- The .NET way -- Implementing the ML.NET pipeline -- ML.NET tasks and algorithms -- Math foundations of machine learning -- Metrics of machine learning -- How to make simple predictions: linear regression -- How to make complex predictions and decisions: trees -- How to make better decisions: ensemble methods -- Probabilistic methods: Naive Bayes -- How to group data: classification and clustering -- Feed-forward neural networks -- Design of a neural network -- Other types of neural networks -- Sentiment analysis: an end-to-end solution -- AI cloud services for the real world -- The business perception of AI. Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4193754-5 |
| title | Introducing machine learning |
| title_auth | Introducing machine learning |
| title_exact_search | Introducing machine learning |
| title_exact_search_txtP | Introducing machine learning |
| title_full | Introducing machine learning Dino Esposito, Francesco Esposito |
| title_fullStr | Introducing machine learning Dino Esposito, Francesco Esposito |
| title_full_unstemmed | Introducing machine learning Dino Esposito, Francesco Esposito |
| title_short | Introducing machine learning |
| title_sort | introducing machine learning |
| topic | Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Maschinelles Lernen |
| url | https://www.gbv.de/dms/ilmenau/toc/1698942729.PDF |
| work_keys_str_mv | AT espositodino introducingmachinelearning AT espositofrancesco introducingmachinelearning |
Indexes
THWS Schweinfurt Zentralbibliothek Lesesaal
| Call Number: |
2000 ST 300 E77 |
|---|---|
| Copy 1 | ausleihbar Checked out – Due: 26.06.2025 Recall This |