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Author Ekaba Bisong shows you how machine learning tools and techniques are used to predict or classify events based on a set of interactions between variables known as features or attributes in a particular dataset. He teaches you how deep learning extends the machine learning algorithm of neural networks to learn complex tasks that are difficult for computers to perform, such as recognizing faces and understanding languages. And you will know how to leverage cloud computing to accelerate data science and machine learning deployments.
Building Machine Learning and Deep Learning Models on Google Cloud Platform is divided into eight parts that cover the fundamentals of machine learning and deep learning, the concept of data science and cloud services, programming for data science using the Python stack, Google Cloud Platform (GCP) infrastructure and products, advanced analytics on GCP, and deploying end-to-end machine learning solution pipelines on GCP.
Understand the principles and fundamentals of machine learning and deep learning, the algorithms, how to use them, when to use them, and how to interpret your results
Know the programming concepts relevant to machine and deep learning design and development using the Python stack
Build and interpret machine and deep learning models
Use Google Cloud Platform tools and services to develop and deploy large-scale machine learning and deep learning products
Be aware of the different facets and design choices to consider when modeling a learning problem
Productionalize machine learning models into software products
Who This Book Is For
Beginners to the practice of data science and applied machine learning, data scientists at all levels, machine learning engineers, Google Cloud Platform data engineers/architects, and software developers
Author Ekaba Bisong shows you how machine learning tools and techniques are used to predict or classify events based on a set of interactions between variables known as features or attributes in a particular dataset. He teaches you how deep learning extends the machine learning algorithm of neural networks to learn complex tasks that are difficult for computers to perform, such as recognizing faces and understanding languages. And you will know how to leverage cloud computing to accelerate data science and machine learning deployments.
Building Machine Learning and Deep Learning Models on Google Cloud Platform is divided into eight parts that cover the fundamentals of machine learning and deep learning, the concept of data science and cloud services, programming for data science using the Python stack, Google Cloud Platform (GCP) infrastructure and products, advanced analytics on GCP, and deploying end-to-end machine learning solution pipelines on GCP.
Understand the principles and fundamentals of machine learning and deep learning, the algorithms, how to use them, when to use them, and how to interpret your results
Know the programming concepts relevant to machine and deep learning design and development using the Python stack
Build and interpret machine and deep learning models
Use Google Cloud Platform tools and services to develop and deploy large-scale machine learning and deep learning products
Be aware of the different facets and design choices to consider when modeling a learning problem
Productionalize machine learning models into software products
Who This Book Is For
Beginners to the practice of data science and applied machine learning, data scientists at all levels, machine learning engineers, Google Cloud Platform data engineers/architects, and software developers
Ekaba Bisong is a Data Science Lead at T4G. He previously worked as a data scientist/data engineer at Pythian. In addition, he maintains a relationship with the Intelligent Systems Labs at Carleton University with a research focus on learning systems (encompassing learning automata and reinforcement learning), machine learning, and deep learning. He is a Google Certified Professional Data Engineer and a Google Developer Expert in machine learning.
Pedagogically structured to make the knowledge of machine learning, deep learning, data science, and cloud computing easily accessible
Equips you with skills to build and deploy large-scale learning models on Google Cloud Platform
Covers the programming skills necessary for machine learning and deep learning modeling using the Python stack
Includes packages such as Numpy, Pandas, Matplotlib, Scikit-learn, Tensorflow, and Keras
| Erscheinungsjahr: | 2019 |
|---|---|
| Genre: | Importe, Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Inhalt: |
xxix
709 S. 4 s/w Illustr. 344 farbige Illustr. 709 p. 348 illus. 344 illus. in color. |
| ISBN-13: | 9781484244692 |
| ISBN-10: | 1484244699 |
| Sprache: | Englisch |
| Herstellernummer: | 978-1-4842-4469-2 |
| Einband: | Kartoniert / Broschiert |
| Autor: | Bisong, Ekaba |
| Hersteller: |
Apress
Apress L.P. |
| Verantwortliche Person für die EU: | APress in Springer Science + Business Media, Heidelberger Platz 3, D-14197 Berlin, juergen.hartmann@springer.com |
| Maße: | 254 x 178 x 39 mm |
| Von/Mit: | Ekaba Bisong |
| Erscheinungsdatum: | 28.09.2019 |
| Gewicht: | 1,366 kg |
Ekaba Bisong is a Data Science Lead at T4G. He previously worked as a data scientist/data engineer at Pythian. In addition, he maintains a relationship with the Intelligent Systems Labs at Carleton University with a research focus on learning systems (encompassing learning automata and reinforcement learning), machine learning, and deep learning. He is a Google Certified Professional Data Engineer and a Google Developer Expert in machine learning.
Pedagogically structured to make the knowledge of machine learning, deep learning, data science, and cloud computing easily accessible
Equips you with skills to build and deploy large-scale learning models on Google Cloud Platform
Covers the programming skills necessary for machine learning and deep learning modeling using the Python stack
Includes packages such as Numpy, Pandas, Matplotlib, Scikit-learn, Tensorflow, and Keras
| Erscheinungsjahr: | 2019 |
|---|---|
| Genre: | Importe, Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Inhalt: |
xxix
709 S. 4 s/w Illustr. 344 farbige Illustr. 709 p. 348 illus. 344 illus. in color. |
| ISBN-13: | 9781484244692 |
| ISBN-10: | 1484244699 |
| Sprache: | Englisch |
| Herstellernummer: | 978-1-4842-4469-2 |
| Einband: | Kartoniert / Broschiert |
| Autor: | Bisong, Ekaba |
| Hersteller: |
Apress
Apress L.P. |
| Verantwortliche Person für die EU: | APress in Springer Science + Business Media, Heidelberger Platz 3, D-14197 Berlin, juergen.hartmann@springer.com |
| Maße: | 254 x 178 x 39 mm |
| Von/Mit: | Ekaba Bisong |
| Erscheinungsdatum: | 28.09.2019 |
| Gewicht: | 1,366 kg |
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