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Google Cloud Training

Questo corso presenta le offerte di intelligenza artificiale (IA) e machine learning (ML) su Google Cloud che supportano il ciclo di vita dai dati all'IA attraverso gli elementi di base dell'IA, lo sviluppo dell'IA e le soluzioni per l'IA. Esplora le tecnologie, i prodotti e gli strumenti disponibili per creare un modello ML, una pipeline ML e un progetto di IA generativa in base ai diversi obiettivi degli utenti, tra cui data scientist, sviluppatori di IA e ML engineer.

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What's inside

Syllabus

Introduzione
Questo modulo copre l'obiettivo del corso di aiutare chi partecipa a esplorare gli strumenti di sviluppo IA su Google Cloud. Fornisce inoltre una panoramica della struttura del corso, che si basa su un framework IA a tre livelli su Google Cloud.
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Elementi di base dell'IA
Questo modulo è incentrato sugli elementi di base dell'IA, inclusa l'infrastruttura cloud, come computing e archiviazione. Illustra inoltre i principali dati e prodotti di sviluppo IA disponibili su Google Cloud. Infine, dimostra come utilizzare BigQuery ML per creare un modello ML, che consente la transizione dai dati all'IA.
Opzioni di sviluppo IA
Questo modulo esplora le varie opzioni per lo sviluppo di un progetto ML su Google Cloud, da soluzioni già pronte come API preaddestrate, a soluzioni no-code e low-code come AutoML e soluzioni basate su codice come la formazione personalizzata. Confronta i vantaggi e gli svantaggi di ciascuna opzione per aiutare a decidere gli strumenti di sviluppo giusti.
Flusso di lavoro per lo sviluppo dell'IA
Questo modulo descrive il flusso di lavoro ML dalla preparazione dei dati, allo sviluppo del modello, alla pubblicazione del modello su Vertex AI. Illustra inoltre come convertire il flusso di lavoro in una pipeline automatizzata utilizzando Vertex AI Pipelines.
IA generativa
Questo modulo illustra l'IA generativa, i progressi più recenti nel campo dell'IA e la tecnologia che la alimenta: i modelli linguistici di grandi dimensioni (LLM). Esplora inoltre diversi strumenti di sviluppo dell'IA generativa su Google Cloud, come Generative AI Studio e Model Garden. Infine, descrive le soluzioni per l'IA e le funzionalità incorporate di IA generativa
Riepilogo
Questo modulo fornisce un riepilogo dell'intero corso coprendo i concetti, gli strumenti, le tecnologie e i prodotti più importanti.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Addresse gli elementi di base dell'IA, come l'infrastruttura cloud e i principali dati disponibili su Google Cloud
Esplora le opzioni di sviluppo IA, da soluzioni già pronte come API preaddestrate a soluzioni basate su codice come la formazione personalizzata
Descrive il flusso di lavoro ML end-to-end, dalla preparazione dei dati alla pubblicazione del modello su Vertex AI
Illustra l'IA generativa, i progressi più recenti nel campo dell'IA e gli strumenti disponibili per svilupparla su Google Cloud
Esplora le soluzioni e le funzionalità integrate di IA generativa su Google Cloud
Fornisce un riepilogo chiaro dei concetti e degli strumenti chiave

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Introduction to AI and Machine Learning on GC - Italiano with these activities:
Organizza e rivedi i materiali del corso
L'organizzazione e la revisione dei materiali del corso ti aiuteranno a consolidare le tue conoscenze e a prepararti al meglio per gli esami.
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  • Raccogli tutti i tuoi materiali del corso
  • Organizza i materiali in una cartella o in un software di gestione dei file
  • Rivedi regolarmente i materiali del corso
Segui un tutorial su come utilizzare BigQuery ML per la previsione
Seguire un tutorial su come utilizzare BigQuery ML per la previsione può aiutarti a comprendere meglio i concetti di base dell'IA e le applicazioni pratiche del machine learning.
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  • Trova un tutorial sul sito web di Google Cloud
  • Follow the steps in the tutorial to create a BigQuery ML model
  • Usa il modello per fare previsioni sui dati di esempio
Partecipa a un workshop su Google Cloud AI Platform
Partecipare a un workshop su Google Cloud AI Platform ti consentirà di saperne di più sulle funzionalità e sui benefici della piattaforma, nonché di interagire con esperti del settore.
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  • Trova un workshop su Google Cloud AI Platform
  • Registrati al workshop
  • Partecipa al workshop
Four other activities
Expand to see all activities and additional details
Show all seven activities
Pratica con Vertex AI AutoML
La pratica con Vertex AI AutoML può migliorare la tua comprensione dei concetti di apprendimento automatico senza codice e dei suoi benefici.
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  • Crea un account Vertex AI
  • Import un set di dati nel tuo account Vertex AI
  • Crea un modello AutoML utilizzando il set di dati importato
  • Valuta le prestazioni del modello addestrato
Costruisci una pipeline ML utilizzando Vertex AI Pipelines
La costruzione di una pipeline ML utilizzando Vertex AI Pipelines rafforzerà la tua comprensione del processo di sviluppo di IA e ti aiuterà a creare flussi di lavoro ML automatizzati.
Browse courses on Vertex AI Pipelines
Show steps
  • Crea un account Vertex AI
  • Crea un set di dati nella tua istanza Vertex AI
  • Crea un modello ML utilizzando uno dei metodi illustrati nel corso
  • Crea una pipeline Vertex AI utilizzando il tuo set di dati e modello per automatizzare il flusso di lavoro ML
Diventa mentor per altri studenti del corso
Diventare mentor per altri studenti del corso ti consentirà di rivedere e rinforzare le tue conoscenze, oltre a fornire supporto e guida ai tuoi studenti.
Show steps
  • Contatta l'istruttore del corso per esprimere interesse nel diventare un mentore
  • Incontra i tuoi studenti e discuti i loro obiettivi
  • Fornisci supporto e guida regolari ai tuoi studenti
Crea una raccolta di risorse sull'IA generativa
La creazione di una raccolta di risorse sull'IA generativa ti aiuterà a organizzare e approfondire le tue conoscenze su questo argomento in rapida evoluzione.
Browse courses on Machine Learning
Show steps
  • Cerca e seleziona articoli, tutorial e video sull'IA generativa
  • Organizza le risorse in una raccolta o cartella
  • Condividi la tua raccolta con altri studenti o colleghi

Career center

Learners who complete Introduction to AI and Machine Learning on GC - Italiano will develop knowledge and skills that may be useful to these careers:
Data Scientist
Data Scientists use their understanding of AI and ML to help organizations make data-driven decisions. This course can help you become a Data Scientist by providing you with the skills you need to collect, clean, and analyze data, as well as build and deploy ML models. You will also learn about the ethical implications of AI and ML, which is an important consideration for Data Scientists.
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, developing, and deploying ML models. This course can help you become a Machine Learning Engineer by providing you with the skills you need to build and deploy ML models on Google Cloud. You will also learn about the different types of ML models and how to choose the right model for your needs.
Software Engineer
Software Engineers who work in AI and ML use their knowledge of programming languages and software development to build and deploy AI and ML systems. This course can help you become an AI or ML Software Engineer by providing you with the skills you need to develop and deploy AI and ML models on Google Cloud. You will also learn about the different types of AI and ML models and how to choose the right model for your needs.
Data Analyst
Data Analysts use their understanding of AI and ML to help organizations make data-driven decisions. This course can help you become a Data Analyst by providing you with the skills you need to collect, clean, and analyze data. You will also learn about the different types of data analysis techniques and how to use them to solve business problems.
Business Analyst
Business Analysts use their understanding of AI and ML to help organizations make data-driven decisions. This course can help you become a Business Analyst by providing you with the skills you need to collect, clean, and analyze data. You will also learn about the different types of data analysis techniques and how to use them to solve business problems.
Project Manager
Project Managers use their understanding of AI and ML to manage AI and ML projects. This course can help you become a Project Manager by providing you with the skills you need to plan, execute, and deliver AI and ML projects. You will also learn about the different types of AI and ML projects and how to manage them successfully.
Product Manager
Product Managers use their understanding of AI and ML to develop and launch new products and services. This course can help you become a Product Manager by providing you with the skills you need to understand the market, identify customer needs, and develop and launch new products and services.
Consultant
Consultants use their understanding of AI and ML to help organizations solve business problems. This course can help you become a Consultant by providing you with the skills you need to understand business problems, develop solutions, and communicate your findings to clients. You will also learn about the different types of consulting services and how to market your services to potential clients.
Entrepreneur
Entrepreneurs use their understanding of AI and ML to develop and launch new businesses. This course can help you become an Entrepreneur by providing you with the skills you need to identify business opportunities, develop business plans, and launch new businesses. You will also learn about the different types of businesses you can start and how to get funding for your business.
Teacher
Teachers use their understanding of AI and ML to teach students about AI and ML. This course may be useful for Teachers who want to learn more about AI and ML so that they can teach their students about these topics. You will learn about the different types of AI and ML models and how to use them to solve problems.
Researcher
Researchers use their understanding of AI and ML to conduct research on new AI and ML algorithms and techniques. This course may be useful for Researchers who want to learn more about AI and ML so that they can conduct research on these topics. You will learn about the different types of AI and ML models and how to use them to solve problems.
Musician
Musicians use their understanding of AI and ML to create music. This course may be useful for Musicians who want to learn more about AI and ML so that they can use these technologies to create music. You will learn about the different types of AI and ML models and how to use them to solve problems.
Writer
Writers use their understanding of AI and ML to write about AI and ML. This course may be useful for Writers who want to learn more about AI and ML so that they can write about these topics. You will learn about the different types of AI and ML models and how to use them to solve problems.
Artist
Artists use their understanding of AI and ML to create art. This course may be useful for Artists who want to learn more about AI and ML so that they can use these technologies to create art. You will learn about the different types of AI and ML models and how to use them to solve problems.
Designer
Designers use their understanding of AI and ML to design products and services. This course may be useful for Designers who want to learn more about AI and ML so that they can use these technologies to design products and services. You will learn about the different types of AI and ML models and how to use them to solve problems.

Reading list

We've selected 12 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Introduction to AI and Machine Learning on GC - Italiano.
Provides a comprehensive overview of deep learning, from the basics to the latest advances. It valuable resource for anyone who wants to learn more about this rapidly growing field.
Practical guide to machine learning using Python. It covers a wide range of topics, from data preparation to model evaluation. It great resource for anyone who wants to learn how to use machine learning in practice.
Gentle introduction to machine learning. It covers a wide range of topics, from the basics to the latest advances. It great resource for anyone who wants to learn about machine learning without getting bogged down in the details.
Comprehensive textbook on statistical learning. It covers a wide range of topics, from the basics of probability to the latest advances in machine learning. It great resource for anyone who wants to learn about the theoretical foundations of machine learning.
Comprehensive textbook on machine learning algorithms. It covers a wide range of topics, from the basics to the latest advances. It great resource for anyone who wants to learn about the theoretical foundations of machine learning.
Practical guide to interpretable machine learning. It covers a wide range of topics, from the basics of interpretability to the latest advances in deep learning. It great resource for anyone who wants to learn how to make their machine learning models more interpretable.
Comprehensive textbook on generative adversarial networks. It covers a wide range of topics, from the basics to the latest advances. It great resource for anyone who wants to learn about the theoretical foundations of generative adversarial networks.
Practical guide to machine learning using Python. It covers a wide range of topics, from the basics to the latest advances. It great resource for anyone who wants to learn how to use machine learning in practice.
Practical guide to deep learning for coders. It covers a wide range of topics, from the basics to the latest advances. It great resource for anyone who wants to learn how to use deep learning in practice.
Practical guide to deep learning using Python. It covers a wide range of topics, from the basics to the latest advances. It great resource for anyone who wants to learn how to use deep learning in practice.
Practical guide to machine learning for hackers. It covers a wide range of topics, from the basics to the latest advances. It great resource for anyone who wants to learn how to apply machine learning to real-world problems.

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