Intro to ML: Language Processing
Earned Ağu 25, 2024 EDT
Analyze Speech and Language with Google APIs
Earned Ağu 25, 2024 EDT
Analyze Sentiment with Natural Language API
Earned Ağu 24, 2024 EDT
Generative AI Explorer - Vertex AI
Earned Ağu 23, 2024 EDT
Feature Engineering
Earned Nis 20, 2024 EDT
Build, Train and Deploy ML Models with Keras on Google Cloud
Earned Nis 12, 2024 EDT
Launching into Machine Learning
Earned Nis 7, 2024 EDT
Introduction to AI and Machine Learning on Google Cloud
Earned Mar 28, 2024 EDT
Business professionals in non-technical roles have a unique opportunity to lead or influence machine learning projects. If you have questions about machine learning and want to understand how to use it, without the technical jargon, this course is for you. Learn how to translate business problems into machine learning use cases and vet them for feasibility and impact. Find out how you can discover unexpected use cases, recognize the phases of an ML project and considerations within each, and gain confidence to propose a custom ML use case to your team or leadership or translate the requirements to a technical team.
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In this course, you learn how Gemini, a generative AI-powered collaborator from Google Cloud, helps developers build applications. You learn how to prompt Gemini to explain code, recommend Google Cloud services, and generate code for your applications. Using a hands-on lab, you experience how Gemini improves the application development workflow.
Duet AI was renamed to Gemini, our next-generation model.
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Earn the intermediate Skill Badge by completing the Classify Images with TensorFlow on Google Cloud skill badge course where you learn how to use TensorFlow and Vertex AI to create and train machine learning models. You primarily interact with Vertex AI Workbench user-managed notebooks.
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Earn a skill badge by completing the Analyze Images with the Cloud Vision API quest, where you discover how to leverage the Cloud Vision API for various tasks, including extracting text from images.
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The Google Cloud Computing Foundations courses are for individuals with little to no background or experience in cloud computing. They provide an overview of concepts central to cloud basics, big data, and machine learning, and where and how Google Cloud fits in.
By the end of the series of courses, learners will be able to articulate these concepts and demonstrate some hands-on skills.
The courses should be completed in the following order:
1. Google Cloud Computing Foundations: Cloud Computing Fundamentals
2. Google Cloud Computing Foundations: Infrastructure in Google Cloud
3. Google Cloud Computing Foundations: Networking and Security in Google Cloud
4. Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud
This final course in the series reviews managed big data services, machine learning and its value, and how to demonstrate your skill set in Google Cloud further by earning Skill Badges.
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Using large scale computing power to recognize patterns and "read" images is one of the foundational technologies in AI, from self-driving cars to facial recognition. The Google Cloud Platform provides world class speed and accuracy via systems that can utilized by simply calling APIs. With these and a host of other APIs, GCP has a tool for just about any machine learning job. In this introductory quest, you will get hands-on practice with machine learning as it applies to image processing by taking labs that will enable you to label images, detect faces and landmarks, as well as extract, analyze, and translate text from within images.
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Büyük veri, makine öğrenimi ve yapay zeka bilişim alanında günümüzün popüler konularıdır. Ancak bu alanlar yüksek uzmanlık gerektirir ve giriş seviyesi eğitim materyalleri zor bulunur. Neyse ki Google Cloud, bu alanlarda kullanıcı dostu hizmetler sunuyor ve bu giriş seviyesi kurs sayesinde Big Query, Cloud Speech API ve Video Intelligence gibi araçlarla ilk adımlarınızı atmanıza imkan tanıyor.
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It’s no secret that machine learning is one of the fastest growing fields in tech, and the Google Cloud Platform has been instrumental in furthering its development. With a host of APIs, GCP has a tool for just about any machine learning job. In this introductory course, you will get hands-on practice with machine learning as it applies to language processing by taking labs that will enable you to extract entities from text, and perform sentiment and syntactic analysis as well as use the Speech to Text API for transcription.
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Earn a skill badge by completing the Analyze Speech and Language with Google APIs quest, where you learn how to use the Natural Language and Speech APIs in real-world settings.
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Earn a skill badge by completing the Analyze Sentiment with Natural Language API quest, where you learn how the API derives sentiment from text.
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The Generative AI Explorer - Vertex Quest is a collection of labs on how to use Generative AI on Google Cloud. Through the labs, you will learn about how to use the models in the Vertex AI PaLM API family, including text-bison, chat-bison, and textembedding-gecko. You will also learn about prompt design, best practices, and how it can be used for ideation, text classification, text extraction, text summarization, and more. You will also learn how to tune a foundation model by training it via Vertex AI custom training and deploy it to a Vertex AI endpoint.
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This course explores the benefits of using Vertex AI Feature Store, how to improve the accuracy of ML models, and how to find which data columns make the most useful features. This course also includes content and labs on feature engineering using BigQuery ML, Keras, and TensorFlow.
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This course covers building ML models with TensorFlow and Keras, improving the accuracy of ML models and writing ML models for scaled use.
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The course begins with a discussion about data: how to improve data quality and perform exploratory data analysis. We describe Vertex AI AutoML and how to build, train, and deploy an ML model without writing a single line of code. You will understand the benefits of Big Query ML. We then discuss how to optimize a machine learning (ML) model and how generalization and sampling can help assess the quality of ML models for custom training.
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This course introduces the AI and machine learning (ML) offerings on Google Cloud that build both predictive and generative AI projects. It explores the technologies, products, and tools available throughout the data-to-AI life cycle, encompassing AI foundations, development, and solutions. It aims to help data scientists, AI developers, and ML engineers enhance their skills and knowledge through engaging learning experiences and practical hands-on exercises.
Managing Machine Learning Projects with Google Cloud
Earned Eyl 20, 2024 EDT
Gemini for Application Developers
Earned Eyl 14, 2024 EDT
Classify Images with TensorFlow on Google Cloud
Earned Eyl 14, 2024 EDT
Analyze Images with the Cloud Vision API
Earned Eyl 9, 2024 EDT
Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud
Earned Eyl 7, 2024 EDT
Intro to ML: Image Processing
Earned Eyl 3, 2024 EDT
Temel: Veri, Makine Öğrenimi, Yapay Zeka
Earned Eyl 1, 2024 EDT
Intro to ML: Language Processing
Earned Ağu 25, 2024 EDT
Analyze Speech and Language with Google APIs
Earned Ağu 25, 2024 EDT
Analyze Sentiment with Natural Language API
Earned Ağu 24, 2024 EDT
Generative AI Explorer - Vertex AI
Earned Ağu 23, 2024 EDT
Feature Engineering
Earned Nis 20, 2024 EDT
Build, Train and Deploy ML Models with Keras on Google Cloud
Earned Nis 12, 2024 EDT
Launching into Machine Learning
Earned Nis 7, 2024 EDT
Introduction to AI and Machine Learning on Google Cloud
Earned Mar 28, 2024 EDT