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Gourav Chahar

Member since 2023

Silver League

33445 points
Machine Learning in the Enterprise Earned יונ 19, 2024 EDT
[DEPRECATED] Feature Engineering Earned יונ 18, 2024 EDT
[Depricated] Build, Train and Deploy ML Models with Keras on Google Cloud Earned יונ 17, 2024 EDT
Launching into Machine Learning Earned יונ 17, 2024 EDT
Introduction to AI and Machine Learning on Google Cloud Earned מאי 11, 2024 EDT
Machine Learning Operations (MLOps) for Generative AI Earned מאי 8, 2024 EDT
Responsible AI for Developers: Interpretability & Transparency Earned מאי 8, 2024 EDT
Responsible AI for Developers: Fairness & Bias Earned מאי 8, 2024 EDT
Introduction to Image Generation - בעברית Earned מאי 8, 2024 EDT
Inspect Rich Documents with Gemini Multimodality and Multimodal RAG Earned מאי 8, 2024 EDT
Vector Search and Embeddings Earned מאי 7, 2024 EDT
Introduction to Generative AI Studio - בעברית Earned מאי 7, 2024 EDT
Create Image Captioning Models - בעברית Earned מאי 7, 2024 EDT
Transformer Models and BERT Model - בעברית Earned מאי 7, 2024 EDT
Encoder-Decoder Architecture - בעברית Earned מאי 7, 2024 EDT
Attention Mechanism - בעברית Earned מאי 7, 2024 EDT
Level 1: Sports Data Earned ספט 12, 2023 EDT
Level 3: GenAI Earned ספט 12, 2023 EDT
Introduction to Generative AI - בעברית Earned ספט 12, 2023 EDT
Prepare Data for ML APIs on Google Cloud Earned ספט 11, 2023 EDT
Build a Secure Google Cloud Network Earned ספט 11, 2023 EDT
Set Up an App Dev Environment on Google Cloud Earned ספט 11, 2023 EDT
Implementing Cloud Load Balancing for Compute Engine Earned ספט 11, 2023 EDT
Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud Earned ספט 11, 2023 EDT
Google Cloud Computing Foundations: Networking & Security in Google Cloud Earned ספט 9, 2023 EDT
Google Cloud Computing Foundations: Infrastructure in Google Cloud Earned ספט 9, 2023 EDT
Google Cloud Computing Foundations: Cloud Computing Fundamentals Earned ספט 8, 2023 EDT

This course takes a real-world approach to the ML Workflow through a case study. An ML team faces several ML business requirements and use cases. The team must understand the tools required for data management and governance and consider the best approach for data preprocessing. The team is presented with three options to build ML models for two use cases. The course explains why they would use AutoML, BigQuery ML, or custom training to achieve their objectives.

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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 Google Cloud's AI and machine learning (ML) capabilities, with a focus on developing both generative and predictive AI projects. It explores the various technologies, products, and tools available throughout the data-to-AI lifecycle, empowering data scientists, AI developers, and ML engineers to enhance their expertise through interactive exercises.

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This course is dedicated to equipping you with the knowledge and tools needed to uncover the unique challenges faced by MLOps teams when deploying and managing Generative AI models, and exploring how Gemini Enterprise Agent Platform empowers AI teams to streamline MLOps processes and achieve success in Generative AI projects.

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This course introduces concepts of AI interpretability and transparency. It discusses the importance of AI transparency for developers and engineers. It explores practical methods and tools to help achieve interpretability and transparency in both data and AI models.

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This course introduces concepts of responsible AI and AI principles. It covers techniques to practically identify fairness and bias and mitigate bias in AI/ML practices. It explores practical methods and tools to implement Responsible AI best practices using Google Cloud products and open source tools.

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בקורס נלמד על מודלים של דיפוזיה, משפחת מודלים של למידת מכונה שיצרו הרבה ציפיות לאחרונה בתחום של יצירת תמונות. מודלים של דיפוזיה שואבים השראה מפיזיקה, וספציפית מתרמודינמיקה. בשנים האחרונות, מודלים של דיפוזיה הפכו לפופולריים גם בתחום המחקר וגם בתעשייה. מודלים של דיפוזיה עומדים מאחורי הרבה מהכלים והמודלים החדשניים ליצירת תמונות ב-Google Cloud. בקורס הזה נלמד על התיאוריה שמאחורי מודלים של דיפוזיה, ואיך לאמן ולפרוס אותם ב-Vertex AI.

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Complete the intermediate Inspect Rich Documents with Gemini Multimodality and Multimodal RAG skill badge course to demonstrate skills in the following: using multimodal prompts to extract information from text and visual data, generating a video description, and retrieving extra information beyond the video using multimodality with Gemini; building metadata of documents containing text and images, getting all relevant text chunks, and printing citations by using Multimodal Retrieval Augmented Generation (RAG) with Gemini. Explore other content in the Gemini Enterprise Agent Ready (GEAR) program.

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Explore AI-powered search technologies, tools, and applications in this course. Learn semantic search utilizing vector embeddings, hybrid search combining semantic and keyword approaches, and retrieval-augmented generation (RAG) minimizing AI hallucinations as a grounded AI agent. Gain practical experience with Vertex AI Vector Search to build your intelligent search engine. Explore other content in the Gemini Agent Ready (GEAR) program.

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בקורס הזה נלמד על Generative AI Studio, מוצר ב-Vertex AI שעוזר ליצור אבות טיפוס למודלים של בינה מלאכותית גנרטיבית, כדי להשתמש בהם ולהתאים אותם לפי הצרכים שלכם. באמצעות הדגמה של המוצר עצמו, נלמד מהו Generative AI Studio, מהם הפיצ'רים והאפשרויות שלו, ואיך להשתמש בו. בסוף הקורס יהיה שיעור Lab מעשי לתרגול של מה שנלמד, ובוחן לבדיקת הידע.

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בקורס הזה תלמדו איך ליצור מודל הוספת כיתוב לתמונה באמצעות למידה עמוקה (Deep Learning). אתם תלמדו על הרכיבים השונים במודל הוספת כיתוב לתמונה, כמו המקודד והמפענח, ואיך לאמן את המודל ולהעריך את הביצועים שלו. בסוף הקורס תוכלו ליצור מודלים להוספת כיתוב לתמונה ולהשתמש בהם כדי ליצור כיתובים לתמונות

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בקורס הזה נציג את הארכיטקטורה של טרנספורמרים ואת המודל של ייצוגים דו-כיווניים של מקודד מטרנספורמרים (BERT). תלמדו על החלקים השונים בארכיטקטורת הטרנספורמר, כמו מנגנון תשומת הלב, ועל התפקיד שלו בבניית מודל BERT. תלמדו גם על המשימות השונות שאפשר להשתמש ב-BERT כדי לבצע אותן, כמו סיווג טקסטים, מענה על שאלות והֶקֵּשׁ משפה טבעית. נדרשות כ-45 דקות כדי להשלים את הקורס הזה.

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בקורס הזה לומדים בקצרה על ארכיטקטורת מקודד-מפענח, ארכיטקטורה עוצמתית ונפוצה ללמידת מכונה שמשתמשים בה במשימות של רצף לרצף, כמו תרגום אוטומטי, סיכום טקסט ומענה לשאלות. תלמדו על החלקים השונים בארכיטקטורת מקודד-מפענח, איך לאמן את המודלים האלה ואיך להשתמש בהם. בהדרכה המפורטת המשלימה בשיעור ה-Lab תקודדו ב-TensorFlow תרחיש שימוש פשוט בארכיטקטורת מקודד-מפענח: כתיבת שיר מאפס.

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בקורס נלמד על מנגנון תשומת הלב, שיטה טובה מאוד שמאפשרת לרשתות נוירונים להתמקד בחלקים ספציפיים ברצף הקלט. נלמד איך עובד העיקרון של תשומת הלב, ואיך אפשר להשתמש בו כדי לשפר את הביצועים במגוון משימות של למידת מכונה, כולל תרגום אוטומטי, סיכום טקסט ומענה לשאלות.

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The beautiful game is changing, and data science is playing a big role. Teams are now using data to make better decisions about everything, from player recruitment to game strategy. We invite you to get hands-on experience on the fundamentals of sports data science no matter who you're cheering for! Use BigQuery ML to train advanced models to predict goals and evaluate performance. Learn new skills and get started towards earning your first Google Cloud Credential. No prior experience required.

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Recently, a McKinsey report found that Generative AI could add up to $4.4 trillion a year to the global economy, signaling an imminent surge in the demand for GenAI professionals. Now is the perfect time to get a head start and gain hands-on experience with Google Cloud's powerful GenAI tools and techniques, carefully designed to enhance your tech skills and set you on a promising course. Play now to be at the forefront of innovation, shaping the future with GenAI and earning your first Google Cloud GenAI game badge!

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זהו קורס מבוא ממוקד שמטרתו להסביר מהי בינה מלאכותית גנרטיבית, איך משתמשים בה ובמה היא שונה משיטות מסורתיות של למידת מכונה. הוא גם כולל הסבר על הכלים של Google שיעזרו לכם לפתח אפליקציות בינה מלאכותית גנרטיבית משלכם.

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Complete the introductory Prepare Data for ML APIs on Google Cloud skill badge to demonstrate skills in the following: cleaning data with Dataprep by Trifacta, running data pipelines in Dataflow, creating clusters and running Apache Spark jobs in Managed Service for Apache Spark, and calling ML APIs including the Cloud Natural Language API, Google Cloud Speech-to-Text API, and Video Intelligence API.

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Earn a skill badge by completing the Build a Secure Google Cloud Network skill badge course, where you will learn about multiple networking-related resources to build, scale, and secure your applications on Google Cloud.

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Earn a skill badge by completing the Set Up an App Dev Environment on Google Cloud skill badge course, where you learn how to build and connect storage-centric cloud infrastructure using the basic capabilities of the following technologies: Cloud Storage, Identity and Access Management, Cloud Functions, and Pub/Sub.

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Complete the introductory Implementing Cloud Load Balancing for Compute Engine skill badge to demonstrate skills in the following: creating and deploying virtual machines in Compute Engine and configuring network and application load balancers.

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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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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 third course covers cloud automation and management tools and building secure networks.

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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

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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 first course provides an overview of cloud computing, ways to use Google Cloud, and different compute options.

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