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36 badges and certificates.
Cassie Kozyrkov introduces decision-making for leaders working in data-rich organizations. The course covers how people frame decisions, use data and technology, delegate with trust, improve organizational decision practices, and build a healthy data culture, with discussion of how AI and machine learning affect business decisions.
Create Generative AI Apps on Google CloudCovers prompt design and retrieval augmented generation for production oriented LLM applications, including building an LLM and RAG chat app.
learn about generative AI applications and how you can use prompt design and retrieval augmented generation (RAG) to build powerful applications using LLMs.
ML Pipelines on Google CloudIntroduces TFX pipeline components and orchestration, then covers repeatable pipelines, continuous training, Cloud Composer, and MLflow.
The first few modules will cover about TensorFlow Extended (or TFX), which is Google’s production machine learning platform based on TensorFlow
Responsible AI for Developers: Privacy & SafetyExplains practical ways to apply privacy and safety practices with Google Cloud products and open source tools.
This course introduces important topics of AI privacy and safety.
Responsible AI for Developers: Fairness & BiasCovers recognizing and mitigating bias, then applying responsible AI practices with Google Cloud and open source tools.
This course introduces concepts of responsible AI and AI principles.
Build a Certification Study Guide: PMLEShows how to create a Gemini Notebook study guide and use it to practice for the Professional Machine Learning Engineer certification.
Learn how to use Gemini Notebook to create a personalized study guide for the Professional Machine Learning Engineer (PMLE) certification exam.
Responsible AI for Developers: Interpretability & TransparencyExplains why transparency matters to developers and engineers and presents practical ways to improve interpretability in data and models.
This course introduces concepts of AI interpretability and transparency.
Machine Learning Operations (MLOps) with Agent Platform: Model EvaluationCovers evaluation measures and methods for generative and predictive models, using Agent Platform for model selection, optimization, and ongoing monitoring.
This course equips machine learning practitioners with the essential tools, techniques, and best practices for evaluating both generative and predictive AI models.
Machine Learning Operations (MLOps) for Generative AIDescribes operational challenges for generative AI deployments and how Gemini Enterprise Agent Platform can streamline MLOps work.
This course is dedicated to equipping you with the knowledge and tools needed to uncover the unique challenges faced by MLOps teams
Attention MechanismIntroduces how attention focuses neural networks on relevant input and how it supports translation, summarization, and question answering.
a powerful technique that allows neural networks to focus on specific parts of an input sequence
Enterprise Search on Generative AI App BuilderExplains how organizations can make scattered internal documents and broad consumer-facing catalogs easier to search. Learners use Generative AI App Builder to add generative search to their applications.
Enterprises of all sizes have trouble making their information readily accessible to employees and customers alike.
Introduction to Vertex Forecasting and Time Series in PracticeCovers forecasting on Google Cloud from sequence-model and time-series foundations through data preparation, model development, and Vertex AI deployment. A retail example informs a final exercise in which learners build their own forecasts.
This course is an introduction to building forecasting solutions with Google Cloud.
Vector Search and EmbeddingsIntroduces AI-powered search patterns including semantic search with vector embeddings, hybrid retrieval that combines semantic and keyword methods, and retrieval-augmented generation. Learners use Vertex AI Vector Search to build a grounded search experience and can continue with related GEAR content.
Explore AI-powered search technologies, tools, and applications in this course.
Machine Learning Operations (MLOps): Getting StartedIntroduces Google Cloud tools and practices for deploying, evaluating, monitoring, testing, and automating production machine-learning systems. It describes how ML engineers work with data scientists to improve deployed models with discipline and speed.
This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud.
Machine Learning in the EnterpriseUses a case study of an ML team to examine business requirements, data governance, and preprocessing choices. Learners compare AutoML, BigQuery ML, and custom training for two example workloads.
This course takes a real-world approach to the ML Workflow through a case study.
Launching into Machine LearningBuilds from data-quality practices and exploratory analysis into AutoML, BigQuery ML, and model optimization. It also covers generalization and sampling as ways to assess models trained for custom machine-learning tasks.
The course begins with a discussion about data: how to improve data quality and perform exploratory data analysis.
Introduction to AI and Machine Learning on Google CloudSurveys Google's AI and machine-learning products for both generative and predictive projects. Interactive exercises connect tools across the data-to-AI lifecycle for data scientists, AI developers, and ML engineers.
This course introduces Google Cloud's AI and machine learning (ML) capabilities, with a focus on developing both generative and predictive AI projects.
Introduction to Generative AIIntroduces generative AI, common applications, and its differences from traditional machine learning. The course also points learners to Google tools for creating their own generative AI applications.
It also covers Google Tools to help you develop your own Gen AI apps.
An introduction to enterprise large language models, technical protocols, and data privacy. Listed in LinkedIn Learning’s August 2024 AI catalog, this course was completed on August 20, 2024, with 15 minutes of course content.
LLMs for Enterprise: Technical Protocols, Considerations, and Data Privacy
Apply ISO 27001 risk management to AI systems, identify vulnerabilities, and adapt ISO 27002 controls. The course connects security monitoring and compliance work with the practical challenges of AI deployments and the standards developing around them.
A short course on using Microsoft Copilot with organizational data. LinkedIn Learning lists it in the April 2024 business-software catalog; the original certificate identifies Microsoft Copilot and artificial intelligence for business as its skills, with 18 minutes of course content.
Use Copilot AI for Secure Work Data in Your Organization
Jerich Beason’s course focuses on securing generative AI in an organization. The original certificate identifies network security and generative AI as the skills covered, with completion on July 27, 2024, and 1 hour 15 minutes of course content.
Securing the Use of Generative AI in Your Organization with Jerich Beason
Doug Rose explains the values and principles behind an agile mindset, then connects them to teamwork and iterative delivery. Learners explore user stories, cross-functional collaboration, responding to change, popular frameworks such as Scrum and Kanban, common team roles, and exercises for improving how teams work.
Fulya Kocak introduces environmental, social, and governance factors and why organizations address them. The course surveys foundational ESG concepts and risks, sustainable investing, the three ESG pillars, common measures, reporting and ratings, communication that avoids greenwashing, and skills relevant to ESG work.
Examine the role of privacy for individuals, businesses, and policymakers. The course uses concrete scenarios to connect responsible data use with organizational safeguards, the consequences of misuse, and the choices that help people protect their information.
Recognize common forms of unconscious bias and how they influence decisions and workplace relationships. The course develops awareness of personal assumptions and offers approaches for responding to bias when collaborating with colleagues and participating in an organization.
Explore how transformers support natural language processing and how major large language models evolved after GPT-3. The course connects model architecture, training approaches, and the contributions of different LLMs to practical choices for AI work.
Oz du Soleil introduces Excel macros as a way to automate repeated spreadsheet tasks. Learners see when a macro is useful, record and review a basic macro, and get oriented to VBA, the Developer tab, launching macros, and common limitations such as errors and the lack of an undo action.
Jonathan Fernandes explores the changes introduced by the GPT-4 release. The course appears in LinkedIn Learning’s guide to generative AI and prompt engineering, and the original certificate records completion on June 12, 2023.
GPT-4: The New GPT Release and What You Need to Know by Jonathan Fernandes
Build a foundation in generative AI: how it works, how models create content, and where different model families fit. The course considers changes to creative work and employment alongside ethical responsibilities and the practical use of generative tools.
Khaulat Abdulhakeem introduces reinforcement learning concepts and the problems they can solve. The course covers core terminology, how to represent learning problems, Monte Carlo and temporal-difference methods, and extensions including deep, multi-agent, and inverse reinforcement learning.
Scott Duffy’s LinkedIn Learning course focuses on controlling Microsoft Azure costs. The original certificate records completion on April 4, 2023, with 1 hour 22 minutes of course content.
Lynn Langit surveys ways to monitor and manage Google Cloud spending across compute, data, networking, and serverless workloads. Learners explore billing reports and filtered cost views, then examine budgets, alerts, quotas, payment settings, committed-use discounts, and dashboards for tracking costs.
Lynn Langit explains how to forecast and manage AWS service costs using billing tools and operational practices. The course covers consolidated billing and alerts, cost controls for services such as EC2, S3, and RDS, and architectural choices that affect spending across common cloud workloads.
Harshit Tyagi introduces data engineering and its relationship to data science. Learners explore data wrangling, database schemas, distributed processing, tools including Hadoop, Hive, Spark, and Airflow, and the stages of building ETL pipelines to extract, transform, load, and schedule data.
Keith McCormick frames classification as a central machine-learning task and compares strategies for building predictive models. The course reviews model assessment, a range of classification algorithms, and practical challenges such as imbalanced categories, interactions, missing data, overfitting, and data reduction.
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