Enhance your skills in prompt engineering with advanced strategies and ethical considerations. This course dives deep into application-specific approaches, offering the tools to optimize and personalize prompts for cutting-edge AI models like OpenAI's GPT, DALL-E, and more. You will explore advanced topics such as iterative improvement, bias reduction, and ethical prompt design, which are essential for building reliable AI systems.

The course focuses on the practical application of advanced techniques, including A/B testing, multilingual prompt design, and multimodal AI models. Real-world examples and quizzes will ensure learners can immediately apply these strategies to domains like education, coding, and entertainment.

What sets this course apart is its ability to merge advanced theory with hands-on exercises, allowing learners to adapt and innovate in the field of prompt engineering. Critical thinking and ethical awareness are at the core of prompt creation, making this course ideal for anyone working with advanced AI systems.

Ideal for professionals seeking to specialize in AI prompt engineering, this course benefits those with a foundational understanding of AI and prompt design. Learners will develop advanced techniques and gain the confidence to tackle more complex AI-driven projects.

This course is part two of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization.

Copyright © 2024 by John Wiley & Sons, Inc. All rights reserved.
Published by John Wiley & Sons, Inc., Hoboken, New Jersey. Published simultaneously in Canada.
Used by arrangement with John Wiley & Sons, Inc.

Advanced Techniques and Applications in Prompt Engineering


This course features Coursera Coach!
A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course.

This advanced course on Prompt Engineering and Memory Management offers you a deep dive into techniques that enhance the performance and interaction of Large Language Models (LLMs). Starting with the basics of prompt engineering, you will explore a variety of advanced strategies, from few-shot to zero-shot and chain-of-thought prompting. As you progress, you’ll dive into context and memory management, learning how LLMs retain and utilize memory for more sophisticated interactions. The course’s hands-on projects help you apply each technique, ensuring that you not only understand the theory but also gain practical experience with real-world scenarios.

The course also covers retrieval-augmented generation (RAG), a cutting-edge method that integrates external data retrieval with generative AI to enhance model responses. Throughout the modules, you'll engage in building and optimizing complex workflows, from setting up memory management for chatbots to constructing a complete RAG pipeline. You'll explore its integration into user interfaces, making the final product both functional and user-friendly.

This course is ideal for intermediate to advanced learners with a background in AI or programming. It focuses on individuals interested in refining their skills in AI model optimization, particularly in the areas of prompt design, memory management, and RAG application development.

By the end of the course, you will be able to implement advanced prompting techniques, manage context and memory in LLMs, develop a functional RAG pipeline, and integrate these systems into interactive applications.

Advanced Prompt Engineering and Memory Management


This course describes different types of computer vision use cases and then highlights different machine learning strategies for solving these use cases. The strategies vary from experimenting with pre-built ML models through pre-built ML APIs and AutoML Vision to building custom image classifiers using linear models, deep neural network (DNN) models or convolutional neural network (CNN) models.

The course shows how to improve a model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters while trying to avoid overfitting the data.

The course also looks at practical issues that arise, for example, when one doesn't have enough data and how to incorporate the latest research findings into different models.

Learners will get hands-on practice building and optimizing their own image classification models on a variety of public datasets in the labs they will work on.

Computer Vision Fundamentals with Google Cloud


This course features Coursera Coach!
A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course.
This advanced course offers an in-depth exploration of behavior trees in Unity 6, focusing on the implementation of AI-driven gameplay mechanics. You'll gain the skills to build complex AI systems that respond intelligently to dynamic game environments, creating immersive player experiences. Throughout the course, you'll dive into behavior tree concepts such as sequences, selectors, and node extensions, as well as advanced techniques like dynamic priority changes and agent cooperation.
The course guides you through practical applications, from setting up pathfinding in Unity to building sophisticated, scalable behavior trees. You’ll also face hands-on challenges, including a cop-and-robber scenario, to test your skills in real-world conditions. By the end, you’ll be able to implement complex AI behaviors, optimize performance, and debug AI systems effectively.
This course is designed for game developers who are already familiar with Unity and want to deepen their knowledge of AI systems. It’s perfect for those looking to elevate their AI development skills to an advanced level, allowing them to create intelligent, interactive gameplay experiences. No prior AI or behavior tree knowledge is required, but a strong grasp of Unity is necessary.

Advanced Game AI with Behavior Trees in Unity 6


This is the third course in the IBM AI Enterprise Workflow Certification specialization.    You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones.

Course 3 introduces you to the next stage of the workflow for our hypothetical media company.  In this stage of work you will learn best practices for feature engineering, handling class imbalances and detecting bias in the data.  Class imbalances can seriously affect the validity of your machine learning models, and the mitigation of bias in data is essential to reducing the risk associated with biased models.  These topics will be followed by sections on best practices for dimension reduction, outlier detection, and unsupervised learning techniques for finding patterns in your data.  The case studies will focus on topic modeling and data visualization.

By the end of this course you will be able to:
1.  Employ the tools that help address class and class imbalance issues
2.  Explain the ethical considerations regarding bias in data
3.  Employ ai Fairness 360 open source libraries to detect bias in models
4.  Employ dimension reduction techniques for both EDA and transformations stages
5.  Describe topic modeling techniques in natural language processing
6.  Use topic modeling and visualization to explore text data
7.  Employ outlier handling best practices in high dimension data
8.  Employ outlier detection algorithms as a quality assurance tool and a modeling tool
9.  Employ unsupervised learning techniques using pipelines as part of the AI workflow
10.  Employ basic clustering algorithms

Who should take this course?
This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses.

What skills should you have?
It is assumed that you have completed Courses 1 and 2 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.

This course requires 7.5 to 9 hours of study.

AI Workflow: Feature Engineering and Bias Detection


This course provides a deep dive into deploying, evaluating, and scaling production-ready AI systems, focusing on LangChain and LangGraph. You’ll gain hands-on experience in implementing, benchmarking, and mitigating risks to ensure robust AI solutions in enterprise environments.

Learners will develop practical skills in evaluating AI systems, covering observability, cost management, and performance optimization. The course emphasizes production readiness and equips you to scale AI applications for real-world tasks while addressing emerging AI trends.

What makes this course unique is its focus on enterprise-level deployment, with expert guidance on maintaining AI systems and preparing them for future advancements in generative AI.

Ideal for AI developers and engineers, this course requires experience with AI systems and a foundational knowledge of software development. Learners will benefit from practical, real-world scenarios.

This course is part three of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization.

Deploying, Evaluating, and Scaling Production AI Systems


Decoding Large Language Models

Large Language Models (LLMs) are transforming the way organizations interact with data, automate tasks, and deliver personalized experiences. This course unpacks the architecture, training methods, and strategic implementation of LLMs—core skills for anyone looking to thrive in the evolving AI landscape.

Through a structured journey from model fundamentals to advanced optimization and deployment, learners will gain practical expertise in fine-tuning, evaluating, and integrating LLMs into real-world systems. By the end, you’ll be able to design efficient, ethical, and scalable AI solutions that drive measurable business value.

Unlike traditional AI courses, this program bridges deep theoretical understanding with hands-on insights drawn from production deployments and case studies. You’ll learn not only how LLMs work, but also how to make them work for you in real business contexts.

This course is ideal for data scientists, software engineers, and IT professionals with a foundational understanding of AI or machine learning concepts. Prior experience with Python or neural networks is beneficial but not mandatory.


e.g. This is primarily aimed at first- and second-year undergraduates interested in engineering or science, along with high school students and professionals with an interest in programmingGain the skills for building efficient and scalable data pipelines. Explore essential data engineering platforms (Hadoop, Spark, and Snowflake) as well as learn how to optimize and manage them. Delve into Databricks, a powerful platform for executing data analytics and machine learning tasks, while honing your Python data science skills with PySpark. Finally, discover the key concepts of MLflow, an open-source platform for managing the end-to-end machine learning lifecycle, and learn how to integrate it with Databricks.

This course is designed for learners who want to pursue or advance their career in data science or data engineering, or for software developers or engineers who want to grow their data management skill set. In addition to the technologies you will learn, you will also gain methodologies to help you hone your project management and workflow skills for data engineering, including applying Kaizen, DevOps, and Data Ops methodologies and best practices.

With quizzes to test your knowledge throughout, this comprehensive course will help guide your learning journey to become a proficient data engineer, ready to tackle the challenges of today's data-driven world.

Spark, Hadoop, and Snowflake for Data Engineering


In this course, you’ll learn how to leverage GenAI for proactive legal risk mitigation, transforming compliance from a reactive process into a strategic advantage. You’ll explore GenAI-driven techniques to identify, evaluate, and resolve legal risks in real time. Through engaging lessons and hands-on practice, you’ll master the art of crafting complex prompts for legal analysis, design adaptive risk mitigation frameworks, and implement GenAI for compliance automation.

This course is tailored for a diverse group of professionals at the intersection of law and technology. It caters to legal practitioners—including lawyers, compliance officers, and in-house counsels—who are looking to deepen their use of GenAI for sophisticated legal risk mitigation. Business leaders and executives interested in embedding AI strategies into their risk management frameworks will also find value, as will regulatory and compliance specialists focused on leveraging AI to enhance compliance systems. Additionally, legal tech developers exploring the next generation of GenAI applications and law students seeking a competitive edge in a transforming legal landscape are ideal participants.

Participants should possess a foundational understanding of generative AI concepts and basic legal risk management strategies. Familiarity with how AI technologies can be used to identify, assess, and manage legal risks is strongly recommended to grasp the advanced concepts presented in this course. This background will enable learners to engage effectively with complex topics such as prompt engineering, regulatory adaptation, and AI-driven legal analysis.

By the end of the course, learners will be equipped to craft nuanced GenAI prompts that yield actionable insights in legal risk contexts. They will gain the skills to architect flexible GenAI-powered compliance systems that evolve alongside shifting regulatory demands. In addition, participants will be able to formulate robust internal policies that govern GenAI implementation, covering critical areas such as privacy, algorithmic fairness, and ethics. Lastly, they will learn to integrate advanced GenAI tools into legal workflows, enhancing automation and predictive analytics to support proactive, data-informed decision-making.

GenAI for Legal Risk Mitigation: Proactive Case Analysis


Neste curso, você vai aprender com engenheiros e instrutores de ML que trabalham com o desenvolvimento de última geração dos pipelines de ML aqui no Google Cloud. Nos primeiros módulos, vamos abordar o TensorFlow Extended (ou TFX), que é uma plataforma de machine learning do Google baseada no TensorFlow criada para gerenciar pipelines e metadados de ML. Você vai conhecer os componentes e a orquestração de um pipeline com o TFX. Também vamos abordar como é possível automatizar os pipelines usando a integração e a implantação contínuas e como gerenciar os metadados de ML. Depois disso, vamos mudar o foco para discutir como podemos automatizar e reutilizar os pipelines de ML em vários frameworks de machine learning, como tensorflow, pytorch, scikit-learn e xgboost. Você também vai aprender a usar outra ferramenta no Google Cloud, o Cloud Composer, para orquestrar seus pipelines de treinamento contínuo. Por fim, vamos mostrar como usar o MLflow para gerenciar o ciclo de vida completo do machine learning.

ML Pipelines on Google Cloud - Português


Building on the foundational knowledge from LLM Level 2, this advanced course, Large Language Models - Level 3 is designed to deepen your expertise in using H2O ai's cutting-edge Generative AI tools and techniques.

Guided by a team of AI experts including Sanyam Bhutani, Rob Mulla, and Andreea Turcu, the course begins with an in-depth exploration of the H2O ai Generative AI ecosystem, highlighting essential tools like h2oGPT, H2O LLM EvalGPT, and key GenAI components. You will learn how to create your custom h2oGPT models, effectively use various evaluation techniques to assess model performance, and understand the functionalities of LLM EvalGPT. Additionally, you will analyze the elements of the H2O ai GenAI Ecosystem to streamline and enhance your AI workflows.

By the end of this course, you will have the knowledge and confidence to apply these advanced tools and techniques to your business scenarios, making your AI projects more efficient and impactful. This course is ideal for AI and data science professionals, machine learning engineers, researchers, and business professionals looking to advance their skills in generative AI.

Upon successful completion, you will earn the LLM Level 3 Certification, demonstrating your proficiency in using H2O ai tools for the above mentioned variety of generative AI tasks. This certification is a valuable asset for your career in AI and data science.

H2O ai Large Language Models (LLMs) - Level 3


Traverse Trees for ML with DFS & BFS

Data that requires decisions and classifications are everywhere. Decision trees help to create solid data inferences for some of the most common types of machine learning problems. To take advantage of this structure, you need to understand how to properly traverse and build rulesets from decision trees. In this course, you'll learn the fundamentals of decision trees, understanding how to implement the structures in Java. From here, you'll explore some different methods of tree traversals, focusing on BFS and DFS. With BFS and DFS, you'll be able to apply tree traversals to generate tree rulesets. With this knowledge, you'll be equiped to implement and traversal decision trees.

This course is for Java developers with a solid programming background, focusing on decision trees, BFS, DFS, and rule generation for machine learning and data classification.

A solid understanding of Java programming is crucial for implementing decision trees and traversal algorithms. Additionally, some familiarity with trees as a data structure will help, as decision trees rely on hierarchical structures.

By the end of this course, you'll have the skills to confidently implement tree traversal algorithms like BFS and DFS, and generate powerful rules from decision trees to tackle real-world machine learning problems.


This course explores the full spectrum of Prompt Engineering, equipping AI practitioners, developers, and knowledge workers with the skills to design, optimize, and govern high-performance prompts for large language models. You'll begin by mastering the foundations of LLM interactions including effective prompting techniques, few-shot patterns, and self-consistency prompting using tools like Google AI Studio and simulated chat environments.
You'll then advance through chain-of-thought reasoning, tree-of-thought exploration, role-based prompting, and prompt chaining before diving into context engineering, structured output design, and human-in-the-loop workflows. Finally, you'll tackle external knowledge integration through RAG pipelines, prompt A/B testing and quality evaluation, and operational governance including adversarial prompting defenses and ethical prompt practices.
By the end of this program, you will be able to:

- Apply foundational and advanced prompting techniques to solve complex, real-world AI tasks.
- Design multi-step prompt workflows using chaining, auto-prompting, and context engineering.
- Build and evaluate RAG pipelines with citation-grounded, provenance-aware responses.
- Measure and optimize prompt quality through A/B testing and iterative refinement.
- Govern prompt systems responsibly by defending against injection attacks and applying ethical principles.

This program is designed for AI enthusiasts, developers, and business professionals who want to move beyond basic AI usage and build reliable, scalable, and responsible prompt-driven systems. Familiarity with AI chat tools will help you get the most from this experience.
Join us to unlock the full potential of Prompt Engineering and gain the technical fluency to shape, steer, and safeguard AI outputs across any domain.

Advanced Prompting and Context Engineering


Updated in May 2025.

This course now features Coursera Coach!

A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course.

This comprehensive course is designed to equip you with the knowledge and skills necessary to effectively audit IT project governance and system development processes. It begins with an overview of project governance and management, emphasizing the roles and responsibilities of key stakeholders, including project sponsors and the Project Management Office (PMO). You will gain a solid understanding of project initiation, planning, and execution phases, learning how to differentiate between auditing project content and project execution. Special focus is given to the project management methodologies, such as Agile and Predictive approaches, and the essential procedures for closing projects, ensuring you are well-versed in each phase of the project lifecycle.

The course then transitions to system development and risk management, where you will explore various system development lifecycle (SDLC) models and their associated risks. You will learn how to assess these risks and understand different software development methods, including the use of Computer Aided Software Engineering (CASE) tools and fourth-generation languages (4GLs). Key topics such as business case development, feasibility analysis, and the identification and design of effective data controls are thoroughly covered, preparing you to evaluate system and software development from an IS auditor's perspective.

In the final modules, the focus shifts to testing methodologies, data integrity, and change management. You will delve into decision support systems, testing plans, and methodologies to ensure data integrity and accuracy in application systems. The course also covers data migration processes, changeover techniques, and post-implementation reviews (PIR). By the end of the course, you will have a robust understanding of how to audit complex IT projects and system development processes, positioning you for success in the CISA exam and your professional career.

This course is ideal for IT auditors, project managers, and professionals preparing for the CISA certification. It is recommended for individuals with a foundational understanding of information systems and project management. No prior CISA experience is required, but familiarity with IT governance and audit principles will be beneficial.

Systems Acquisition, Development and Implementation


Do you want to know how robots work?  Are you interested in robotics as a career?  Are you willing to invest the effort to learn fundamental mathematical modeling techniques that are used in all subfields of robotics?

If so, then the "Modern Robotics:  Mechanics, Planning, and Control" specialization may be for you.  This specialization, consisting of six short courses, is serious preparation for serious students who hope to work in the field of robotics or to undertake advanced study.  It is not a sampler.

In Course 1 of the specialization, Foundations of Robot Motion, you will learn fundamental material regarding robot configurations, for both serial robot mechanisms and robots with closed chains.  You will learn about configuration space (C-space), degrees of freedom, C-space topology, implicit and explicit representations of configurations, and holonomic and nonholonomic constraints.  You will also learn how to represent spatial velocities and forces as twists and wrenches.  This material is at the core of the study of anything that moves (e.g., robots).

This course follows the textbook "Modern Robotics:  Mechanics, Planning, and Control" (Lynch and Park, Cambridge University Press 2017).  You can purchase the book or use the free preprint pdf.  You will build on a library of robotics software in the language of your choice (among Python, Mathematica, and MATLAB) and use the free cross-platform robot simulator V-REP, which allows you to work with state-of-the-art robots in the comfort of your own home and with zero financial investment.

This course requires 4 weeks of study of approximately 3-5 hours/week.

Modern Robotics, Course 1:  Foundations of Robot Motion


Design Beyond Limits with Figma

Modern digital products demand scalable, collaborative, and efficient design workflows that support fast-moving teams and evolving user needs. This course explores advanced Figma practices that help designers streamline collaboration, strengthen design systems, improve developer handoffs, and create accessible, production-ready interfaces with confidence.

You will learn how to organize Figma files for real-world teamwork, leverage plugins and AI tools to automate repetitive tasks, and build scalable design systems using tokens and reusable libraries. The course also covers collaboration strategies, async feedback workflows, accessibility practices, and precision handoff techniques that improve communication between designers, developers, and stakeholders.

What makes this course unique is its practical, workflow-focused approach built from real product design challenges and team collaboration experiences. By combining modern design operations with scalable system thinking, the course prepares you to manage complex design environments while maintaining consistency, efficiency, and design quality.

This course is ideal for UI/UX designers, product designers, design system specialists, frontend developers, and creative professionals looking to improve collaborative design workflows in Figma. Basic familiarity with Figma and digital product design concepts is recommended for the best learning experience.


This course features Coursera Coach!

A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course.

This course provides an in-depth look at how to manage and secure endpoints using Microsoft Endpoint Manager. You will gain a strong understanding of the key components, including Microsoft Endpoint Configuration Manager and Microsoft Intune, both of which are powerful tools for deploying, managing, and protecting devices, data, and applications in an enterprise environment. By the end of this course, you’ll be proficient in the core practices of endpoint management, from enrollment to security policy deployment.

The course begins with an introduction to Microsoft Endpoint Manager and its co-management features. You’ll explore various device enrollment methods, followed by a detailed examination of deploying and managing Windows clients in a business environment. Next, you’ll dive into configuring endpoints using profiles and managing their security posture with compliance policies. You'll also learn how to use Microsoft Intune for application deployment and management.

Finally, you’ll explore additional features of Endpoint Manager, such as troubleshooting and the advanced security capabilities available within the platform. The course includes practical tips and hands-on examples to ensure you can apply your knowledge effectively in real-world scenarios.

This course is ideal for IT professionals, system administrators, and anyone involved in managing endpoints in an organization. It requires a basic understanding of IT infrastructure, but no prior experience with Microsoft Endpoint Manager is needed. This course is suitable for intermediate learners who want to take their endpoint management skills to the next level.

Mastering Microsoft Endpoint Manager and Intune


Build a Mobile App with Firebase

e.g. This is primarily aimed at first- and second-year undergraduates interested in engineering or science, along wiUnlock Your App Development Potential: Creating a Feature-Rich Shopping List App

Are you ready to turn your visionary app ideas into reality? If your answer is a resounding 'YES,' then this course is your gateway to a transformative journey.

In this meticulously designed course, we will guide you through the creation of a sophisticated shopping list app using a powerful tech stack, including HTML, CSS, JavaScript, and Firebase. Leveraging Firebase's real-time database capabilities, you'll learn how to store and manage data seamlessly.

Course Highlights:

Mastering Database Fundamentals: Delve into the world of databases as we demystify the intricacies of Firebase Realtime Database. Gain the skills needed to establish and configure your database, while learning the art of data storage and retrieval.

Progressive Web App (PWA) Magic: Elevate your app development expertise by utilizing a unique technique that transforms your web app into a mobile-like experience. Learn how to create PWAs that exhibit remarkable responsiveness and user-friendliness.

Hands-On Experience: Throughout the course, you'll embark on a series of challenges designed to solidify your understanding and proficiency in each topic. Practical application is at the heart of our teaching methodology.

Real-Time Data Interaction: Discover the enchanting world of real-time data synchronization. Witness the magic as your app dynamically updates and displays data changes in real time, enhancing user engagement.

Comprehensive Learning: By the course's conclusion, you'll have the knowledge and skills to set up, connect, populate, display, and manage data in your Firebase Realtime Database. Plus, you'll be well-equipped to handle data deletion tasks.

Exploration Beyond: This course serves as a stepping stone to explore the myriad of other impressive features Firebase offers. You'll have the foundation needed to explore and utilize Firebase's full potential.

Join us on this immersive journey, where innovation meets practicality. Transform your app development aspirations into reality, and become proficient in crafting feature-rich, real-time applications. Enroll today and embark on your path to app development excellence.th high school students and professionals with an interest in programming.


Computer Vision Basics

By the end of this course, learners will understand what computer vision is, as well as its mission of making computers see and interpret the world as humans do, by learning core concepts of the field and receiving an introduction to human vision capabilities. They are equipped to identify some key application areas of computer vision and understand the digital imaging process. The course covers crucial elements that enable computer vision: digital signal processing, neuroscience and artificial intelligence. Topics include color, light and image formation; early, mid- and high-level vision; and mathematics essential for computer vision. Learners will be able to apply mathematical techniques to complete computer vision tasks.

This course is ideal for anyone curious about or interested in exploring the concepts of computer vision. It is also useful for those who desire a refresher course in mathematical concepts of computer vision. Learners should have basic programming skills and experience (understanding of for loops, if/else statements), specifically in MATLAB (Mathworks provides the basics here: https://www.mathworks.com/learn/tutorials/matlab-onramp.html). Learners should also be familiar with the following: basic linear algebra (matrix vector operations and notation), 3D co-ordinate systems and transformations, basic calculus (derivatives and integration) and basic probability (random variables).

Material includes online lectures, videos, demos, hands-on exercises, project work, readings and discussions. Learners gain experience writing computer vision programs through online labs using MATLAB* and supporting toolboxes.

 * A free license to install MATLAB for the duration of the course is available from MathWorks.

University at Buffalo
The State University of New York


Fundamentals of Semiconductor Manufacturing is an intermediate level course which provides a comprehensive introduction to the foundational principles, processes, and technologies in semiconductor manufacturing with a specific focus on wafer manufacturing. Learners will dive into the entire journey of a silicon wafer, from raw silicon to packaged functional chip. You will learn the purpose and mechanics of each major process step, including oxidation, photolithography, etching, doping, and thin film deposition. The course will also cover essential quality control concepts, such as cleanroom protocols and yield management. Designed for students with an interest in microelectronics manufacturing, this course bridges theoretical concepts with real-world manufacturing practices, preparing learners for further study or careers in the semiconductor industry.

This course is ideal for students, engineers, and technology professionals seeking a foundational understanding of semiconductor manufacturing. It’s also valuable for those entering roles in electronics, materials science, or manufacturing operations, as well as non-technical professionals working in semiconductor-related industries who want to grasp core production concepts.

No prior semiconductor experience is required. A basic understanding of physics, chemistry, or electronics will be helpful but not mandatory.

By the end of this course, you’ll understand the full semiconductor manufacturing journey, from raw silicon to finished chips, and the key processes, technologies, and quality controls that make it possible.


This course provides a comprehensive overview of data storage and management approaches for big data. Learners will explore structured, semi-structured, and unstructured data formats, compare SQL and NoSQL database technologies, and implement data lakes and data warehouses. The course includes working with various file formats and understanding the differences between batch and real-time processing approaches.

Course Learning Objectives:

By the end of this course, you will be able to:
- Compare and implement SQL and NoSQL database solutions for different big data scenarios
- Work effectively with structured, semi-structured, and unstructured data formats
- Design and implement data lakes and data warehouses for big data workloads
- Build data pipelines using ETL and ELT approaches with Azure Data Factory
- Differentiate between batch and real-time processing methodologies and implement appropriate solutions

Data Storage and Management for Big Data


Multi-Agent Systems with LangGraph

This program introduces Building Stateful & Multi-Agent Systems with LangGraph for developers and AI engineers who want to move beyond single-prompt agents and build reliable, production-ready workflows. You’ll begin by learning how LangGraph executes agent workflows and why state management is critical for correctness, debuggability, and long-running tasks.

Next, you’ll work with state reducers, typed state objects, and checkpointing mechanisms that allow agents to persist progress, recover from failures, and resume complex multi-step executions. Through hands-on demonstrations, you’ll implement conditional routing, parallel execution paths, and modular subgraphs to enable dynamic, decision-driven workflows.

As you progress, you’ll design human-in-the-loop systems with approvals and interrupts, apply debugging and time-travel analysis using execution logs and snapshots, and build multi-agent systems using supervisor–worker and consensus-based reasoning models for scalable, collaborative agent workflows.

By the end of the program, you will be able to:
- Explain how LangGraph executes workflows and manages state across agent nodes.
- Design stateful agent pipelines using typed state objects and reducer patterns.
- Implement checkpointing and recovery mechanisms for long-running agent workflows.
- Control execution flow using conditional routing, parallel execution, and subgraphs.
- Build human-in-the-loop workflows with approvals, interrupts, and state inspection.
- Debug agent systems using execution logs, snapshots, and time-travel analysis.
- Design multi-step planner–executor workflows for complex task execution.
- Orchestrate multi-agent systems using supervisor–worker and consensus-based models.

This program is ideal for AI engineers, backend developers, and system architects who want to build agent systems that are not only intelligent, but also predictable, auditable, and production-ready. Prior experience with Python, LLM fundamentals, and basic agent concepts will help maximize your learning experience.

Learners need a reliable internet connection, a modern web browser, and access to Python development tools. The course uses LangGraph and modern LLM APIs, which do not require specialized hardware. Familiarity with LangChain or agent-based workflows is recommended.

Join us to learn how to design stateful, multi-agent systems that can plan, recover, coordinate, and reason reliably in real-world applications.


GenAI for Front-End Developers

This course delves into how Generative Artificial Intelligence (GenAI) is transforming front-end development. Through discussions, video demos, and guided hands-on activities, you'll discover how GenAI can boost productivity in UI/UX design, code generation, testing, and documentation. Additionally, the course addresses ethical concerns, risks, and best practices for responsible GenAI use in front-end development.

This course is ideal for team leads and managers guiding front-end development teams, individual front-end developers seeking to enhance their workflows with GenAI tools, and aspiring front-end developers looking to future-proof their skills and gain a competitive edge in the field.

Learners should have a solid understanding of HTML, CSS, JavaScript, and common front-end frameworks like React, Angular, or Vue, along with experience in UI/UX design principles and basic knowledge of version control systems like Git. Additionally, an open mindset towards incorporating GenAI tools and a willingness to experiment with new technologies are essential.

Whether you're an experienced front-end developer looking to stay ahead of the curve or an aspiring professional seeking to future-proof your skillset, this course provides a foundation that will empower you to unlock new levels of efficiency, productivity, and creativity in your work.


Updated in May 2025.

This course now features Coursera Coach!

A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course.

Cybersecurity is more critical than ever, and this course provides a solid foundation in understanding cyber threats and protective measures. You’ll explore the evolving threat landscape, identify common cyberattack techniques, and learn about Wi-Fi vulnerabilities and Advanced Persistent Threats (APTs). By the end, you'll be well-versed in key cybersecurity challenges.

The course begins with an introduction to networking fundamentals, covering essential concepts such as internet operations, routing protocols, and network addressing. You will then dive into security models, cybersecurity technologies, and security best practices for data centers, cloud environments, and virtualization. Additionally, you'll gain insights into endpoint security, system administration, and ITIL support functions.

Next, you will explore Palo Alto Networks security solutions. You’ll learn about Next-Generation Firewalls (NGFWs), Prisma Cloud and Prisma Access, SaaS security solutions, and Cortex XDR and XSOAR for incident response. Understanding these tools will help you navigate modern cybersecurity challenges effectively.

This course is ideal for aspiring cybersecurity professionals, IT administrators, and network engineers. A basic understanding of networking and IT concepts is helpful but not required. The difficulty level is intermediate, making it accessible to those with foundational IT knowledge looking to expand into cybersecurity.

Palo Alto Networks Cybersecurity Fundamentals


Ship data and schema changes without outages. This hands-on course teaches you how to treat schemas as contracts, evolve them safely, and keep producers, consumers, and warehouses green end-to-end. You’ll design compatibility policies in a Schema Registry (backward/forward/full, transitive), automate checks in CI, and practice expand → adapt → contract rollouts. In streaming labs, you’ll capture OLTP changes with Debezium, deliver Avro-encoded events to Kafka, and route malformed records to a DLQ with actionable alerts. On the analytics side, you’ll evolve BigQuery/Iceberg schemas additively (NULLABLE/defaulted columns), shield downstream users with views/contracts, and validate correctness with queries and time travel. Realistic scenarios walk you through enum expansions, type widening, null/tombstone semantics, and subject naming rules.

This course is for data engineers, backend engineers, and analytics engineers who work with real-time or streaming data systems and need to evolve schemas without downtime. It’s also useful for platform engineers and architects responsible for data contracts, CDC pipelines, or Kafka-based platforms.

Learners should have basic SQL knowledge and a general understanding of streaming systems such as Kafka, along with familiarity with Git and the command line. Experience with schemas, CDC, Docker, or cloud data warehouses is helpful but not required.

By the end, you’ll have runnable templates, governance checklists, and a portfolio-ready project that proves you can design zero-downtime change—confidently and repeatably. For more information, check out the document.

Manage Schema Evolution in Real‑Time Data


Optimize ML Models: Hyperparameter Tuning gives you the practical skills to move from “good enough” models to models that perform reliably at scale. You’ll learn how default hyperparameters shape model behavior, how computational complexity affects training cost, and why structured tuning methods outperform guesswork. Through short videos, hands-on practice, and a guided GridSearchCV project, you’ll build a complete workflow for selecting, evaluating, and explaining tuned model configurations. By the end of the course, you’ll know how to design effective search spaces, run systematic tuning experiments, interpret cross-validated results, and save tuned parameters for real ML pipelines—all essential skills for modern machine learning and AI roles.


Design Patterns

This course extends object-oriented analysis and design by incorporating design patterns to create interactive applications. Through a survey of established design patterns, you will gain a foundation for more complex software applications. Finally, you will identify problematic software designs by referencing a catalog of code smells.

You will be challenged in the Capstone Project to redesign an existing Java-based Android application to implement a combination of design patterns. You will also critique a given Java codebase for code smells.

After completing this course, you will be able to:
• Demonstrate how to use design patterns to address user interface design issues.
• Identify the most suitable design pattern to address a given application design problem.
• Apply design principles (e.g., open-closed, dependency inversion, least knowledge).
• Critique code by identifying and refactoring anti-patterns.
• Apply the model-view-controller architectural pattern.


Updated in May 2025.

This course now features Coursera Coach!

A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course.

Unlock the full potential of Microsoft Azure with this comprehensive course on storage, compute, containers, and automation. You’ll learn to manage complex cloud environments while building expertise in configuring and deploying Azure services. This course equips you with practical skills to streamline operations, optimize resources, and enhance scalability in your cloud infrastructure.

The course begins with mastering Azure Storage, including data import/export, redundancy, and object replication. You'll progress to configuring Azure Files and Blob Storage, covering everything from file shares to lifecycle management. Practical demonstrations ensure you can integrate these tools seamlessly into real-world IT workflows.

Dive deeper into automation using Azure Resource Manager templates and VM extensions. You’ll also explore virtual machine configurations, disk encryption, and resource management, building a robust foundation in virtualized compute environments. Further modules introduce container management, from scaling Kubernetes services to creating container apps, and finally, Azure App Service for custom domains, backups, and secure deployment.

This course is designed for IT professionals, cloud administrators, and security specialists aiming to enhance their Azure expertise. A basic understanding of cloud computing and familiarity with Azure fundamentals is recommended. With hands-on exercises and expert guidance, this intermediate-level course will elevate your Azure management skills.

AZ-104: Azure Storage, Compute, Containers, and Automation


Building a Machine Learning Solution

Welcome to Building a Machine Learning Solution, where you'll journey through the complete lifecycle of a machine learning project. This capstone course covers critical steps from problem definition to deployment and maintenance. You'll learn to define clear problem statements, collect and preprocess data, perform exploratory data analysis (EDA), and engineer features to enhance model performance. The course guides you in selecting and implementing appropriate models, comparing classical machine learning, deep learning, and generative AI approaches. Emphasizing real-world considerations, you'll address scalability, interpretability, and ethical implications. You'll gain hands-on experience with tools like scikit-learn, TensorFlow, PyTorch, and more, ensuring you can deploy and monitor models effectively. By the end of this course, you'll be equipped to build end-to-end ML solutions that transform data into actionable insights, making informed decisions at each stage of development.


This course is an introduction to sequential decision making and reinforcement learning. We start with a discussion of utility theory to learn how preferences can be represented and modeled for decision making. We first model simple decision problems as multi-armed bandit problems in and discuss several approaches to evaluate feedback. We will then model decision problems as finite Markov decision processes (MDPs), and discuss their solutions via dynamic programming algorithms. We touch on the notion of partial observability in real problems, modeled by POMDPs and then solved by online planning methods. Finally, we introduce the reinforcement learning problem and discuss two paradigms: Monte Carlo methods and temporal difference learning. We conclude the course by noting how the two paradigms lie on a spectrum of n-step temporal difference methods. An emphasis on algorithms and examples will be a key part of this course.

Decision Making and Reinforcement Learning


Fundamentals of Network Communication

In this course, we trace the evolution of networks and identify the key concepts and functions that form the basis for layered architecture. We introduce examples of protocols and services that are familiar to the students, and we explain how these services are supported by networks. Further, we explain fundamental concepts in digital communication, and focus on error control techniques that include parity check, polynomial code, and Internet checksum. Students will be required to have some previous programming experience in C-programming (C++/Java), some fundamental knowledge of computer organization and IT architecture and a background in computer science is a plus.


PyMongo - Advanced

Take your MongoDB and Python skills to the next level with Mastering PyMongo: Apply, Analyze, and Transform MongoDB Data Using Python. This advanced, project-based course teaches you how to use the official PyMongo library to manage, query, optimize, and transform document-oriented data through practical, hands-on exercises.

You'll begin by building a strong foundation in PyMongo, learning how to prepare sample data, load documents into MongoDB collections, perform sorting operations, and validate stored records. From there, you'll progress to advanced querying techniques using cursors, pagination, counting operations, and indexing to improve query performance.

The course then focuses on data transformation and analysis by guiding you through the creation of MongoDB aggregation pipelines and converting query results into structured pandas DataFrames for downstream Python analysis. Every concept is reinforced through project-based learning using sample datasets, helping you develop practical experience with real-world NoSQL workflows.

This course is ideal for Python developers, data professionals, and learners who want to strengthen their MongoDB programming skills using PyMongo. By the end of the course, you'll be able to build efficient data access patterns, optimize MongoDB queries, construct aggregation pipelines, and transform document data into structured formats for analysis using Python.


This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course.

Explore advanced game design topics, including player engagement, multiplayer systems, VR/AR/XR, monetization strategies, and industry realities. This course equips you with the frameworks needed to create compelling, market-ready games.

Learn to apply practical design strategies, integrating music, narrative, and business insights to enhance player experience and commercial success. By the end, you will understand how to navigate both creative and business challenges in game development.

Combining theory with real-world examples, the course stands out by offering actionable guidance from industry practices. It balances creative design thinking with strategic monetization approaches.

Ideal for game designers, developers, and industry enthusiasts seeking advanced knowledge. Prior experience in game design or development is beneficial. By the end of the course, you will be ready to design engaging games for diverse platforms.

This course is part three of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization.

From Level Up! The Guide to Great Video Game Design
Copyright © 2025 by John Wiley & Sons, Inc. All rights, including for text and data mining, AI training, and similar technologies, are reserved. Illustrations in this course are copyright © 2025 Scott Rogers.
Used by arrangement with John Wiley & Sons, Inc.

Advanced Game Design: Platforms & Monetization


Build a Memory Game in React

Discover how to build a fully interactive memory game in React, designed to enhance your development skills and with a focus on accessibility. This course guides you through each step, from fetching and managing API data to designing reusable components and implementing user interactions.

You’ll work on challenges such as randomizing game elements, detecting matches, and handling errors. Along the way, you'll gain practical experience in solving common development problems.

Accessibility is a key focus of the project, with detailed guidance on ARIA attributes, semantic HTML, and designing for inclusivity.

This course is ideal for anyone looking to deepen their React knowledge while working on a meaningful, real-world project. By the end, you’ll have an accessible, polished memory game that demonstrates your skills and commitment to building user-friendly applications.


Routing, Network Devices, and Security

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A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course.

In this course, you'll dive deep into the essential components of networking, routing, network devices, and securing TCP/IP systems. Starting with routing protocols such as NAT, OSPF, and BGP, you will learn to configure routers, understand static and dynamic routing, and explore concepts like port forwarding and NAT. As you progress, you'll uncover the various network applications and learn to apply network tools like Wireshark and netstat for troubleshooting and managing network traffic effectively. With hands-on activities throughout, you will gain the confidence to apply what you learn in real-world scenarios.

As the course continues, you'll also explore security techniques and solutions critical for safeguarding your networks. You'll learn about encryption methods, access control, digital certificates, and tools like RADIUS and TACACS+ that ensure a robust network security infrastructure. Additionally, you'll learn how to configure and manage advanced network devices like switches and firewalls. Concepts such as VLANs, inter-VLAN routing, and load balancing will help you optimize network performance while maintaining security.

This course is designed for IT professionals and network administrators who want to improve their skills in routing, network management, and security. The course does not require prior knowledge of networking, though some basic computer skills may be helpful. By the end of the course, you will understand how to manage network devices, configure security protocols, and implement best practices to build and secure complex network infrastructures.

By the end of the course, you will be able to configure routers, secure TCP/IP communications, troubleshoot network issues using various tools, implement VLANs, and ensure network security with encryption and access control methods.


Information System Auditing, Governance, and Management is the first course of Exam Prep: Certified Information Systems Auditor (CISA) Specialization. The course also explores enterprise governance frameworks, IT policies and standards, quality management practices, and enterprise architecture. Additionally, it covers IT resource management and regulatory compliance, providing a comprehensive foundation for professionals involved in IT audit, risk, and governance roles.
The course is divided into three modules, and Lessons and Video Lectures further segment each module. This course facilitates learners with approximately 2:00-2:30 Hours of Video lectures that provide both Theory and Hands-On knowledge. Also, Graded and Ungraded Quizzes are provided with every module to test the ability of learners.
- Module 1: Information System Auditing Process
- Module 2: Execution
- Module 3: Governance and Management of IT

This course is designed for IT auditors, audit managers, security professionals, and consultants. Their current job roles often involve assessing IT and business systems, managing risks, ensuring compliance, and implementing controls.

By the end of the course, learners will be able to:
- Evaluate & Test IT Controls Effectively.
- Utilize Key Audit Evidence & Analytics.
- Understand IT Governance & Compliance.

Information System Auditing, Governance and Management


Unreal Engine 5 for Games - Physics

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course.

Unreal Engine 5 is one of the most powerful game development tools available today. This course offers a comprehensive guide to mastering Unreal Engine's physics system, providing you with the skills to create realistic, dynamic game environments. Throughout this course, you will learn how to simulate physical interactions, handle object collisions, and manipulate forces and materials for a more immersive gaming experience.

The course starts with foundational physics principles in Unreal Engine 5, such as physics bodies, vectors, and collision volumes. As you progress, you'll explore more advanced topics like destruction physics, cloth simulation, and cable dynamics. Practical applications will guide you through creating interactive gameplay elements, and by the end of the course, you'll be able to apply your knowledge in real-world game development projects.

This course is ideal for aspiring game developers, particularly those with an interest in the technical aspects of Unreal Engine. No prior experience with physics engines is required, although familiarity with game development basics will be helpful. The course has an intermediate difficulty level, so it is perfect for learners with some foundational knowledge of game development who are ready to dive deeper into physics simulations.

By the end of the course, you will be able to simulate realistic physics interactions in Unreal Engine 5, create dynamic environments with forces and materials, design destructible objects, and implement advanced simulation systems such as cloth physics and cable dynamics.


This course provides thorough preparation for the CASP+ CAS-004 exam, focusing on advanced security strategies, techniques, and real-world applications. The course covers crucial concepts in risk management, endpoint security, cloud environments, and incident response, preparing learners for success in handling complex security systems and making strategic decisions.

Learners will develop the skills necessary to configure secure endpoints and cloud environments, conduct thorough risk assessments, and identify indicators of compromise. Hands-on assessments, and practical scenarios reinforce the concepts, ensuring learners can directly apply what they’ve learned in real-world cybersecurity situations.

What sets this course apart is its seamless integration of theoretical knowledge with practical, real-world tasks, closely aligned with the CAS-004 exam domains. Through interactive learning tools, learners will gain deep, actionable expertise in managing advanced security environments.

This course is ideal for cybersecurity professionals seeking CASP+ certification, with an assumed background in security fundamentals, networking, and system administration. The curriculum is designed to expand their technical capabilities and strategic decision-making skills.

This course is part one of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization.

Copyright © 2023 by John Wiley & Sons, Inc. All rights reserved. Published by John Wiley & Sons, Inc., Hoboken, New Jersey. Published simultaneously in Canada and the United Kingdom. Used by arrangement with John Wiley & Sons, Inc.

Advanced Security Practices: Risk & Endpoint Security


This course begins with an introduction to automation frameworks, focusing on the Page Object Model (POM) to enhance code reusability and maintainability. You will learn to create object repositories, page classes, and use Page Factory for efficient web element initialization. The course covers practical tips for finding all links on a webpage, providing a solid foundation in automation frameworks.

Next, delve into Log4j2, from default configurations to advanced custom loggers, writing configuration files for console and file logging, and integrating Log4j2 into test cases to improve logging infrastructure.

Transitioning to TestNG, the course covers setup, annotations, method prioritization, grouping, parameterization, parallel test execution, DataProviders, ITestResult, and listeners for customized test behavior. Practical exercises reinforce these concepts, preparing you for real-world application.

Designed for software testers, automation engineers, and QA professionals with basic Selenium WebDriver and Java knowledge, this course deepens your understanding of advanced automation frameworks and TestNG. Implement POM and Page Factory, configure and use Log4j2, set up and leverage TestNG, utilize its advanced features, create and manage advanced reports, and execute data-driven tests and handle file uploads using Selenium WebDriver.

Advanced Automation Frameworks and Testing with TestNG


In this course, you'll explore some of JavaScript’s most powerful features to enhance your coding skills. You’ll learn how to design and manage custom objects, as well as work with advanced patterns for handling collections and functions. These concepts will help you write cleaner, more efficient code and leverage JavaScript’s unique capabilities to handle complex scenarios effectively.

There are 3 sections in this course:

1. Creating Custom Objects: Learn how to create custom objects using factory functions, constructor functions, and classes. Explore inheritance via the prototype chain, the use of methods like apply(), call(), and static methods, as well as defining getters, setters, and private fields for controlled property access.

2. Collections and Symbols: Discover efficient data management with JavaScript’s Map and Set collections, and explore Symbols, a unique, immutable data type for private or globally unique properties in objects.

3. Advanced Function Patterns and Generators: Master advanced function techniques including closures, IIFE’s, and recursion. Learn about throttling and debouncing to optimize function execution, and use generators to create controlled iterable sequences.

By the end of the course, you’ll have a wealth of knowledge on modern JS techniques and patterns, giving you the ability to write more efficient, scalable code.

Advanced JavaScript 3: Objects, Collections, Functions


This hands-on, project-driven course gives you the skills to bring artificial intelligence (AI) to life using Java and the Spring Framework. In just 3 weeks, you’ll learn how to build intelligent apps that create text, images, or recommendations quickly and become the go-to developer employers are looking for.

To start, you’ll set up your Java development environment and dive into the core principles of AI. You’ll then move straight to building hands-on experience using tools and techniques such as Deeplearning4j and Spring AI in practical projects that employ powerful Java and Spring AI libraries. Plus, you’ll integrate your AI skills with Spring, leveraging its robust architecture to build smart applications.

This course is ideal for developers with a working knowledge of Java and Spring who want to break into the fast-moving world of AI without switching ecosystems or learning a new language from scratch.
If that’s you… enroll today and build the highly sought-after GenAI skills employers are actively seeking in just 3 weeks.

Generative AI for Java and Spring Development


Engineer & Explain AI Model Decisions

Engineer & Explain AI Model Decisions is an Intermediate-level course designed for Machine Learning and AI professionals who need to build trustworthy and justifiable AI systems. In today's complex data environments, high accuracy is not enough; you must be able to prove why a model made its decision and remediate biases that cause real-world harm.

This course empowers you to combine advanced feature engineering and model interpretability practices to ensure ethical, reliable deployment. You will begin by mastering data transformation, learning to clean chaotic, conversational logs (like agent chat history) and converting them into structured, model-ready tensors using Python, scikit-learn, TF-IDF, and embedding aggregation.

Further, you will dive into the "black box" using powerful explainability techniques like SHAP to analyze model reasoning. You will run diagnostics on misclassified examples, flag spurious correlations (such as time-of-day dependencies), and develop strategies for bias remediation. The final deliverable is an AI Model Decision Toolkit, culminating in a stakeholder-ready interpretability report that translates technical findings into actionable, business insights. This course is essential for anyone responsible for the transparent, reliable, and bias-aware deployment of AI in production.


Are you a Systems Analyst, IT Systems Architect, Software Architect, or a Solutions Architect and keen to use generative AI (GenAI) to enhance how you analyze and design systems, define solution requirements, and share technical insights?

In this course, you’ll learn how to craft prompts and apply AI-driven requirement extraction to convert unstructured inputs, such as stakeholder interviews, emails, and process notes, into structured documentation, including user stories, use cases, system requirements, and acceptance criteria.

You’ll gain hands-on experience generating and refining BPMN diagrams, system architecture workflows, process flowcharts, and customer journey maps using AI-powered modeling tools. You'll use GenAI to produce solution design assets such as business rules, architecture summaries, impact assessments, and stakeholder-ready briefs. Plus, you'll explore practical integration of AI into platforms such as Jira, Confluence, Miro, and Power BI to enhance collaboration, traceability, and efficiency across solution design lifecycles.

If you’re looking to develop job-ready skills in system analysis, solution clarity, and stakeholder alignment using GenAI, enroll today and become the future-ready architect businesses need.

Generative AI: Advancing Systems Analysis & Architecture


Updated in May 2025.

This course now features Coursera Coach!

A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course.

Embark on a comprehensive learning journey starting with fundamental Python programming, including installation, variable manipulation, and essential data structures like lists, tuples, and dictionaries. Gain proficiency in numerical computations with NumPy and data manipulation with Pandas.

Strengthen your mathematical foundation with key linear algebra concepts vital for machine learning algorithms. Progress to data visualization using Matplotlib and Seaborn, interpreting and presenting data effectively. Develop a strong base in simple linear regression and gradient descent, and explore classification techniques with KNN and logistic regression through hands-on case studies.

Dive into advanced machine learning algorithms, including regularization techniques and deep learning foundations, tailored for NLP applications. By course end, you'll have a robust understanding of implementing and optimizing machine learning models for NLP tasks, preparing you for advanced projects and career opportunities.

Ideal for aspiring data scientists, machine learning enthusiasts, and professionals specializing in NLP, with basic Python and high school-level math knowledge required.

Foundations of Data Science and Machine Learning with Python


This course features Coursera Coach!

A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course.

In this advanced UiPath course, you will explore key aspects of UiPath Orchestrator, learning how to effectively manage, monitor, and scale your robotic process automation (RPA) workflows. By the end of the course, you will have mastered Orchestrator features such as robots, environments, queues, triggers, and more, empowering you to build and manage automation projects efficiently. You will also gain hands-on experience in managing automation workflows with real-time applications.

The course is structured to ensure a seamless learning experience as you move through interactive lessons on setting up machines, managing assets, configuring queues, and orchestrating jobs. You'll apply your knowledge in a real-world project by automating a grade management system using UiPath's Dispatcher and Performer workflows. The project offers deep insights into process management and end-to-end debugging, which will enhance your practical skills. Additionally, the course includes a comprehensive section of mock interviews that simulate real-life RPA interviews to help you build confidence and ace your next job opportunity.

This course is perfect for anyone looking to advance their skills in RPA, especially professionals aiming to specialize in UiPath Orchestrator. No prior knowledge of Orchestrator is necessary, but familiarity with RPA concepts will help you navigate the course more effectively. Whether you’re looking to enhance your job prospects or deepen your automation expertise, this course offers all the tools and knowledge you need.

Advanced UiPath, Orchestrator, and Mock Interviews


Welcome to this exciting new course on Tailwind CSS! This course introduces some intermediate Tailwind concepts that are perfect for rapidly building and styling websites in the real world.

If you’re new to Tailwind, head to our Learn Tailwind course here on Coursera for a quick intro. Throughout this course, we’ll build on these concepts, taking you from the basics to more advanced features, and dive into even more amazing things that Tailwind can do.

We’ll use Tailwind to build a web store product card for our client using professional mockups—one for desktop and one for mobile. By the end, you'll have discovered intermediate and advanced styling techniques using Tailwind CSS and crafted a fully responsive, professional product card for a web store.

Tailwind CSS Practice Project: Build a Product Card


Android Automotive OS is transforming how millions interact with their vehicles daily, with major automakers like GM, Ford, and Volvo adopting this platform. This Short Course was created to help Software Development professionals accomplish building production-ready infotainment applications that meet automotive safety standards. By completing this course, you'll be able to architect AAOS apps that integrate seamlessly with vehicle systems while ensuring driver safety compliance. By the end of this course, you will be able to: Understand Android Automotive system architecture to describe the roles of System UI, car services, and Vehicle HAL in infotainment stacks, Apply Car App Library templates to integrate a media playbook app that conforms to driver-distraction guidelines, and Apply emulator or device deployment steps to install and test an AAOS application using appropriate manifest and permission settings. This course is unique because it provides hands-on experience with real Vehicle HAL integration and teaches both GAS and non-GAS implementation approaches across multiple OEM platforms. To be successful in this project, you should have a background in Android development and familiarity with Android Studio.

Introduction to Android Automotive for Infotainment Systems


Learn how to write efficient, maintainable C++ code for data-intensive applications in this hands-on course. Key application areas include scientific simulation software, statistical data processing, and computer graphics.

You’ll begin by exploring how modern C++ supports high-efficiency programming and review the principles of a central piece of hardware in data-intensive applications: computer memory.
Next, the course shifts to software design. You’ll examine performance shortcomings of traditional object-oriented programming and dive into data-oriented perspective, which improves performance by structuring code around the data itself. You’ll learn how this paradigm overcomes bottlenecks and leads to more scalable, high-performance solutions.

Finally, you’ll apply these principles to modern multi-threaded systems. Through hands-on experience you will learn to use C++'s built-in parallel features to target both multi-core CPUs and GPUs.

Data-Oriented C++ in Scientific Programming

Jonas Latt
Bastien Chopard
Jean-Luc Falcone


Build Practical Voice Apps: From Basics to Implementation is a beginner-level course designed to help developers create voice applications that users actually want to use. As voice AI transforms industries from healthcare to retail, the demand for skilled voice application developers continues to grow.

This course provides hands-on experience with the core technologies—speech recognition, natural language processing, and text-to-speech—while emphasizing real-world implementation challenges and user experience design. Through practical projects, real-world case studies from companies like Deepgram and VAPI AI, and comprehensive testing strategies, you'll learn to build voice applications that solve genuine business problems.

The course covers everything from choosing the right APIs and optimizing for different environments to integrating with existing systems and deploying production-ready solutions. Whether you're adding voice features to existing applications or creating entirely new voice-first experiences, this course gives you the practical skills and strategic thinking needed to succeed in the rapidly evolving voice AI landscape.

Voice AI: Introduction to Building Voice Applications


Social Network Analysis

This course is designed to quite literally ‘make a science’ out of something at the heart of society: social networks. Humans are natural network scientists, as we compute new network configurations all the time, almost unaware, when thinking about friends and family (which are particular forms of social networks), about colleagues and organizational relations (other, overlapping network structures), and about how to navigate delicate or opportunistic network configurations to save guard or advance in our social standing (with society being one big social network itself). While such network structures always existed, computational social science has helped to reveal and to study them more systematically. In the first part of the course we focus on network structure. This looks as static snapshots of networks, which can be intricate and reveal important aspects of social systems. In our hands-on lab, you will also visualize and analyze a network with a software yourself, which will help to appreciate the complexity social networks can take on. During the second part of the course, we will look at how networks evolve in time. We ask how we can predict what kind of network will form and if and how we could influence network dynamics.


Code Free Data Science

The Code Free Data Science class is designed for learners seeking to gain or expand their knowledge in the area of Data Science.  Participants will receive the basic training in effective predictive analytic approaches accompanying the growing discipline of Data Science without any programming requirements.  Machine Learning methods will be presented by utilizing the KNIME Analytics Platform to discover patterns and relationships in data. Predicting future trends and behaviors allows for proactive, data-driven decisions.  During the class learners will acquire new skills to apply predictive algorithms to real data, evaluate, validate and interpret the results without any pre requisites for any kind of programming.  Participants will gain the essential skills to design, build, verify and test predictive models.
You Will Learn
•	How to design Data Science workflows without any programming involved
•	Essential Data Science skills to design, build, test and evaluate predictive models
•	Data Manipulation, preparation and Classification and clustering methods
•	Ways to apply Data Science algorithms to real data and evaluate and interpret the results


Data visualization is a powerful tool for exploring and communicating insights from data effectively. Seaborn, a Python visualization library built on top of Matplotlib, offers a wide range of features for creating attractive and informative statistical plots. This course provides a comprehensive overview of Seaborn, covering basic plotting techniques as well as advanced statistical visualizations. Participants will learn how to leverage Seaborn to visualize data distributions, relationships, and patterns, enabling them to convey complex information visually with confidence.

Data visualization is a powerful tool for exploring and communicating insights from data effectively. Seaborn, a Python visualization library built on top of Matplotlib, offers a wide range of features for creating attractive and informative statistical plots. This course provides a comprehensive overview of Seaborn, covering basic plotting techniques as well as advanced statistical visualizations. Participants will learn how to leverage Seaborn to visualize data distributions, relationships, and patterns, enabling them to convey complex information visually with confidence.

Participants should have a basic understanding of Python programming and fundamental data visualization concepts before enrolling in this course. Familiarity with Python's data manipulation libraries such as Pandas, and an introductory knowledge of Matplotlib, will be beneficial. This foundational knowledge will enable learners to quickly grasp Seaborn's functionalities and apply them effectively in their data visualization tasks.

By the end of this course, learners will be equipped to explain the critical role of data visualization in data analysis and interpretation. They will gain practical skills in creating basic plots using Seaborn to visualize data distributions and relationships. Additionally, learners will explore advanced statistical plots for deeper data analysis and develop the ability to customize and enhance Seaborn visualizations, ensuring their data stories are communicated clearly and impactfully.

Seaborn: Visualizing Basics to Advanced Statistical Plots


GenAI for Technology Consultants

"GenAI for Technology Consultants" is an immersive course designed to equip technology consultants with the knowledge and practical skills necessary to integrate Generative Artificial Intelligence (GenAI) into their daily tasks. Through the use of ChatGPT as the primary GenAI tool, participants will learn how to enhance productivity, foster innovation, and deliver superior value to clients.

This course is ideal for both seasoned technology consultants who want to maintain a competitive edge and aspiring professionals who aim to future-proof their skills. Additionally, it caters to business analysts seeking to streamline data analysis and automate report generation, enhancing their ability to make data-driven recommendations.

Participants should have a basic understanding of technology consultancy concepts and workflows, including client research, data analysis, and report generation. Experience with consulting tools and methodologies, as well as a fundamental knowledge of GenAI principles, is recommended. A curious mindset and willingness to explore new technologies will greatly benefit learners in this course.

By the conclusion of this course, consultants will possess the expertise to effectively incorporate GenAI tools into their consulting repertoire.

This course is ideal for both seasoned consultants looking to maintain a competitive edge and aspiring professionals seeking to future-proof their skills. It offers a robust foundation for achieving greater efficiency and innovation in consulting practices.


Learners will analyze Generative AI deployment environments, evaluate platform and vendor options, and apply best practices to integrate, deploy, and manage GenAI systems at scale. By the end of this course, learners will be able to design deployment architectures, assess operational trade-offs, and implement responsible GenAI solutions across real-world use cases.

This course equips learners with practical, job-ready skills for integrating Generative AI into production systems. Learners gain a structured understanding of the GenAI development landscape, deployment models, scalability considerations, and vendor evaluation strategies. Through real-world case studies, platform deep dives, and hands-on labs, learners move beyond theory to develop end-to-end deployment competence.

What makes this course unique is its balanced focus on strategy, technology, and execution. Instead of treating GenAI deployment as a purely technical exercise, the course emphasizes decision-making, cost management, risk mitigation, and responsible deployment practices. Learners explore leading platforms such as managed foundation model services and inference-optimized frameworks while applying best practices through guided projects. This course is ideal for professionals seeking to operationalize Generative AI solutions reliably, efficiently, and responsibly in modern enterprise environments.

Apply Generative AI Integration and Deployment Strategies


Did you know that hospitals using data-driven performance metrics can reduce readmission rates by up to 25% while improving patient satisfaction scores?

This Short Course was created to help data analysis professionals accomplish foundational healthcare data interpretation and metrics recognition.

By completing this course, you'll be able to confidently identify and calculate key healthcare performance indicators, interpret exploratory data analysis reports, and extract actionable insights from healthcare visualizations that you can apply immediately in your healthcare data role.

By the end of this course, you will be able to:
- Recognize common healthcare performance metrics and their formulas
- Understand exploratory data analysis (EDA) reports to identify key patterns

This course is unique because it bridges the gap between general data analysis skills and healthcare-specific applications, providing you with industry-standard metrics used across hospitals and healthcare systems.

To be successful in this project, you should have a background in basic mathematics and introductory statistics concepts.

Analyze Healthcare Data: Boost Patient Outcomes


In this first course of this specialization, you will discover what data visualization is, and how we can use it to better see and understand data. Using Tableau Public, we’ll examine the fundamental concepts of data visualization and explore the Tableau interface, identifying and applying the various tools Tableau has to offer. By the end of the course you will be able to prepare and import data into Tableau and explain the relationship between data analytics and data visualization. This course is designed for the learner who has never used Tableau before, or who may need a refresher or want to explore Tableau in more depth. No prior technical or analytical background is required. The course will guide you through the steps necessary to create your first visualization from the beginning based on data context, setting the stage for you to advance to the next course in the Specialization.

Fundamentals of Visualization with Tableau


This course provides essential skills for professionals seeking to build and manage sophisticated, reliable reporting workflows. Learners will progress from manual report creation to creating fully automated distribution systems that deliver timely insights to stakeholders. You will master the critical process of data validation, learning how to systematically verify the accuracy of report metrics against source data to ensure absolute trust in the insights you provide.

Through practical, hands-on activities, you will learn to implement efficient reporting processes, from configuring scheduled delivery to investigating and documenting data discrepancies. The course is designed to equip you with a powerful, efficient toolkit to enhance reporting reliability. By mastering report automation, data validation, and clear documentation, you will be prepared to transform your role from simply building reports to delivering trusted, actionable intelligence that drives confident, data-informed decision-making in any business setting.

Automate, Validate, and Deliver Insightful Reports


Python Programming Essentials

This course will introduce you to the wonderful world of Python programming!  We'll learn about the essential elements of programming and how to construct basic Python programs. We will cover expressions, variables, functions, logic, and conditionals, which are foundational concepts in computer programming. We will also teach you how to use Python modules, which enable you to benefit from the vast array of functionality that is already a part of the Python language. These concepts and skills will help you to begin to think like a computer programmer and to understand how to go about writing Python programs.

By the end of the course, you will be able to write short Python programs that are able to accomplish real, practical tasks. This course is the foundation for building expertise in Python programming. As the first course in a specialization, it provides the necessary building blocks for you to succeed at learning to write more complex Python programs.

This course uses Python 3.  While many Python programs continue to use Python 2, Python 3 is the future of the Python programming language. This first course will use a Python 3 version of the CodeSkulptor development environment, which is specifically designed to help beginning programmers learn quickly.  CodeSkulptor runs within any modern web browser and does not require you to install any software, allowing you to start writing and running small programs immediately.  In the later courses in this specialization,  we will help you to move to more sophisticated desktop development environments.

This is a four week class with an expected workload of 5-7 hours per week.


Mathematics underpins every aspect of computing, from algorithms and artificial intelligence to data analysis and cryptography. Applied Mathematical Methods for Computing equips you with essential tools in algebra, vectors, matrices, sequences, series, combinatorics, probability, and statistics. These methods provide the structure and reasoning needed to solve complex computational problems. Across four modules, you’ll explore advanced techniques, practise solving real-world examples, and build the confidence to apply mathematics in programming, algorithms, and data science. By the end, you’ll have a comprehensive toolkit for modelling systems, analysing uncertainty, and reasoning rigorously about computational tasks. Whether you’re preparing for advanced studies in computer science or strengthening your foundations for professional roles, this course offers the mathematical depth you need to succeed.

University of London
Goldsmiths, University of London


AI Mindfulness: Focus, Guide, Analyze

Build a sustainable mindfulness practice powered by AI guidance and personal data analysis. You'll learn the foundational principles of mindfulness—present-moment focus and nonjudgmental awareness—to explain why these techniques support wellbeing. Master the AI-guided session workflow, from receiving prompts to logging completion with active engagement. Most importantly, you'll analyze your attention drift patterns to identify your unique challenges and select targeted improvement tactics. Through realistic role plays and coach dialogues, you'll practice explaining mindfulness to skeptics and interpreting your own data. Designed for anyone seeking a systematic, evidence-based approach to mindfulness. No prior meditation experience or technical background required.


Foundations for Data Analytics Part 1

This course offers students an opportunity to learn fundamentals of computation required to understand and analyze real world data. The course helps students to work with modern data structures, apply data cleaning and data wrangling operations. The course covers conceptual and practical applications of probability and distribution, cluster analysis, text analysis and time series analysis.

This course is Part 1 of 2.


Improving delivery at scale requires more than running SAFe ceremonies — it requires using data, prioritization, and reflection to drive real change. In this short, hands-on course, you’ll learn how to analyze Agile Release Train (ART) performance, make economically sound portfolio decisions, and run Inspect & Adapt events that lead to measurable improvement.

Designed for beginner SAFe practitioners in project and program management roles, this course focuses on practical application of SAFe measurement and continuous improvement practices. You’ll explore how to interpret ART metrics such as PI Predictability and escaped defects, identify throughput bottlenecks and quality gaps, and explain what those signals mean to leaders.

Through realistic scenarios, guided activities, and decision-based practice, you’ll apply Weighted Shortest Job First (WSJF) to evaluate portfolio epics and recommend funding priorities within Lean budgets. You’ll also design and facilitate an Inspect & Adapt workshop, using root-cause analysis and quantified goals to turn insight into action.

By the end of the course, you’ll be able to use data to guide decisions, prioritize work transparently, and lead improvement conversations that build trust and deliver results across the enterprise.

Scaled Agile Framework (SAFe)/Optimize SAFe ARTs


This course will provide students with an introduction to using specific data techniques to address key sports administrative functions in team and roster management.  The primary focus  is on the use of data analysis in player acquisition and retention, as well as player and coach assessment.  Students will learn how the collective bargaining agreement (CBA) and standard player contract (SPC) provide the framework for management decisions in which data analysis play a pivotal role.

There is a focus on data science techniques as applied to sports datasets. Students will interpret outcomes of data analysis within the context of a sports calendar; identify provisions of the CBA and SPC; and gain an understanding of how the provisions impact decisions on team and roster management.

Player  Evaluation, Team Performance and Roster Management

Bruce Pitman
Helen Drew
Gerry Meehan
Amy Moore


Take action on business automation and innovation using no-code AI platforms. This course delivers hands-on frameworks for selecting, implementing, and tracking AI-powered process improvements—from data collection to workflow automation to stakeholder reporting. By emphasizing regional nuances in the USA and Indian business landscapes, learners acquire critical thinking tools, measuring methods, and ethical approaches to lead sustainable AI projects.

Applied No-Code AI for Business Processes


This course features Coursera Coach!

A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course.

Generative AI is rapidly reshaping how organizations grow, compete, and operate. This course equips business leaders and executives with a practical understanding of AI’s strategic value, helping them identify opportunities for revenue growth, operational efficiency, innovation, and competitive advantage. Through real-world case studies and hands-on exercises, learners will develop the confidence to evaluate AI initiatives and make informed business decisions.

The journey begins with foundational AI concepts, including the business impact of Generative AI, key differences between AI and traditional software, and frameworks for identifying high-value opportunities. Learners then explore how AI drives revenue growth through sales transformation, marketing acceleration, customer experience enhancement, and AI-powered commerce, supported by examples from organizations such as Klarna, Morgan Stanley, Coca-Cola, and Shopify.

The course continues with operational excellence, cost reduction, governance, and organizational change management. Learners examine AI applications across HR, supply chains, legal operations, and compliance while developing practical adoption strategies. Additional modules focus on building AI teams, selecting use cases, managing risk, creating AI champions, and fostering a culture that supports sustainable transformation.

Designed for business leaders, executives, managers, consultants, entrepreneurs, and decision-makers, this beginner-level course requires no prior technical AI expertise. It is ideal for professionals seeking to understand AI from a strategic and business-focused perspective rather than a technical implementation standpoint.

By the end of the course, you will be able to evaluate AI opportunities, prioritize high-impact use cases, develop AI adoption roadmaps, implement governance frameworks, measure business value and ROI, and create a long-term strategy for leveraging Generative AI as a driver of growth, efficiency, and competitive advantage.

Generative AI for Business Leaders & Executives


Essential Design Principles for Tableau

In this course, you will analyze and apply essential design principles to your Tableau visualizations. This course assumes you understand the tools within Tableau and have some knowledge of the fundamental concepts of data visualization. You will define and examine the similarities and differences of exploratory and explanatory analysis as well as begin to ask the right questions about what’s needed in a visualization. You will assess how data and design work together, including how to choose the appropriate visual representation for your data, and the difference between effective and ineffective visuals. You will apply effective best practice design principles to your data visualizations and be able to illustrate examples of strategic use of contrast to highlight important elements. You will evaluate pre-attentive attributes and why they are important in visualizations. You will exam the importance of using the "right" amount of color and in the right place and be able to apply design principles to de-clutter your data visualization.


Introduction to CNN Training

This beginner-friendly course on Convolutional Neural Networks (CNNs) equips you with essential skills to understand deep learning fundamentals and apply them to real-world image recognition tasks. Learn how CNNs power modern AI applications and gain practical experience through guided lab demos. Build confidence in designing, training, and implementing CNN models effectively.

By the end of this course, you will be able to:

Understand CNN Basics: Explain what CNNs are and their role in deep learning and computer vision
Explore Core Components: Learn about convolution, ReLU, and pooling layers in CNNs
Recognize Image Processing: Understand how CNNs detect and classify image features
Apply CNN Models: Build and implement CNN models through hands-on guided labs
Gain Practical Skills: Develop expertise to handle real-world image classification projects

Ideal for beginners, and professionals interested in AI, computer vision, and deep learning.


Customer Segmentation and Analytics

You will learn to convert raw customer and survey data into actionable segments tied directly to revenue and growth levers. This course introduces sophisticated persona-building, cross-tab and pivot analyses, and RFM (Recency, Frequency, and Monetary) segmentation so you can identify high-value cohorts and prioritize activation tactics. You'll practice extracting segments from both transactional and survey datasets, applying rigorous scoring and labeling conventions, and estimating incremental revenue from small-scale pilots. The curriculum focuses on translating these segmentation results into operational recommendations—including tailored retention, upsell, or reactivation campaigns—complete with simple lift estimates and implementation notes. You will prove that you can translate analytical outputs into actionable strategies and produce portfolio-ready documents showing that you can connect data to the bottom line.

By producing concise activation briefs and testing plans, you will learn to bridge the gap between analytic segmentation outputs and marketing or product execution, ensuring research recommendations can be implemented and measured for success. You will conclude this course with a high-impact project: executing a full RFM segmentation to calculate the profitability lift of specific customer segments. This process ensures you can move beyond simple data grouping to deliver evidence-based strategies that drive measurable business growth and customer lifetime value. This course provides the portfolio-ready artifacts necessary to demonstrate your ability to link data-driven insights directly to a company's bottom line.