I enrolled in the JHU Agentic AI course hosted by Great Learning. It has been over a month now, and I'm thrilled to see a well-structured and up-to-date curriculum. The instructors are industry professionals with hands-on experience in cutting-edge AI tools. The learning sessions cover the basics and gradually progress to more advanced, hands-on examples, effectively bridging the gap between theory and real-world applications. The JHU faculty video series was particularly instrumental in building the foundational concepts. I'm eagerly anticipating the next few months and the exciting content they have in store. Additionally, the learning team has been incredibly helpful in managing this course for full-time professionals. They provide timely updates on upcoming plans and remind us of key deadlines, which has made the course experience smooth and hassle-free for me. At the moment, there are no job support sessions, but I believe there may be sessions later in the program.

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Please join if you want to learn programming, agentic AI, and LLMs.

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The Johns Hopkins Certificate Program in Agentic AI is an exceptional program that blends technical, theoretical, and practical hands-on experience in Agentic AI. It provides a firm foundation in the underpinnings of modern AI systems. The program itself, along with the learning support staff, is extraordinary—by far the best learning experience I've had in decades of training programs. It's tailored for everyone, from those just starting their Agentic AI journey to seasoned professionals seeking depth, breadth, and experiential learning. The quality is superb—a true tour de force learning journey. I also want to personally and professionally thank Lekhni from the Program Office for the Great Learning Agentic AI Program. Her guidance, support, and direction were pivotal to my success. She elevated my learning experience by being attentive, responsive, solution-oriented, and empathetic to the unique challenges faced by adult learners and working professionals. Lekhni's support and customer service were exceptional.

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If you are interested in AI and have a basic understanding of Python or programming, I highly recommend this program. It provides a strong foundation in Agentic AI with practical examples, hands-on learning, and excellent instructor support. The knowledge gained can be applied immediately to real-world projects and career growth.

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I am a physician by training, and much of my professional experience has been at the point where healthcare systems meet real people: patients, families, junior medical staff, and the operational realities of hospitals. Over the years, I began noticing a recurring problem. Healthcare does not necessarily fail because information is unavailable. It can fail because the right information, in the right context, does not reach the right person at the right moment. That observation changed the questions I was asking. I became particularly interested in informed consent. I have seen consent administered countless times in both elective and emergency settings. The process can vary considerably depending on the clinician, the time available, and the circumstances. A form may be correctly completed, signed, and filed, while the patient may still not fully understand the procedure, its alternatives, its material risks, or what the decision actually means. In other words, the documentation can be complete while the communication remains incomplete. That led me to begin developing the concept of ConsentPod: a bedside system intended to transform consent from a document-signing event into a process of education, communication, comprehension, and shared decision-making. But I quickly encountered a limitation in my own thinking. Having an idea for an AI-enabled healthcare product was not enough. I needed to understand what actually happens beneath an AI system if I wanted to build something responsibly. I did not want to become merely a better user of ChatGPT. I wanted to understand agents. Those questions motivated me to undertake the Johns Hopkins Agentic AI program. I entered the program as a doctor trying to understand technology. I came out thinking much more like a systems designer. The most valuable shift was conceptual. I began to move from thinking about AI as a model to thinking about AI as a system operating within an environment. That distinction became particularly meaningful when I applied it to healthcare. One concept I kept returning to was context. An AI system may be able to access enormous amounts of information, but access to information is not the same as understanding. I increasingly believe the future of AI is not about making the context window larger, but about making the context better. This connected many of the technical concepts from the program, including retrieval-augmented generation, tool use, orchestration, memory and state, evaluation, and human-in-the-loop systems, to problems I had already encountered clinically. The program therefore gave me something more valuable than a collection of technologies. It gave me a framework for decomposing complex human problems into intelligent, evaluable workflows without losing sight of the human decision at the center. The clearest application has been my continued development of ConsentPod. Agentic AI gave me a way to think about how such a workflow could be constructed. Rather than building one general-purpose chatbot, I began decomposing the problem into responsibilities: explaining information in the patient’s preferred language, retrieving authoritative clinical and institutional information, checking comprehension, verifying that workflow requirements have been addressed, documenting the interaction, and escalating situations that require human attention. The physician would still make the clinical judgment. The patient would still make the decision. The AI would not become the doctor. It would become an orchestration, communication, and context layer around the decision. That distinction has become central to how I think about healthcare AI. My most obvious challenge was that I came to the program as a physician rather than as a computer scientist. But the deeper challenge was learning how to translate a clinical problem into a computational problem without stripping away the human complexity that made the problem important in the first place. Healthcare is not a clean software environment. It contains incomplete information, interruptions, uncertainty, hierarchy, time pressure, emotional vulnerability, legal accountability, and decisions whose consequences can be irreversible. My clinical background became an advantage here. Whenever I encountered a technical concept, I tried to translate it back to the bedside. That way of translating between technical architecture and clinical reality helped me overcome the initial learning curve. More importantly, it changed the way I approach technology. I no longer begin with where can I put AI. I increasingly begin with what is the human problem, what decision is being made, what context is missing, what workflow surrounds that decision, and where an intelligent system could responsibly improve it. For me, the greatest value of the Johns Hopkins Agentic AI program is not the certificate. It is the change in the way I think. I entered with a collection of healthcare problems and a growing fascination with artificial intelligence. I leave with a more structured way of thinking about how intelligent systems might participate in solving those problems. Most importantly, I have begun to see AI not as a destination, but as infrastructure for solving human problems. That is the perspective I hope to carry forward as I continue working at the intersection of medicine, technology, and healthcare delivery.

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When I was offered this course, I was told that I did not need to know Python. I believe that was a fundamental mistake because, in order to successfully complete the hands-on labs, it is essential to have a solid understanding of Python. While it is true that you can complete the assignments, since the labs and projects often require writing only a few lines of code, I personally believe that many students rely heavily on artificial intelligence to finish the tasks without developing a true understanding of Python. The level of Python required for this course is actually quite advanced, as the course is much more development-oriented than focused purely on artificial intelligence itself. Regarding the instructors, I must honestly say that I am not completely satisfied. I have no doubt that they are highly professional and extremely competent in their respective fields. However, I found that most of the videos move too quickly, often giving the impression that the speakers are reading from a script. The explanations also tend to be somewhat superficial, and in many cases some concepts are not fully explained. I was left with the feeling that this course is about 90% Great Learning and only 10% Johns Hopkins University. At the beginning of the course, several instructors are introduced. However, in practice, I felt that William Roncal and Dr. Shelby were the ones who played the main role throughout the program. Unfortunately, neither the professor who worked for the FBI nor the military expert from the Gulf War contributed much, if anything, to the course. From that perspective, I felt somewhat disappointed. To be fair, I do not know whether they contributed significantly behind the scenes in ways that were not visible to students. If that is the case, then my opinion may be biased. Nevertheless, this is my honest feedback. Regarding the hands-on labs, Professor Hassam is a wonderful person with an excellent attitude and a genuine willingness to teach. However, I sometimes felt there was a slight lack of coordination or preparation regarding the material covered during the sessions. At times, I had the impression that Professor Hassam was reviewing the code for the first time during class rather than beforehand. That said, this may simply be my own mistaken perception. Overall, Hassam deserves a perfect 10 out of 10. I also found the Great Learning platform somewhat confusing and not particularly intuitive to navigate. It took me quite some time to become comfortable using it. Regarding the presentations, throughout my professional career—28 years at IBM and now 4 years at Dynatrace—I have always strongly disagreed with presentations that are filled with text while someone is speaking at the same time. I simply do not believe that the human brain is capable of reading and listening effectively at once. Therefore, when watching presentations by Dr. Shelby and Dr. William Roncal, you inevitably have to choose between listening to the speaker or reading the slide. The way I managed this was by pausing the video, reading each slide carefully, and then resuming the video to listen to the explanation. Although this is a tedious process, it greatly improves comprehension. I believe it would be valuable to hear Dr. William Roncal's and Dr. Shelby's thoughts on this aspect of the course. As for the course material and the knowledge shared, I think they are excellent. The program certainly requires significant effort from students, especially for those of us who do not always have a great deal of available time. However, I consider that to be the student's responsibility rather than the course's. Overall, I am extremely satisfied with the knowledge that was shared. My overall assessment of the course is highly positive, and I firmly believe that enrolling in it was the right decision. Regarding the Program Manager, I have nothing but praise. Isha has been incredibly supportive, always attentive to the students' needs, proactive in keeping everyone informed, and committed to resolving any issues that arose. A perfect 10 out of 10 for Isha. One thought that still remains with me, and which I have not yet fully resolved, is whether it still makes sense to spend so much time learning Python at a moment when artificial intelligence can already produce code much faster and often with far greater expertise than most individuals. I believe that, going forward, professionals may benefit more from specializing in harness engineering than from focusing exclusively on learning Python. Finally, I would like to apologize if my honesty seems too direct, but I understand that feedback is requested with the goal of improving the program. I would also like to thank you personally for your professionalism and kindness. My sincere thanks to Hassam for his outstanding attitude, patience, and commitment to teaching—we learned a great deal from him. I would also like to thank the Johns Hopkins University faculty for sharing their tremendous knowledge and expertise with all of us. I would truly love to continue taking more courses because learning is one of my greatest passions. I would even be happy to help promote these programs throughout Latin America. The internet is full of companies offering low-quality courses, whereas this Johns Hopkins University program is, in my opinion, among the very best available on the market and demonstrates an outstanding level of professionalism. If I had to rate the course on a scale from 1 to 10, I would give it a 9.8. The remaining 0.2 simply represents opportunities for improvement. Sincerely, Miguel A. Contreras

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Johns Hopkins University offers a Certificate in Agentics that is a serious and extensive 16-week program requiring, among other things, three significant projects. The program includes pre-recorded lectures by faculty and mandatory weekly in-person meetings in small groups with industry experts. The code is in Python using LangChain and related tools. Johns Hopkins University has a reputation to maintain, and you can see that in the high quality of the offering. This is a serious program, requiring 1 to 2 hours of work per day to get the most out of it. The projects are challenging, and by the end, you have a solid understanding of Agentics, even though in some ways you are just beginning your journey because AI in general, and Agentics in particular, are rapidly evolving. Most importantly, the faculty updates the course regularly, keeps it current, and does a tremendous job of presenting the material. Highly recommended.

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I've learned a lot about Agentic AI through the Agentic AI Certificate Program from Johns Hopkins University. I recommend this course to anyone who wants to keep their technical skills up to date.

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My thoughts on the Agentic AI certification at Johns Hopkins University so far: I'm really happy with the content. I've already been applying everything I'm learning to my daily work, especially since I can now build and deploy agents designed around these core pillars: policy layer, tools and skills, memory and knowledge, guardrails, and observability. The knowledge in LangGraph has been particularly useful for understanding how to steer agent behavior in production at an enterprise scale. Based on what I've learned, I've already managed to build and launch a few agentic solutions into production, from prompt engineering and multi-agent systems all the way to agent evaluation and deployment using LangGraph.

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This certificate has been awesome and fantastic for me. I was motivated to join the JHU Agentic AI program because I wanted to go beyond the general conversation around Agentic AI and Generative AI and develop a deeper, more structured understanding of how autonomous and multi-agent systems can be designed, orchestrated, evaluated, and applied responsibly in real-world contexts. Given my professional focus on digital transformation, data, analytics, and AI-enabled business innovation, I saw this program as an opportunity to strengthen both my technical perspective and my ability to translate AI capabilities into practical business value. My key learnings centered on understanding agentic AI as a system, not just as a model or a prompt. The program helped me better understand agent architectures, multi-agent collaboration, retrieval-augmented generation, tool use, orchestration, evaluation, safety, governance, and the importance of human oversight. One of my most important takeaways was that successful agentic AI solutions require more than autonomy; they require clear objectives, well-designed workflows, reliable evaluation mechanisms, guardrails, and measurable outcomes. The program has helped me significantly in my professional journey by giving me a stronger framework to design and discuss AI solutions with both technical and executive audiences. It has improved my ability to identify high-value use cases, assess feasibility and risk, and think more critically about how agentic systems can be deployed in enterprise environments. It has also reinforced my belief that AI should be applied not only as a productivity tool, but as a strategic capability that can transform operating models, decision-making, customer experience, and innovation. A memorable aspect of the program was the opportunity to connect concepts with hands-on projects and real-world scenarios. Working through practical assignments made the learning experience much more meaningful and helped me internalize how agentic AI systems behave, where they can create value, and where they need careful governance. Overall, the program was intellectually stimulating, highly relevant, and directly applicable to the work I do with organizations exploring the next stage of AI adoption. I am recommending this program to my colleagues, friends, and customers.

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The certificate marks the end of a program. The learning marks the beginning of a new perspective. Over the past 10 weeks and 60 hours of learning, discussions, and masterclasses, I had the privilege of completing the AI in Healthcare Certificate Program from The Johns Hopkins University. What I'll remember most isn't the certificate itself. It's the conversations. The thought-provoking masterclasses. The challenging discussions. And the opportunity to learn from exceptional faculty, particularly Dr. Ian McCulloh and Dr. Daniel Byrne, whose ability to bridge artificial intelligence, healthcare, leadership, and real-world implementation made every session both insightful and practical. One lesson has stayed with me throughout the program. AI is rarely the hardest part. Leadership is. Technology can analyze data, generate insights, and automate processes. But people build trust. People make ethical decisions. People lead change. This program reinforced my belief that the future of healthcare will not be shaped by artificial intelligence alone. It will be shaped by leaders who know how to implement AI responsibly, communicate its value effectively, and always keep patients, healthcare professionals, and ethics at the center of every decision. I'm grateful to Johns Hopkins University, Great Learning, the faculty, mentors, and fellow learners for an enriching experience. The learning journey continues, and I look forward to applying these insights to healthcare leadership, executive education, workforce development, and the responsible implementation of AI.

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Another milestone in my AI in healthcare journey. I'm delighted to share that I've completed the AI in Healthcare programme at Johns Hopkins University—my second AI-in-healthcare credential this year, following Harvard Medical School's AI in Health Care: From Strategies to Implementation in April. As a physician, I've always believed technology should never replace the human connection in medicine—it should strengthen it. This course deepened my understanding of how AI can be integrated thoughtfully to improve patient outcomes, support clinicians, and build safer, more efficient health systems. Two ideas stayed with me. First: AI is fundamentally a pattern-recognition machine. The human brain cannot process millions of data points and detect every hidden signal. AI can. Used well, it doesn't replace clinical judgment—it enhances it by helping clinicians recognize risks earlier. Second: burnout and chronic stress quietly change how clinicians think. They narrow attention, reduce pattern recognition, and make the obvious harder to see. Metacognition—being aware of our own thinking—is just as important as any algorithm. AI has the potential to support that awareness and help clinicians recognize cognitive drift before it affects patients or themselves. This belief has become the common thread across my work. It's why I wrote The Boiling Frog, why I host the Transforming Stress with Dr. Ash podcast, and why I co-founded the UK Hospital at Home Society. Different disciplines, one mission: recognizing what is slowly drifting before it becomes harm. A real highlight of this journey was welcoming Daniel Byrne, a Johns Hopkins faculty member with more than 40 years' experience in biostatistics and predictive modelling, onto the podcast. We discussed why pragmatic randomized controlled trials are essential for clinical AI, an AI breakthrough that reduced lupus diagnosis from seven years to just 43 days, and what it will take for AI to earn trust at the bedside. My sincere thanks to the faculty, mentors, and fellow learners at Johns Hopkins for such an outstanding learning experience. The future of healthcare isn't simply about adopting AI. It's about leading it responsibly—with evidence, wisdom, and humanity.

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Seven months ago, dinner table conversations about AI felt like listening to a foreign language. Today, I am proud to share that I have officially completed the Certificate Program in AI in Healthcare from Johns Hopkins University. With over 35 years of experience as a pathologist in laboratory medicine, technology conversations at home—led by my son, Mr. Nikhil Jain, and my daughter, Dr. Saijal Jain, who both work with software—used to leave me on the sidelines. While they discussed machine learning, LLMs, neural networks, APIs, and coding, I mostly listened, hoping to bridge the gap. The biggest takeaway? I still don't know how to code, and that is completely fine. Today, I fluently speak the language of AI: its capabilities, its limitations, and, critically, how to implement it responsibly in medicine. At our preventive healthcare venture, Jain Diagnostics and Medical Education and Learning Point (MELAP), we leverage Smart Health Reports to deliver predictive risk scores for conditions like diabetes, hypertension, heart attacks, and strokes, alongside cardiac age and personalized lifestyle insights. Where I once appreciated these tools simply for their utility, I now deeply understand the underlying AI architecture, transforming my entire perspective on preventive care. Above all, this journey reinforced a vital truth: AI is designed to empower clinicians, never to replace them. When deployed responsibly, it supercharges risk prediction, enhances diagnostic accuracy, and elevates clinical decision-making, while keeping human expertise firmly at the helm. Far from being just chatbots and automation, AI represents our greatest catalyst for revolutionizing preventive health and patient trajectories. Enormous gratitude to Great Learning for tailoring this accessible pathway for non-technical medical professionals, and to the brilliant faculty at Johns Hopkins University for making complex technology practical, inspiring, and empowering.

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I am pleased to share that I have completed the AI in Healthcare Certificate Program through The Johns Hopkins University Whiting School of Engineering. This program provided a rigorous foundation for my mission: leading the meaningful integration of AI in healthcare. By bridging the gap between technical innovation and clinical excellence, we can prepare the next generation of physicians to use AI as a reliable partner in patient care. Core program themes included clinical decision support, medical LLMs and generative AI, AI ethics, bias and governance, and strategy and change management. I am grateful for the mentorship of Ian McCulloh and Daniel Byrne. Their focus on human-centered design is key to ensuring that AI-accelerated healthcare remains focused on the patient. I look forward to applying these AI frameworks to advance the future of healthcare.

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I am delighted to have completed the AI in Healthcare Program. As a Community Medicine faculty member and public health professional, stepping into the world of Artificial Intelligence felt both exciting and essential. What stood out to me the most was the clarity with which complex concepts were explained, making them accessible even without a technical background. The program bridged the gap between theory and real-world healthcare applications, from Predictive Analytics to Decision Support Systems. The modules were structured and well paced, with a strong focus on real-world healthcare use cases and academic depth. This experience has strengthened my interest in digital health and AI-driven healthcare solutions. I see immense potential in leveraging Artificial Intelligence for early risk detection and scalable health interventions. I am looking forward to applying these insights in academics and healthcare delivery.

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I am happy to announce that I have completed the AI in Healthcare Program. This experience focused on the real-world deployment of model types that are actively shaping care delivery, such as predictive models for readmission risk, NLP and LLMs for clinical documentation, and decision support systems. Learning from industry experts reinforced where Artificial Intelligence is delivering value today through readmission reduction, workflow optimization, and scalable clinical decision support. A key takeaway is that model performance alone does not drive impact; strong data and clinical integration do. The shift is from building models to embedding them into workflows in a way that is measurable, trusted, and actionable. Organizations that succeed will be the ones that pair clinical leadership with disciplined, evidence-based Artificial Intelligence deployment.

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I am thrilled to share that I have successfully completed the AI in Healthcare Program. As an MD in Biochemistry, stepping into the world of Machine Learning marks a pivotal step in my journey. Throughout this intensive program, I explored how Artificial Intelligence is revolutionizing diagnostics, patient care, and medical research. My focus remains on the integrative future where the precision of biochemistry meets the predictive power of Artificial Intelligence. The goal is not just to treat, but to understand the hidden patterns in human health. Whether it is metabolic pathways or clinical data, Artificial Intelligence is the new microscope for the twenty-first century. I am grateful for the incredible learning experience provided by the faculty.

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I am thrilled to announce that I have completed the AI in Healthcare Program. This experience provided a great opportunity to expand my knowledge of Artificial Intelligence in healthcare and its real-world impact through case studies and mentoring sessions. As a clinician with a keen interest in digital health, I was drawn to insights that go beyond the algorithm, such as Clinical Workflow Integration, Human-Centered Design, and Strategic Impact. I am excited to bring these insights into the digital health space to optimize clinical processes and improve clinical outcomes. I am grateful for the invaluable experiences shared by the faculty and for the ongoing mentorship I received throughout the program.

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When I first started the AI in Healthcare Program, I was uncertain if it would require deep coding knowledge. I knew I needed to understand how Artificial Intelligence works in healthcare at a bare minimum. My experience has been valuable, providing a well-paced introduction to how Artificial Intelligence is reshaping the industry. We covered Decision Support Systems, AI Business Strategy, and the use of Artificial Intelligence in Population Health. The program emphasized the pillars of Responsible AI, from precision prompting to the importance of a cross-functional ecosystem. My key takeaways include the necessity of human-in-the-loop systems and the ROAD framework for accountability. I now prioritize interpretable models over black box systems. I believe the bridge between innovation and improved patient outcomes is education. I am grateful to the faculty and my mentor for this enriching experience.

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I am excited to share that I have successfully completed the AI in Healthcare program. This program has deepened my understanding of how Artificial Intelligence can transform healthcare, from improving diagnostics to enhancing patient outcomes and operational efficiency. Through this learning journey, I have gained insights into the applications of Artificial Intelligence in real-world healthcare systems, data-driven decision-making in clinical settings, and the future of intelligent healthcare solutions. As someone passionate about Data Science and Artificial Intelligence, I am especially interested in exploring how AI-driven solutions can create meaningful impact in healthcare systems and beyond. I am grateful for the continuous support and encouragement I received throughout the program. I am excited to learn and grow as I move forward into the next stage of my career.

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I would be delighted to encourage him to join us, advance his career, and upskill for the Generative AI era.

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I went into the Applied Generative AI and Agentic AI Program not sure what to expect, just wanting to learn enough about AI to leverage in my job. However, I am completing this program equipped with a lot of knowledge. The curriculum is very well balanced between theory and practical knowledge. The instructors, mentors, and Program Managers have been knowledgeable, experienced, and helpful. All issues were promptly and professionally addressed, and communication was timely throughout.

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I am about to complete the Applied Generative AI and Agentic AI Program, and the entire experience has been wonderful. The staff is very responsive to inquiries and always willing to help if you are stuck on a project. The mentored learning sessions are a great reinforcement of course concepts, and the Program Management was outstanding in sending reminders for due dates and answering questions. In my career field of Government Contracting, we are being encouraged to use AI in our daily jobs to improve efficiency, and this program helped me build the confidence and skills needed to explore using AI successfully. I had never used Python prior to this course, but the pre-work was manageable and helped me build a solid foundation. Highly recommend this program to anyone looking to understand AI workflows, agents, and Python.

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The Applied Generative AI and Agentic AI Program directly complements my 28 years of experience in transportation planning, systems analysis, and data-driven decision making by strengthening my ability to apply AI to complex, real-world operational challenges. The curriculum's focus on Large Language Models has provided me with the technical foundation needed to implement Retrieval Augmented Generation, and the confidence to conduct analysis with more reliable large transportation datasets. Hands-on Python development, model evaluation, and AI-assisted debugging naturally broaden my skill set and performance-based planning frameworks. The applied final project brings these skills together. Overall, this program has meaningfully augmented my leadership toolkit and is positioning Generative AI as a force multiplier for advancing my career in transportation planning.

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I had an excellent experience with the Applied Generative AI and Agentic AI Program. The program is highly professional and well organized, making the overall learning journey smooth and rewarding. The faculty were outstanding. They went beyond simply delivering the curriculum, often sharing insights and practical knowledge that extended well past the course material. The mentored learning sessions were particularly impressive and played a crucial role in bridging theory with real-world applications, providing practical examples that are directly useful in a professional setting. I would highly recommend this program to anyone looking to advance their skills in this domain.

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I had a very positive experience with the Applied Generative AI and Agentic AI Program. As CFO and Head of Capital Development and Strategic Partnerships at InMapz Inc., I found the program especially valuable because it helped me connect Generative AI concepts to real business strategy and practical enterprise use cases. What stood out was the balance between technical learning and practical application. The program strengthened how I think about AI in the context of growth strategy, partnerships, investor discussions, and long-term planning. It also gave me more confidence in communicating AI-related opportunities in a thoughtful and actionable way. I would recommend it to professionals who want to better understand Generative AI and apply it meaningfully in their current roles.

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The Certificate Program in Applied Generative AI and Agentic AI delivered exactly what I was looking for. By the end of the program, I had developed a conceptual Retrieval-Augmented Generation (RAG) architecture for a new business venture and gained the strategic framework needed to guide government clients in the safe adoption of AI technologies. The curriculum balanced technical depth with accessibility, making it valuable for both non-coders and professionals transitioning into AI. I had the opportunity to work hands-on with advanced models, including deploying Llama models locally, while benefiting from the support and structure needed to succeed. Overall, I am extremely pleased with the practical value of the program and would highly recommend it to anyone looking to develop hands-on skills in Generative AI and Agentic AI.

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I recently completed the Certificate Programme in Applied Generative AI and Agentic AI, where my project involved building a production-grade AI pipeline to ingest and semantically index 112 research papers and support grant proposal drafting. Using LangChain, Chroma DB, and GPT-4, I developed a seven-stage system covering topic extraction, semantic filtering, compliance checks, human review, and final consolidation. What stood out to me was the programme’s strong focus on foundations, including Transformer Architectures, Retrieval-Augmented Generation (RAG), Fine-Tuning, and Agentic Workflows, along with a thoughtful discussion of limitations and evaluation. As a PhD candidate in Pure Mathematics, I value building AI systems that are explainable, testable, and methodologically grounded. The knowledge and skills I gained through the programme align closely with my work in AI for education, including RAG-based learning assistants, adaptive platforms, AI literacy initiatives, and my book, Generative AI for Teachers.

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The Certificate Program in Applied Generative AI from Johns Hopkins University was one of the most relevant and well-structured programs. As a seasoned professional transitioning into the AI space, this program provided both the theoretical foundation and practical, hands-on skills needed to understand and apply Generative AI tools in real-world environments. The curriculum struck a perfect balance between academic rigor and industry application. I particularly appreciate the focus on Retrieval-Augmented Generation (RAG), LangChain, Multi-agent Systems, and Prompt Engineering, which were all delivered through clear explanations and guided labs. The instructors were outstanding, supportive, knowledgeable, and deeply engaged in current AI research and industry trends. The program not only strengthened my understanding of cutting-edge AI capabilities but also enabled me to build a working Capstone Project that I could use professionally. I highly recommend it to anyone looking to enter the Generative AI space with confidence and credibility.

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I completed the Certificate Program in Applied Generative AI from Johns Hopkins University to gain a thorough understanding of how Generative AI works and apply that knowledge to become more efficient as a Healthcare IT Project Manager. Before enrolling, I had no previous experience with programming or coding and was skeptical about keeping up with the Python modules. However, the Program Manager I interviewed with assured me that success was possible with consistent effort, by watching all the instructor’s videos and attending weekly live sessions with the mentor. That turned out to be true. The instructor’s pre-recorded videos and weekly live sessions helped me build a strong understanding of Python coding and how it is used in Generative AI.

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If you're considering this program, I would absolutely encourage you to take it. AI is transforming every industry, and understanding how to apply it strategically is becoming an essential leadership skill. This program goes beyond the technology by teaching you how to align AI initiatives with business strategy, governance, change management, and value creation. Come with an open mind, actively engage with the material, and think about how you can apply what you learn to real business challenges. The knowledge and practical frameworks you'll gain will not only expand your perspective but also prepare you to lead AI-driven transformation with confidence.

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The Certificate Program in AI Business Strategy from Johns Hopkins University provided exactly what I was looking for: a clear, practical understanding of AI from a business and strategic perspective. As a senior enterprise architect, I appreciated the strong focus on non-technical aspects, including AI operating models, governance, value realization, and organizational impact. The program consistently emphasizes creating real business value rather than pursuing prestige AI projects, and it clearly highlights that high-quality, well-governed data is critical to success. The learning experience was engaging, well-structured, and directly applicable to real-world challenges. I highly recommend this program to professionals who want to lead AI initiatives with confidence and deliver meaningful outcomes.

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I participated in the Certificate Program in AI Business Strategy and it far exceeded my expectations. The program was highly practical and application-oriented, with concrete tools, frameworks, and real-world use cases I could immediately relate to my work. The content was truly state-of-the-art, especially around Generative AI and the strategic use of AI in business. The lecturers were outstanding: experienced experts who combined academic rigor with real industry insights. Equally impressive was the program support team, always responsive, professional, and genuinely committed to helping participants succeed. I would highly recommend this program to anyone who wants to not only understand AI, but also use it strategically to create real business impact.

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I am excited to share that I have completed the Certificate Program in AI Business Strategy from Johns Hopkins University. This journey reinforced something I strongly believe: AI is not a technology initiative, it is a business transformation enabler. Its real value does not come from algorithms alone, but from how leaders align AI with strategy, governance, culture, and measurable outcomes. Throughout the program, I deepened my understanding of how organizations can translate AI capabilities into strategic advantage, make informed and data-driven decisions, and bridge the gap between business vision and technological execution. This experience strengthens my commitment to helping leaders adopt AI in a way that is intentional, responsible, and value-driven. I am excited to keep applying these insights to real-world business challenges.

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My experience with Great Learning, through a 10-week Certificate Program in AI Business Strategy offered in collaboration with Johns Hopkins University, was truly excellent. The curriculum, expert faculty, mentors, and synchronous sessions were all superb—engaging, inspiring, and eye-opening. As a non-expert in the field of AI and computer science, I was able to absorb an incredible amount of knowledge, and I attribute that to the thoughtfully structured design of the program. I personally benefited from all the optional pre-work and readings, in addition to the formal weekly assignments, which were hosted on the Olympus online learning management platform. Having spent over 20 years in higher education, including extensive experience with platforms like Blackboard, Canvas, and other online delivery systems, I can confidently say that, as a student, my experience with Olympus was seamless, highly engaging, and intuitive. What made the experience especially rewarding, beyond the content itself, were two key aspects: the real-world, project-based assignments and the dedicated commitment of the outstanding faculty. They brought global perspectives, deep industry expertise, and a genuine investment in the student learning experience. The projects were challenging. They pushed me to think critically and creatively and to stretch myself intellectually. I also greatly appreciated the detailed feedback provided by the mentors.

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When a friend recommended the Certificate Program in AI Business Strategy at Johns Hopkins, I was a bit skeptical. I am a healthcare lawyer, not an IT specialist. But I was curious how AI would impact my industry, especially its ethics, and I signed up. Three things became clear immediately. One: I did not need to be in the field to learn how to apply AI ethically and efficiently. Two: the faculty is a mix of professors and in-house experts, making the topics very accessible and practical. And three: I will leave this course understanding how to navigate this new and still developing area, even if I do not code for a living. This was an excellent experience.

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I recently completed the Certificate Program in AI Business Strategy by Johns Hopkins University. It was one of the more challenging courses I’ve taken outside a university setting. Looking forward to the next challenge.

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I have officially completed the Certificate Program in AI Business Strategy from Johns Hopkins University, Whiting School of Engineering. The program explored aligning AI capabilities with organizational goals, evaluating risks, and designing strategies for responsible and impactful AI adoption. This experience provided deeper insight into identifying opportunities where AI can drive business value, balancing innovation with ethical and compliance considerations, creating implementation roadmaps bridging technical teams and executive leadership, and leveraging AI for operational efficiency and customer experience. I am excited to apply these learnings to future projects, continuing to bridge the gap between AI technology and strategic business impact.

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The Certificate Program in AI Business Strategy from Johns Hopkins University, Whiting School of Engineering, changed my perspective on intelligent systems. Key takeaways include the importance of governance, bias mitigation, and ethical oversight in AI development. As a UX leader, I now focus on creating transparent, explainable, and inclusive AI systems. The Program's R.O.A.D. methodology (Research, Observe, Analyze, Deliver) provides a valuable framework for building ethical and effective AI solutions. I thank the faculty and the Great Learning team for this impactful learning experience.

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The learning journey of the Certificate Program in AI Business Strategy from the Johns Hopkins University, Whiting School of Engineering, has been transformational. I am excited about harnessing Artificial Intelligence to design real-world solutions. This program is about reimagining possibilities when innovation meets purpose.

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I'm pleased to share that I recently completed the Program in Artificial Intelligence: Applied ML, GenAI, and Agents by Johns Hopkins University. Through hands-on projects in Generative AI, Machine Learning, Computer Vision, and Data Analytics, I developed solutions for real-world challenges across the energy, healthcare, finance, and aviation sectors. This journey strengthened my skills in Python and deep learning, as well as predictive analytics and data-driven decision-making. I look forward to applying these skills to create meaningful business impact. I would like to thank Johns Hopkins University and Great Learning for providing such an excellent learning experience. A special thanks to my teacher, Faraaz Sheriff, and my Program Manager, Nithin Raj, for their guidance and support throughout the program.

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I am Riccardo Cast from Miami, Florida, and I work at Cleveland Clinic Foundation as an Enterprise Infrastructure Support Manager. I recently completed the JHU Professional Certificate in Cybersecurity Program. I wanted to gain a wider knowledge of Cybersecurity and better understand how AI can play a significant role in this field. I now have a broader understanding of Cybersecurity and the technologies shaping the industry. While I am not responsible for the cybersecurity division in my company, I can now better understand what they do and why they do it.

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Hi, I am Qadsia Javed from New Jersey. I work in the Information Technology department of a non-profit organization and hold both an MSc in Cybersecurity and an MBA. I recently completed the JHU Professional Certificate in Cybersecurity Program. I pursued the program to build a stronger professional profile. The program provided good exposure to Azure and included Google and Amazon projects.

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My name is Kurt Demeyere, and I am based in Brussels, Belgium. I am a Senior Lawyer and Head of Knowledge with more than 25 years of experience. I recently completed the Professional Certificate in Cybersecurity. My learning experience was wonderful, and the program provided a valuable introduction to Cybersecurity. The preparatory course helped me build a strong foundation, while the projects allowed me to discover new tools and better understand how Cybersecurity works in practice. I also appreciated the platform, the weekly content, and the support provided throughout the program. Through the seven modules, I learned Cybersecurity vocabulary, developed a better understanding of security concepts, and increased my awareness of Cybersecurity. While I do not intend to become a Cybersecurity Specialist, the program helped me bridge the gap between IT and legal, allowing me to communicate more effectively with technical teams and apply what I learned in my current role.

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I learned a lot about the different aspects of Cybersecurity. I wanted to learn new skills and maybe start a career in the cybersecurity space. I learned how to analyze viruses, set up a Cloud Container, and gained my first look into Penetration Testing.

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My name is Chad Waltz from Des Moines, Iowa. I work at an insurance company as a Software Engineer and currently work with .NET programming while preparing to start with Node.js. I enjoyed the weekly Saturday sessions, and the projects exposed me to new technologies. I have always been interested in security and protecting computers and personal information. I learned by creating Virtual Machines and working on projects focused on viruses and security. We are also starting a central logging system to help troubleshoot issues.

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I am Kojo O. Acheampong from New York City, and I work as an elementary school teacher at Laboratory Charter School. I recently completed the JHU Professional Certificate in Cybersecurity Program and am proud of this experience. I am hopeful about securing a new role as a Cybersecurity Specialist. My journey into cybersecurity was a mix of problem-solving and challenges, but it was rewarding when concepts came together. I chose this program because I wanted a new intellectual challenge, a fresh start, and the opportunity to build a career in cybersecurity. I learned a great deal about Cloud Security, hacking, and using Kali Linux. I am confident that I will apply the skills and knowledge gained from this program in my future endeavors.

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