No Code AI and Machine Learning: Building Data Science Solutions Reviews

I attended a well-structured No Code and Agentic AI course organized by Great Learning, which offered an excellent balance between theoretical knowledge and practical learning. Project-based assignments and regular assessments supported continuous learning and development.
My goal was to explore different areas of AI and gain hands-on experience with modern AI tools. The course provided an excellent opportunity to achieve these goals.

I recently completed the No Code AI and Machine Learning program delivered through Great Learning, and overall, it was a very positive and rewarding learning experience.

**Course Structure and Content**

One of the aspects I appreciated most was the well-structured curriculum and the strong emphasis on practical, hands-on learning.

The program introduced the main tools, KNIME and n8n, early in the course, allowing participants to become familiar with them and progressively develop their skills.

The first part of the program focused on the applications of AI and machine learning, with KNIME used extensively for exercises, hands-on sessions, and project work.

The second part covered the foundations of AI, generative AI, and agentic AI, including large language models (LLMs), prompt engineering, building and evaluating generative AI workflows, working with proprietary business data, and developing autonomous and multi-agent systems using n8n.

I particularly appreciated how the course combined theoretical concepts with practical applications, making it easier to understand how AI can be applied to real-world business problems.

**Mentors and Learning Experience**

The mentors were excellent. They were knowledgeable, approachable, and always willing to help whenever participants encountered challenges.

The hands-on sessions were particularly productive, as they provided opportunities to discuss issues encountered while developing our workflows and receive practical guidance.

Another major strength was that learning materials were made available several weeks before the live sessions, allowing sufficient time to prepare.

The program included three practical projects, each supported by a dedicated discussion forum where participants could collaborate, ask questions, share experiences, and help one another overcome challenges. I found this collaborative learning environment particularly valuable.

**Flexibility and Program Support**

As someone working full-time from Monday to Friday, I appreciated that the live sessions were scheduled on weekends. This made it much easier to balance professional responsibilities with the course requirements.

The program office was also accommodating when exceptional circumstances affected attendance.

I also appreciated the mandatory attendance requirements, as they encouraged participants to remain committed, actively engage with the learning material, and take the program seriously.

**Areas for Improvement**

One area that could be improved is the technical setup documentation for KNIME and n8n. Some sections of the setup guides were not fully up to date with the latest tool versions or interfaces, which occasionally made the initial setup process more challenging than necessary.

Keeping these guides regularly updated would make the learning experience smoother, particularly for participants who are new to these platforms.

**Career Impact and Overall Assessment**

Coming from a professional background in SAP data transformation and migration, I enrolled in the program to gain a solid foundation in AI and explore how these technologies could be applied to business processes without requiring extensive programming knowledge.

The program has achieved that objective. It has equipped me with practical knowledge and tools that I can apply directly to my daily work to improve productivity, automate repetitive tasks, and identify opportunities for AI-driven process improvements.

More importantly, it has given me the confidence to continue developing my capabilities in AI, generative AI, and agentic AI.

Overall, I would rate the program **4 out of 5 stars** and would recommend it to professionals seeking a structured, practical introduction to AI and machine learning, particularly those interested in applying AI to real-world business challenges without extensive coding experience.

As a scientist with a PhD in Toxicology and several professional certifications in Health and Safety, I'm always looking for opportunities to expand my knowledge. I enrolled in the MIT No Code AI & Machine Learning course with curiosity and a healthy dose of skepticism, wondering how much I could really learn without coding.

The course exceeded my expectations. It provided a clear, practical introduction to AI—from Generative AI to decision systems and recommendation engines—and gave me the confidence to start designing an AI-powered version of a childhood game I've always wanted to build.

Great Learning delivered a well-structured curriculum, engaging mentoring sessions, and responsive support throughout the program. Despite the challenge of attending live sessions from the Pacific Time Zone, they were always worth it.

This course has inspired me to continue learning, and it has motivated me to return for more MIT Professional Education programs. I highly recommend it to anyone looking to build a strong foundation in AI, regardless of their technical background

1. I learnt fundamentals of AI/ML, Gen-AI and Agentic AI .
2. The course design is a week of online self-paced lectures that are available. Post every lecture - there are (non-graded) questions that checks the understanding of the learner. After every modules there is an online (MCA) assessment. Every weekend there is mentored learning session, where you can get all the doubts cleared of the past week module and the instructors are very supportive and continuously check for our understanding and open for us to ask any questions. For example, when they were going very deep in to the algorithms and when we gave feedback - they quickly corrected.
3. The curriculum is most relevant - for people who have not done hands-on coding - but would like to know ML/AI fundamental concepts, how to use simple No code tools like RapidMiner, Knime, N8N to get good understanding of how data science work and how it can be leveraged to address real world problems.
4. The projects and case studies that we went through were real-world examples that help you with the business impact of AI in day to day business we touch and feel.
5. Overall course design was done very well and for working professionals - if they can spend 90 mins a day and 3 hrs in the weekend - it should be sufficient.
6. Some suggestions in the spirit of continuous improvement:
a.⁠ ⁠Learners can be evaluated also based on their active participation in the session.
b.⁠ ⁠In the mentored learning sessions - use tools (like slido for interaction, Quiziz / Kahoot for individual grading assessment during the mentoring sessions and use those also for final grading.
c.⁠ ⁠In the Online assessment - provide higher grades for completing the assessment in the first assessment and the amount of time it takes to complete the assessment. 2 attempts are provided to complete online assessment.

Overall a very good course for non-IT and IT Professionals that don't do day to day coding - but would like to have a good understanding of AI.ML fundamentals and how it can be leveraged for addressing business problems and also gain a peek in to the world of GenAI and Agentic AI.

I had a very positive learning experience with the No Code AI and Machine Learning program. The course gave me a structured and practical introduction to AI, data science, machine learning, and no-code tools such as KNIME, RapidMiner, and n8n.

What I appreciated most was the balance between theoretical foundations and hands-on application. The projects were challenging, but they helped me understand how AI workflows can be used in real business contexts — from data exploration and predictive modeling to automation and agentic AI use cases.

The support throughout the program was excellent. The program team and mentor were responsive, encouraging, and helpful whenever technical or organizational questions came up. This made a real difference, especially during the final project phase.

For me, the program was a valuable step in building practical AI skills that I can connect to my professional work and future career goals. I am very glad I decided to take part and complete the journey.

Most of people today speaks of AI and thinks it is just a prompt to input in chatGPT. A very superficial user experience without knowing how it really works. I was the same when enrolling in this course. Now after course end, I got the details of what is needed to make ML and AI work well explained. The first impact was a little hard because it introduced the foundamental maths concepts used by AI models. Honestly, I had to reopen the books from my studies of decades ago, but it was somehow possible to grasp the concepts needed, even if some of them, like linear algebra, were not part of my curriculum. Since the beginning, the mentor teaches useful tools to apply those concetps, and each lesson had its hands on session. In the second half of the course, the lessons becomes more and more practical and less theoretical. Finally I got a valuable knowledge to apply what I learned of AI to business cases and new ideas. I would had never had so much information on this topic in so short time, the result worths the price. Nonetheless it is demanding, it takes more than 10 hours per week to study and prepare the week end lesson, and even the full week ends for 12 weeks in a raw are needed for exercise and study. A big thanks to the mentors and the program team. I have just one improvement that I must mention: some handsout of some MIT professors videos where just the transcript of their videos, with ...so, ehm. well... scattered in any sentence, not very practical to read and study on, my simple advice is to check them out for review, before releasing to the next cohorts!

I recently completed “No Code AI and Machine Learning: Building Data Science Solutions” programme. It was valuable learning experience. It provides a solid introduction to predictive and generative AI, combining theory with practical exercises (projects). It also helps participants build a clear understanding of no code AI tools and how to use them effectively. Overall, it’s a strong starting point for further development in the field.

I recently completed the “MIT Professional Education No Code AI and Machine Learning” program delivered by Great Learning, and overall it was a well-structured and worthwhile learning experience.One of the strengths of the program was its structure. The pre-work helped build a useful foundation, and the mix of self-paced content with mentored learning sessions made the material easier to follow, especially for topics that can otherwise feel quite technical.The rigorous structure—including quizzes, projects, and mandatory attendance for mentored sessions—ensured that we stayed on our toes and remained actively engaged throughout the program. A special thanks to our Group Mentor, Mr. Olabode James, for his expertise in helping us grasp difficult concepts, and to our Program Manager, Ms. Saumya, for her constant support throughout this entire learning journey.The curriculum covers a vast landscape, from Generative AI Foundations to Neural Networks and Recommendation Systems. A tremendous amount of content was packed into these 5 months. Since I retain access to the course materials and recordings, I will certainly be revisiting them to brush up on my knowledge—or as we say in ML terms: Reinforcement Learning.This program was rich, engaging, and truly challenged the way I look at data, fundamentally changing how I perceive AI. For anyone serious about learning and ready to put in the necessary commitment and effort, please go for it. It will change your perspective entirely.

I joined the course with an expectation to learn the foundational details of an AI set up, the evolution, the concepts, the methods and finally how best to apply right AI tools in a given situation. All from Business perspective so no-code, lo-code way. The program delivered exactly that, the instructors, the course material, assessment and project work all followed a well orchestrated path to maximize learnings and retention in most pleasing and efficient manner. The LMS platform, the support team and recordings were immensely helpful to augment the learning. I am a much enriched individual at the end of this program. Thank you Great Learning for delivering a wonderful course!

I successfully completed the program “No Code AI and Machine Learning: Build Data Science Solutions,” and I can honestly say it was an excellent learning experience.
The journey through the different AI and Machine Learning concepts was extremely interesting and enriching, and I learned many valuable topics that I can already see being useful in real-world scenarios.
The lessons were very well structured, and both the instructors and mentors were highly professional, knowledgeable, and detailed in the way they explained complex concepts, making them easier to understand even for participants coming from different backgrounds.
I would also like to highlight the outstanding support provided by the Program Manager, who was always precise, punctual, and supportive throughout the entire program.
Overall, I am very happy with this experience and would definitely recommend this program to anyone interested in exploring AI and Machine Learning through a practical and accessible approach.
Thanks for this amazing travel.

Nicola Grieco

Overall, this has been a genuinely valuable learning experience. A brief note on context: I approached this course with an IT engineering background and some prior university-level exposure to these topics, so I was not starting entirely from scratch. I would encourage prospective students to factor this in when considering whether the programme is the right fit for their starting point.With that said, the course material is well structured and thoughtfully paced, making it entirely compatible with a full-time working schedule, not granted given the depth of the programme. The balance between theoretical content and hands-on project work is well judged: the project complexity aligns closely with what the modules cover, which ensures that concepts are properly consolidated rather than simply read and forgotten.Particularly noteworthy is the recent addition of a GenAI module. It reflects a genuine commitment to keeping the curriculum current, and it adds meaningful practical relevance in a professional context where these tools are increasingly central to decision-making.The support received throughout the course was positive. Live mentoring sessions are a genuine asset, though the experience does vary depending on the mentor. Occasionally, sessions leaned too heavily on quiz-based revision of module content, which added limited value compared to more applied or discussion-led formats. A more consistent approach to session design across mentors would strengthen this element considerably.What I found most valuable, beyond the technical skills themselves, was the broader shift in mindset the course encourages:  learning not just how to use these tools, but how to think about them strategically and apply them effectively in a business context. That dimension alone makes it worth the commitment.

I had a wholesome and good experience at the No Code AI and Machine Learning course, with Great Learning, which I recently concluded. I operate a licensed Money Lending Institution in the financial services sector and I was desirous of understanding how AI and ML can aid businesses, without the need for me to learn how to code. The models that were taught during the Course were very educative and can be replicated, using AI and ML to find creative solutions to scale customer base, deepen customer experiences and significantly improve operational procedures. The trainers were experts in their respective fields and they attended to enquiries appropriately.  In addition, support from the Great Learning team throughout the Course duration was great. Thank you.

As a QA Manager, I enrolled in the MIT No-Code AI program to bridge the critical gap between traditional software testing and the non-deterministic nature of artificial intelligence, seeking to transform my team from functional testers into AI quality advocates. The program equipped me with essential insights into model evaluation metrics like confusion matrices and precision-recall curves, while no-code tools allowed me to validate complex algorithms without the overhead of deep programming. This experience has fundamentally shifted my professional focus toward data quality and bias detection, allowing me to speak the language of data scientists and lead strategic discussions on model drift and retraining protocols. Professionally, this has been a major milestone, facilitating my transition into a QA Architect role where I now oversee high-stakes AI deployments. By applying these learnings, I’ve already successfully mitigated risks by identifying training data biases before production, ultimately proving that a no-code approach can deliver high-level oversight and robust quality assurance in an AI-driven landscape.

I was exploring various AI tools and learning programs to advance my career and stay aligned with evolving market demands. During this search, I came across Great Learning, which stood out due to its partnerships with reputed institutions like MIT Professional Education and other globally recognized universities. The credibility of these affiliations gave me confidence in the quality and industry relevance of their programs.After evaluating different options, I decided to enroll in the No-Code AI & Machine Learning course, as it was specifically designed for professionals who want to understand and apply AI concepts without requiring programming expertise. The course provides a comprehensive foundation in key areas of artificial intelligence and machine learning, making it suitable for individuals from both technical and non-technical backgrounds. The curriculum is structured in a way that balances theoretical understanding with practical exposure through no-code tools, enabling learners to build and evaluate models without writing code. Overall, the course helped me develop a solid understanding of AI concepts and how they can be applied in business and real-world use cases. The live sessions were handled by Great Learning mentors.

I recently completed the No-Code AI and Machine Learning course from Great Learning, and overall, I had a very positive experience. The program offers a well-structured introduction to AI and ML concepts without requiring a technical coding background, making it highly accessible and practical.What stood out to me most was the supportive learning environment. The staff was consistently friendly, approachable, and accommodating, ensuring that any questions or concerns were addressed promptly. This made the entire journey smooth and encouraging.The faculty also deserves special mention. They demonstrated strong subject knowledge and explained concepts clearly, making even complex topics easier to understand. Their teaching approach balanced theory with real-world applications, which added significant value to the learning experience.Overall, I am satisfied with what the course delivered. It provided a solid foundation in no-code AI/ML tools and concepts, and I would recommend it to anyone looking to explore this field in a structured and supportive environment.

First time yanina a course with Great Learning and just want to day thanks because have been a great exlerience, regarding the AI No Code tools for ML and Data Science program provide the perfect introducción to the Data asciende world, giving great tools to reinforce the class knowledge and concepts in a practical way, all the instructors with their vasta experience create the perfect environment for learning.

I recently completed the MIT – No Code AI and Machine Learning (December 2025 – Batch A) program with Great Learning(MIT Professional Education), and it was a highly valuable learning experience.The course is very well-structured, especially for those who want to understand AI and Machine Learning concepts without deep coding knowledge. The content is practical, relevant, and easy to follow, making complex topics much more approachable.A special mention to Bhuwan for his continuous guidance and support throughout the course. His mentorship played a key role in helping me stay on track and successfully complete the program.Additionally, the mentored sessions were extremely helpful. They provided clarity on key concepts, real-world insights, and an opportunity to ask questions and interact, which significantly enhanced the overall learning experience.Overall, I would highly recommend this program to anyone looking to get started with AI and Machine Learning in a structured and supportive environment.

I am a researcher in Education specializing in qualitative video data analysis, with particular interest in methodological innovation and the integration of AI into research practices. I had a genuinely very positive experience with this MIT No Code AI & Machine Learning Program.What I particularly appreciated was the way the program brought together a strong academic perspectives and managerial expertise. This combination created a rich and balanced environment, where conceptual understanding was not treated in isolation but continuously connected to real professional practices.For me, this was especially valuable. The course did not simply introduce tools, nor did it stay at a purely theoretical level. It articulated both. Concepts were grounded, discussed, and then translated into hands-on applications in a way that made sense from a research as well as a professional standpoint.This kind of alignment between theory, methodology, and practice is not always easy to achieve, and I found it particularly well executed here.I also want to highlight the role of the platform and the Great Learning team. The platform itself was intuitive and well-structured, which made navigating between concepts and tools very smooth. More importantly, the Great Learning team created a supportive and responsive environment that facilitated understanding at every step. This combination significantly contributed to making what could be complex material feel accessible and well-paced.