Great Learning

For this course, the collaboration between the MIT faculty and the Great Learning Team felt like a jugalbandi of two maestros! When I decided to pursue a course on "Data Science and Machine Learning," I encountered several options through buzz-feed on social media, ranging from long-term to short-term courses. Despite my two decades of experience in IT Applications design, development, and deployment, this new area seemed daunting. I was unsure where to begin, how to approach it, and the level of commitment required.

When I discovered the "Data Science and Machine Learning: Making Data-Driven Decisions" course offered by MIT IDSS in collaboration with Great Learning, it immediately caught my attention. It wasn't just the MIT brand that drew me in, but also the course curriculum, schedule, fees, and expected daily hours of commitment, which perfectly suited my situation. Although I was excited about my enrollment, I felt anxious since I wasn't familiar with "Great Learning" at that time.

Now, having reached the end of the program, I feel confident in the concepts and skills I have learned. How did this happen? Thanks to the team that put together the course structure, content, and the excellent faculty (both MIT and Great Learning Team), along with the course delivery platform and program support team.

Key Aspects That Made a Difference

• Prework and Initial Steps: Great Learning provided prework materials and pre-work webinars, which helped me set my foot forward comfortably.
• Course Curriculum: The curriculum was designed with weekly video lectures, assessment quizzes, mentored learning sessions, and practice case studies. The team did a phenomenal job curating the content and concepts, balancing depth and the need of the moment.
• Mentored Sessions: I always looked forward to the mentored sessions to see what new insights would be revealed. Each faculty member was unique and special in their delivery, taking the time to answer questions without rushing.
• MIT Faculty Lecture Recordings: The MIT faculty stuck to the fundamentals, explaining concepts with examples and visuals while balancing course content depth. Their expertise and engaging teaching style made complex data science topics accessible and exciting to learn.
• Olympus Platform: The Olympus platform of Great Learning served as a one-stop center for everything, with all sessions and activities systematically orchestrated.
• Program Office Support: The Program Office team, especially Vanshika, was friendly, professional, and easily approachable. She provided tremendous support and understanding with reasonable accommodation when coursework conflicted with my work and personal schedules.

Overall, Great Learning has mastered the art of delivering a seamless learning experience with its well-established processes, faculty, program office, course material, and the structured flow of content. The 12 weeks flew by, packed with content, practice quizzes, and hands-on case studies, leaving me confident in the concepts I learned and well-prepared for real-time projects.

Recommendation

"The best learning experiences are the ones that feel as natural as breathing; seamless, engaging, and continuous." My experience with this coursework resonates perfectly with this quote. I highly recommend this course to anyone looking to quickly equip themselves with the fundamentals of Data Science, balanced content, and hands-on real-time case studies.  Read less

MIT-PE is a fantastic professional education route! I took a summer fermentation technology course from MIT-PE in 2004. From the knowledge I gained in that one summer program, I became a senior scientist in fermentation and bioprocess engineering and was critical in the research and development of sustainable aviation fuel. It was natural for me to decide on MIT-PE when I decided I wanted to expand my skills into data science and learn Python, deep learning, machine learning, and AI. 




It is a super intensive 12- week program consisting of 8 courses with three main projects: a first coding and statistics project, an elective project and a capstone project. The courses are the following: Foundations:Python and Statistics, Data Analysis and Visualization, Machine Learning, Practical Data Science, Deep Learning, Recommendation Systems, Elective Project, and capstone Project. The first week of the actual program, you learn Python. The second week, you learn descriptive and inferential statistics. If you took statistics in college, imagine all that you learned in that one undergraduate course in one semester and compressed it into one week. That can give you an idea of the compression and intensity of the courses.  Each course has 30 to 40 mini videos no longer than 20 minutes. The stats course even had historical background videos. The videos get straight to the point and are very engaging. Each course also has live lecturers. These are world class Massachusetts Institute of Technology (MIT) college professors performing real world research in data science, statistics, engineering and the biological and mathematical sciences. The professors give excellent live lectures focusing on concepts and thinking and really get you excited. here is a different MIT world class professor for each course. Those lectures are each 2 hours long during the week and you might have 2 to 4 per course. Each of the 8 courses also has case studies written in Jupyter Notebooks. On the weekends both Saturday and Sunday you have mandatory mentor 2-hour sessions each where mentors go cases by case through the Jupyter notebooks. During the mentor session is where the ‘Applied Data Science’ happens. You apply the concepts you learned from the professors in the case studies during the mentor sessions. The mentors are live and this is where you can ask highly detailed or code questions.  They offer incredible support through Slack. You are assigned your own program manager. Your program Manager is your cheerleader, facilitator and main support. The Program Managers and mentors are part of Great Learning. Great Learning is a learning facilitator, and they are the producers of all the mini videos that get into the details of the courses. MIT professors provide high-altitude concept live lectures. Great Learning provides all the support, mentoring and tutorials, and detail-oriented training. Great Learning uses real-world industry experts. This is a global industry with global experts in data science. Great Learning appears to be an India based company, so you learn mathematics, statistics, and data science concepts from Indian data scientists, engineers, and computer scientists. And they are beyond excellent! That is the beauty of online education. You can learn from the absolute best leaders in the field. MIT-PE leverages online learning by offering the very best instruction and instructors. The live lectures and mentoring lectures are all recorded that day and uploaded by the afternoon in case you miss them. I was on the west coast, so they were 7:00 am for professor lectures and 6:00 am for mentor lectures. The timing was not ideal, but I did it and it was worth it! They also offer written transcripts of all lectures and mentoring sessions.




 The program offers a lot of support, cheerleading and motivational support and empathy from the program managers is really nice and sometimes required. The mentored learning sessions are very practical, detail oriented and hands on applications of coding the concepts learned by the professors in their live lectures. The lectures from the world class lecturers are conceptual and big picture. The AI mentor is very helpful on the 3 main projects. Gemini is also very helpful. Plan on everything taking twice as long as you originally expected. 




Plan accordingly. I would recommend to anyone who is considering taking this program to understand the commitment required. It is not self-paced. It is rigorous. It is an intense workout. So be prepared.  But it will be one of the most rigorous and rewarding programs you can take. You will learn so much your brain will be exploding! They have complimentary starting courses to get you up to speed before the actual program session starts. Take advantage of the pre-course prep. Learn how to navigate around in Olympus learning platform before your ever attend program orientation. The lectures are usually 2 hours long. They do post recordings of them on the Olympus learning platform. You will probably have to go through them a few times because there is so much great information. Pre-read the class lecture slides and pre-reads before the lectures, watch the lectures without taking notes during the live presentations so you can absorb the concepts, tips and tricks the guest lecturers provide. Then go back and re-watch the lectures to take your notes. Focus on getting the concepts down to take the quizzes. Use the case studies to practice your work in Google Colab and Jupyter notebooks.  Your mentoring sessions on the weekends are also 2 hours long each day and they go through case studies.  Get two monitors. I wish I had had two monitors. One for the mentor describing the case study and one for the case study Jupyter Notebook open and operating in Colab. Just because you go through the case studies with the mentor does not mean you know what you are doing.  Copy the case studies and practice code in the case studies. Hands-on work is essential. This is a rigorous fully immersive learning experience.  When you get to the neural networks and recommenders, plan on doubling the time to complete projects. This is not because of the rigor of the coursework per se it is because you are using shared services in Google Colab.  In fact, once you hit the 5th course in the program, you might want to buy Google Colab Pro for $9.99 per month to get a higher ranking as a shared user and possibly increase your quota. Extract files only once per notebook. Extracting files exceeds your google quota if you do it more than once you will start to have all kinds of problems. This is a Google problem not MIT-PE problem.  When you do your projects, have a clear strategy and plan before you even start coding. Organizing code is important so as you learn the concepts and work in the case studies, pay attention to how the code is organized. It is fun! and model building is a BLAST! Once you are done with the program, you will probably want to go back to everything you couldn't do the first time and improve all your work. You need 60% in every class in the course to get the certificate. That is not 60% overall but 60% per each class and project. You will be pushing for 100%, but when you get down to time and the last few projects, you just always need to remember what you need for the certificate. Data Science is just plain fun! And MIT-PE and Great Learning ignite a passion for data science! So, get in there and just do it!  

The cost of my program starting in September 2024 was $3700 USD. It might be more now. But they do offer early enrollment specials and some different financing alternatives. I just slapped it on my credit card and thought of it as investment in myself. Data Scientists make anywhere from $80K to $500K USD so I figured it was a decent investment. 




 




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 The course was very well organized, and the staff were extremely helpful. I feel much more confident applying for jobs, particularly in data analysis and machine learning, after completing this course. I highly suggest this course to anyone interested in learning more about data analysis and machine learning.  Read less

I recently completed the MIT No-Code AI and Machine Learning course, and I must say it was an outstanding experience. The course curriculum was thoughtfully designed, offering rich content combined with practical, real-life use cases that made learning both engaging and highly applicable. It provided an excellent foundation for developing applications and processes using no-code AI tools.

The instructors, Hussain and Nitin, were exceptional in their teaching approach, delivering complex concepts with clarity and ensuring a seamless learning journey. The course coordinators, particularly Mehak, were incredibly supportive and always available to provide guidance, making the experience even more rewarding.

The program's comprehensive structure, combined with the unwavering support of the team, created a truly top-tier learning environment. I highly recommend this course to anyone looking to enhance their skills in AI and machine learning without prior coding experience. Read less

This course was an excellent learning experience which provided me with valuable insights into AI/ML concepts and no-code tools. The course’s hands-on projects were challenging and engaging, providing me with practical examples to apply the concepts covered. The projects reinforced my understanding and gave me the confidence to work with AI/ML in real-world scenarios.
The Great Learning platform was well organized and easy to navigate, allowing me to focus on the course content. The program support was impressive as well. Their timely and helpful guidance enhanced my overall experience. The combination of a user-friendly platform and responsive support made the process enjoyable.
The course structure was well-designed, with a progression that built on foundational concepts. Each module connected together well, making complex topics easier to understand and apply. This structured approach ensured that the learning experience was both challenging but also manageable.

My primary goals for taking this course were to gain practical experience in applying AI and Machine Learning to business contexts and to develop a more informed vocabulary for discussing these topics with colleagues. This course effectively met both objectives, leaving me better equipped to engage with AI/ML initiatives in my professional environment.
I highly recommend this course to anyone interested in AI/ML with or without a technical background. Great Learning exceeded my expectations, and I look forward to exploring more courses through this platform. Read less

I'm thrilled to share my experience with the AI and Machine Learning certification program offered by Great Learning in partnership with UT Austin's McCombs School of Business. Coming from a marketing background with zero coding experience, I was initially intimidated, but this program proved that with dedication, anyone can master these skills. From the start in May to completion in January, this program has exceeded my expectations with its comprehensive curriculum and real-world business applications. The instructors, all top experts in their fields, deliver complex concepts with clarity and provide invaluable insights from their industry experience.

While the program demands significant time commitment and self-discipline, the investment pays remarkable dividends in knowledge and skill development. Through consistent effort and determination, I'm proud to be graduating in the top 10 of my 60-person cohort with a 4.26 GPA – proof that even complete beginners can excel in this field. Each module builds systematically on previous concepts, creating a solid foundation in AI and machine learning principles. The focus on practical business applications and assignment of usable projects to start your e-Portfolio of work sets this program apart, ensuring that students can immediately apply their learning in professional settings.

As I approach the finish line, I'm amazed at the breadth and depth of knowledge I've gained. This certification has positioned me at the forefront of one of technology's most dynamic and fastest-growing fields. For professionals looking to evolve their careers in AI and machine learning, regardless of their background, this program offers an exceptional pathway to success, backed by the prestigious reputation of UT Austin's McCombs School of Business. Read less

The MIT course No Code AI and Machine Learning: Building Data Science Solutions has been a great experience. The course is well organized, structured, and taught by impressive faculty from MIT and field experts. Learning material, mentored lectures and discussion groups, and hands-on projects are excellent. Learning in a supportive environment with personalized assistance from an assigned program manager is a plus. Special thanks to my program manager, Mehak Gupta, for her valuable support throughout the program.
This course offers real-world business insights into building and applying different machine learning models and leveraging ML and AI in data-based problem-solving. I particularly enjoyed the modules on deep learning and neural networks as applied to complex datasets, as well as learning more about generative AI and all case study hands-on and business interpretation projects. I recommend this course to enhance your knowledge about data science solutions and how to implement ML models in business and academic problems. 



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Just completed the No Code AI and Machine Learning course, and the six-month investment was worth it. As someone who works with AI tool tuning at Google, I took this course to advance my career and better understand my daily work. The knowledge gained has been valuable both for my new business venture and for explaining my role more clearly - especially important given how complex emerging AI positions can be to describe.What I found most valuable was deepening my understanding of statistics in AI and prompt engineering. These seemingly straightforward concepts revealed significant practical benefits when studied comprehensively. If you're considering signing up for any GreatLearning courses, choose a track that aligns with your goals and commit to it. While the course can be challenging, the program administrators are very supportive and flexible! Overall, it's been instrumental in deepening my understanding of AI applications in business contexts. Read less

5 🌟. I attended the MIT course "No Code AI and Machine Learning: Building Data Science Solutions by MIT Professional Education - Digital Plus Programs" through Great Learning, and it was exceptional. The course was well-structured, and the personalized mentorship and dedicated program support helped me manage it alongside my full-time work and travel schedule by breaking it down into manageable bite-sized pieces. Special mention to Madhur Bhanot, my program manager, who was always available to assist me regardless of time zone. Additionally, the course content was comprehensive and engaging, making complex concepts accessible and easy to grasp. The interactive learning experience and practical applications provided a thorough understanding of the subject matter.  Read less

This program employs a superb teaching style. It is a great program if you want to upskill and become more effective in research or industry. The learning curve is not too steep for those with a background in Math, Engineering, Bioinformatics, Epidemiology, or Statistics. 
Please continue the good work. Read less

The Data Science and ML from Great Learning and MIT IDSS is a 12-week program is intense and covers both theoretical as well as practical aspects. The training course materials and hands-on projects gave me greater insights into how to use the libraries and models. In particular, the course content on deep learning, CNN, and recommender systems has equipped me with the skills to leverage these advanced methodologies to enhance my analytical capabilities. The course is practical, engaging and provides data-driven insights into this complex topic. 
I would recommend this to anyone looking to upskill in Data Science & Machine Learning.= Read less

 The MIT No Code AI & ML Program with Great Learning has been a fantastic experience. The course is super interesting, and the content feels really up-to-date and relevant, especially for my work in Marketing Analytics and AI Innovation. From day one, the learning team has been incredibly supportive—they made sure we had access to the platform and answered any questions quickly.The way the content is organized makes it easy to follow. It’s broken into bite-sized modules, so it doesn’t feel overwhelming. Plus, the quizzes and assessments along the way help reinforce the material and make self-paced learning a breeze.On weekends, we get to learn from an industry instructor who goes over all the concepts and walks us through a case study. This part really brings everything together and helps connect the dots. The hands-on projects have been especially valuable—they give you a real sense of how to apply what you’re learning to real-world situations.For me, this course has been a game-changer. I’ve sharpened my skills in data modeling, AI, and data-driven decision-making. It’s made me much more confident in collaborating with our Data Science team and working with AI tools because I understand how things actually work now.If you’re looking to level up your knowledge in data science, AI, and ML without getting bogged down by coding, I highly recommend this program. It’s practical, engaging, and totally worth it.  Read less

The courses are well thought out. The focus is on applying open source tools to create machine learning and artificial intelligence models.
That is not to say that the course doesn’t go in-depth into how the models work and what to look for when training models. The course does go into that with some detail. This helps avoiding the “black-box” pitfalls so many of us have fallen into using tools without understanding how they work under the hood.
The syllabus as well as the content is maintained regularly, all videos I watched made references to pretty recent stuff.

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Participating in this intensive training on AI and ML applications has been the best experience. As someone who leads a data capture and analysis function in the agricultural research field, I am convinced that our success partly hinges on our ability to derive insight and actionable strategies from data in a comprehensive and timely manner. This training, coupled with hands-on projects, has allowed me to navigate opportunities to answer existing and emerging questions. In particular, the course content on deep learning, computer vision, and neural network models has equipped me with the skills to leverage these advanced methodologies to enhance our analytical capabilities. By applying computer vision techniques, I can now process and interpret complex visual data, which is invaluable for tasks such as phenotyping and yield prediction. The knowledge gained from this training has helped me strategize our efforts towards a more streamlined analysis pipeline and nuanced understanding of complex data. I highly recommend this training to anyone looking to deepen their understanding of AI and ML applications, especially in fields where data-driven insights are crucial for success.
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The GreatLearning course was a short but intense 12 week programme that gives a detailed training on the theoretical and practical aspects of Data Science and Machine Learning. They have a great platform and a supportive administrative team that gives that social push to get going. I've enjoyed learning from the great and inspiring faculty from MIT along with the industry mentors. I would recommend them to anyone looking to upskill quickly in Data Science & Machine Learning in a short time. Read less

I can honestly say that the MIT IDSS Data Science and Machine Learning course through Great Learning was one of the best educational experiences I’ve had. I started with a basic understanding of data science, but through the course, I gained real confidence in handling complex topics like machine learning algorithms, predictive modelling, and making data-driven decisions.
The course is impressively structured, and each week introduces new concepts that build on one another. There’s a great balance between theory and practice, which keeps things interesting and engaging. What really stands out are the projects: beyond just a capstone, the program includes three extensive practical projects and numerous practice assignments to reinforce each concept. These projects made learning feel hands-on, allowing me to apply what I was learning to real-world scenarios.
The mentorship sessions were a highlight for me. Having access to industry experts who were always open to questions and willing to give personalized feedback was incredibly helpful. They guided us through challenging areas and provided insights on how the material connects to industry practices. The mentors were not only knowledgeable but also genuinely supportive, which kept me motivated every step of the way.
I’d highly recommend this course to anyone serious about getting into data science. It’s not just about learning technical skills; it’s about building practical, applicable knowledge and feeling prepared to tackle real-world challenges. Great Learning’s team was also fantastic with support, always there to help with any questions or issues. Overall, this course is a perfect combination of depth, rigor, and hands-on experience. Read less

The course takes 12 weeks and includes 6 courses, which are obligatory to be completed. Coursed include python and statistics, data and visualization, machine learning , deep learning and recommendation system. The lectures are lead by MIT instructors. Mentor sessions augment lectures by detailed hand-on seminars. The course is concluded by Capstone project. Throughout the course you are supported by mentors for training and office for admin issues. You also can talk with industry experts from the field for career advise and resume review. This is a great course to get knowledge and experience, do networking and get access to some exclusive resources. Read less

From the moment I contacted them, all the way to the end of the course, the people at Great Learning have been extremely..

1. Clear about the course details
2. Communicative regarding anything related to courses
3. Helpful when I had questions, and they answered them very quickly
4. Kind and empathic when I had difficulties.

First, I completed the MIT Applied Data Science certification course.  The ADSP was quite a challenge, and I worked very very hard to complete it.  I felt really confident in what I learned.  Immediately after that, I noticed that there was another Data Science certification course with MIT.  After speaking with my Program Advisor at Great Learning, I felt that taking this course would greatly benefit me to really solidify what I learned.  The course has some similar content with ADSP, but this course was through the MIT Institute for Data, Systems, and Society (IDSS).  I knew that the course would be just as great as ADSP, so I decided to go for it, and I'm really glad that I did.  I have just completed the MIT-IDSS Machine Learning and Data Science certification course!  
Both courses had the best of professors from MIT teaching us and the content was superb.  Also, the mentored learning sessions on the weekends were so valuable to really solidify the course material from that week.  The Great Learning Mentors were absolutely amazing.  Really knowledgeable, with real-world experience, and very skilled at teaching.  If you didn't understand something, you could send them a chat (during the session) and they would answer the question right then. 

And...I signed up for yet another Great Learning certification course - Microsoft AI Professional Program, and I'm really excited to start that one soon.

I've had a tremendous experience with Great Learning and I highly recommend taking courses through them.  They are organized, professional, and have everything covered for you.

eta:  By the way, Great Learning DOES provide Job Assistance.  I just haven't used it, yet. Read less

This AI course is fantastic! It introduces code-free AI tools, it is perfect for busy professionals. The course structure is very flexible, with top-quality content and support materials, and the team behind it is always available to assist you. The learning platform is excellent, enabling you to study on your own schedule and revisit resources anytime. Highly recommended for anyone looking to gain AI skills without coding!



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