Great Learning

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. 




 

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.

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.

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.

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. 

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.

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. 

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.=

 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.