Great Learning

I have been enrolled in the AI/ML course offered by Great Learning for about 5 months now and targeting to wrap up by end of March. Overall, it has been very useful for me in understanding AI/ML concepts. What I find particularly useful is that the instructors/mentors are actual professionals from the industry. They are able to provide guidance based on their real world experience which helps in grasping the ideas better. The weekly quizzes are a good way to validate understanding of the course materials. The videos accompanying them are from instructors who are very knowledgeable and soaking up all that information is definitely challenging, but very useful.  Using Python, to validate models etc., was a lot more easier for me than when I tried using R earlier as part of something else. The projects are tailored to test out the understanding of the concepts even more in depth and take a significant amount of investment in time, but the learning gained as part of that process is difficult to replicate in other ways. The course is hectic without being overwhelming, requires dedication and focus rather than rote memorization. The knowledge gained as part of this course has definitely given me confidence in the overall AI/ML domain and I hope to leverage it to further my career aspirations. 

I recently completed the MIT Data Science and Machine Learning course from Great Learning, and I can confidently say it was one of the best learning experiences I've had!From start to finish, the course was well-structured, engaging, and packed with real-world applications. The curriculum covers everything from the basics of data science to advanced machine learning techniques, making it perfect for both beginners and those looking to upskill.What really stood out was the quality of instruction. The professors explained even the most complex concepts in a way that was easy to understand, and the practical assignments helped solidify my learning. The hands-on projects were especially valuable, allowing me to apply what I learned to real-world problems.The Great Learning platform itself was smooth and user-friendly, and their support team was always responsive whenever I had questions.Overall, this course gave me a strong foundation in data science and machine learning, and I feel much more confident in applying these skills professionally. If you're looking for a well-rounded, practical, and industry-relevant learning experience, I highly recommend it!

I came into this program knowing absolutely nothing about data science, ai or machine learning, embarking on an unfamiliar knowledge journey. What I've gotten out of it is the foundational understanding of this fast-growing and very essential space and the ability to differentiate myself from others in my field. I feel more confident than ever about facing my upcoming job hunt head on because I now have AIML as another tool on my CV and list of abilities - something that felt so foreign to me just months ago. The tremendous support from the professors and my advisor Debanwee made it possible for me to succeed in this program while also accommodating my very busy professional and personal lives. 

Embarking on the Post Graduate Programme in AI and ML with Great Learning and UT Austin's McCombs School of Business felt like setting sail in the middle of a stormy ocean, with no prior experience navigating a ship. Initially, I was overwhelmed and ready to give up. However, witnessing peers ready to challenge themselves and step out of their comfort zones inspired me to persevere. The support from professors and mentors, who delivered concepts with stunning clarity and hands-on practices, gradually anchored my understanding.
The journey was grueling—balancing work, quizzes, case studies, projects, and hackathons was a daunting task, especially with 20+ years work experience in a complete different field. Yet, reaching the first milestone and securing a top 10 spot fueled my determination. A special mention goes to Debanwee, our life-saving program manager, always available to support us like Poseidon in the storm.
With each milestone, I saw improvements and even topped several courses. Starting with Python Foundation and then Machine Learning, Advanced Machine Learning, Neural Networks, Computer Vision, Natural Language Processing, Supervised learning, Ensemble techniques and Model Tuning .... and many more within every courses!! Now, as I near the end, I can hardly believe how far I’ve come. The journey was so enriching that I don't want it to end; I'm eager to explore new horizons and complement this rigorous journey.


This certification from the McCombs School of Business has been a testament to hard work and resilience, positioning me at the forefront of AI and ML. It's been an exhilarating voyage, and I’m excited to continue exploring the vast ocean of knowledge.

The partnership between MiT and Great Learning has proven to be a successful course structure. Offering accessible and consumable learnings, in a fast evolving space that is AI. Great Learnings digitally served platform means professionals and students alike can explore and extend their repertoire for application in real world problem solving. The continuous support of a program manager (shout out to Himani) - keeps the student on track and striving to engage and achieve in their journey to knowledge and discovery. I’ve had a great time learning through employing this service to assist my data science and AI growth. I would recommend these collab driven courses to anyone wanting to level up their game in the new face of Data and Info Tech. 

I was amazed by the resource-rich learning experience provided by the MIT Professional Education program, "No Code AI and Machine Learning: Building Data Science Solutions." This program not only offers the flexibility of traditional self-paced remote learning but also provides foundational knowledge, relevant resources, and career development guidance on the Great Learning platform. Each group is assigned an MIT professor as a mentor who resolves class-related questions weekly in real-time. Our teaching assistants, Ruchika Jain, were incredibly supportive, constantly reminding us of assignment deadlines and exam cut-offs in the group chats, and they were almost always reachable for private inquiries.
In terms of knowledge, every lesson builds on previously introduced concepts, creating a seamless learning journey. The more effort I put into studying, the more I gained. Initially, I struggled during the first three lessons, but with the teaching resources offered by Great Learning, I was able to understand the subsequent courses with ease. Despite having no prior experience in programming, machine learning, or data science, this program helped me build a comprehensive knowledge system for AI. It was an incredibly worthwhile experience!

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. 

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.