Great Learning

Is a challenging course because you have to be committed with the time it demands. The business cases related to the application of the learning sessions are good and applicable and the mentors are really kind and comprehensive if you are a person who... Read More is working.

Pros would be to increase ability in analyzing data and results, and understand implications by making data-driven decisions to optimize operational performance.
Another pro is to learn how data-driven solutions help identify inefficiencies within healthcare... Read More processes, and allow for actionable insights and recommendations.

The collaboration between Great Learning and MIT IDSS in bringing this course to learners is truly commendable. This program serves as a comprehensive resource for mastering data science and machine learning, striking a great balance between theory and... Read More practical application. The curriculum is well-structured, covering key concepts with real-world case studies and hands-on projects that enhance learning. It provides valuable insights and practical skills essential for making informed, data-driven decisions across various domains. Highly recommended for anyone looking to build expertise in this field.

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... Read More 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 felt truly fortunate to have participated in this online Machine Learning course. The experience was absolutely enriching and exceeded my expectations in every way.
The course structure was clear and logical, with well-organized modules that covered... Read More all the fundamental aspects of Machine Learning. The instructors were exceptional, with a great passion for the subject and the ability to explain complex concepts in a clear and concise manner.
The learning materials were of high quality and always available, allowing me to learn at my own pace. The practical assignments and projects were challenging but rewarding, helping me apply the learned concepts to real-world scenarios.
The learning community was also a valuable part of the experience. The discussion forums were always active, and the instructors and fellow students were always willing to offer help and guidance.
Overall, I'm extremely satisfied with this course and would highly recommend it to anyone interested in learning about Machine Learning. It's been a truly transformative experience that has opened new doors for me in the field of Artificial Intelligence."

I signed up for GL's course on Leveraging AI for Effective Decision Making, offered by GL in partnership with MIT. It was very well organized. The content and delivery were structured really well. It was gruelling but in the end the knowledge gained and... Read More the community built made it truly worthwhile. While the weekly lectures were difficult to attend (especially for working professionals like me), the weekend mentoring sessions that consolidated the weekly lectures and bolstered them with plenty of examples aided in my understanding. The support team is also very helpful and understanding and make your learning journey easier. Whether you are in a managerial position and looking to further your career or a newbie that's trying to get in to this field, I would highly recommend signing up for this certification.

My experience was very satisfying, as I had the opportunity to open my mind to these new technologies, to learn a bit more deeply about their functioning, and at least to understand what is behind them.

It was a very challenging course because of the amount... Read More of information to assimilate; however, the material is designed so that we can review it and gradually understand the different topics.

The teachers and the classes were excellent, and the patience, support, and follow-up from the staff are extraordinary.

I am motivated to continue exploring the material and take future courses.

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... Read More was critical in the research and development of sustainable aviation fuel. It was natural for me to decide on MIT-PE ADSP 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.

Enrolling in the MIT Professional Education - Applied Data Science & Machine Learning program has been a career-defining decision. The program provided a deep dive into machine learning algorithms, deep learning models, and natural language processing... Read More (NLP), offering me a comprehensive understanding of the core areas driving modern AI innovations. The hands-on projects were especially valuable, allowing me to apply my learning to real-world AI-driven applications.

The curriculum was not only rigorous but also highly practical, equipping me with the technical skills required to excel in a rapidly evolving industry. The instructors were incredibly knowledgeable and provided consistent support throughout the program, ensuring I never felt lost—even when tackling complex concepts.

This program has significantly shaped my career outlook, instilling the confidence and expertise to pursue new opportunities in AI and machine learning. If you're looking to elevate your skills and embark on a career in this dynamic field, I highly recommend this program. It offers the knowledge, tools, and support necessary to succeed.

The MIT ADSP program stands out for its thoughtful structure, engaging live sessions, and practical approach through projects and case studies. The live sessions are highlight in the overall program and highly valuable to get great understanding of the... Read More foundational concepts.