In this first course of the AI Product Management Specialization offered by Duke University's Pratt School of Engineering, you will build a foundational understanding of what machine learning is, how it works and when and why it is applied. To successfully manage an AI team or product and work collaboratively with data scientists, software engineers, and customers you need to understand the basics of machine learning technology. This course provides a non-coding introduction to machine learning, with focus on the process of developing models, ML model evaluation and interpretation, and the intuition behind common ML and deep learning algorithms. The course will conclude with a hands-on project in which you will have a chance to train and optimize a machine learning model on a simple real-world problem. At the conclusion of this course, you should be able to: 1) Explain how machine learning works and the types of machine learning 2) Describe the challenges of modeling and strategies to overcome them 3) Identify the primary algorithms used for common ML tasks and their use cases 4) Explain deep learning and its strengths and challenges relative to other forms of machine learning 5) Implement best practices in evaluating and interpreting ML models
KV
Great way to get started and introduced to concepts. Project work ensure it covers all the topics taught in the course. Great way to recap and apply concepts to play.
AA
Excellent course, very interesting, useful, well balanced. Very skilled lecturer and the material is easy to understand and fruitful for the graded assignment provided.
JE
I thought the course had a good pace and was informative. I should have took advantage of the discussion forums more to ask some questions. Doing the project brought even more questions.
RR
As a foundation is pretty good. It can be a bit difficult the part of the algebra and the final project, but they provided instructions on how to do it. Just follow the instructions.
NR
A great course for Project and Product Managers. I found the practice questions very effective to think on practical aspects. The content is comprehensive. Kudos to the Trainer.
LS
Good introduction to Machine Learning, which developed further with the ML course project. Overall good learning experience and continuing on with the next course in the specialisation.
TT
I think the course is a little but technical for product managers, I would expect more examples from the real life to be used in industry and less mathematical calculations
AK
The training provides a good overview of ML concepts. At the same time pre-project data quality review and initial data analysis could have a more extensive coverage from my point of view
TR
One of the best courses I have taken on introduction Machine Learning. I enjoyed taking it as it provided good foundation on ML and put everything into perspective.
CS
The course was phenomenal. It provided me with important insights into machine learning functionality and performance. I truly enjoyed completing the final course project.
AM
Awesome content, with a good degree of difficulty, it's been foundational for my deep dive into AI products and have face to face conversations with Data and ML teams
DP
Would recommend - instructor takes time explaining the fundamentals in a simple, relatable way. Didn't feel bored or the need to skip forward and it set a strong base to build on
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This course is WAY too technical. If you are a data scientist, this would be more understandable, but as a Product Manager, it went way over my head.
At the end of the course, the project is far beyond my understanding, and I had to give up. :-(
A lot of good content, but not a great presentation/organization making it hard to be engaging. Especially for working professionals, the presenter's energy level does not motivate them to keep going. You are better off doing a proper AI/ML course instead.
the instructor is reading from a slide,it is not a well prepared course
A very good - and technical- course. It's a bit misleading the refer to this course as "Beginner" level. It's a bit more than that. The one VERY BIG suggestion I have is about the final project. The course DOES NOT provide any support to prepare "beginners" for that project. In fact, some people withdrew from the course in anger over this. What Duke should do is provide a demonstration of how to do that work, whether in Excel or Google or any other tool. I had NO IDEA how to begin. So I spent literally hours looking for videos that demonstrated it. I persevered and got it done, but I think Duke should do more for people who are paying for this. It is really unfortunate to do all that course work and then withdraw at the every end because you lack the support and guidance to complete the final project. PLEASE pass this on to Jon Reifschneider.
Not a bad course but I'm not sure how this it relates to Product Management, aside from some industry examples in the content. For my knowledge level, this was way too dense. Loads of formulas and modeling that I'll never use. The course lost me when it started writing out mathematic equations. I'm finishing the course because of sunk cost bias but would not take it again seeing how the content does not match up with any area of my job.
This course is well structured, covering a lot of what is required high level to discover ML.
Though the level of math required is too high. I don't think this course is for beginners.
Interesting course with a lot of potential, but 3 major feedback points soured it for me: 1. Although this course is explicitly "for Product Managers" in its title, there is no mention of product management or anything specifically relevant to PMs in the entire course. It is really more of a generalist course for anyone. Had I known this, I would have more fully evaluated the complete ML foundations course landscape. 2. The AutoML platform recommended for the final project was sunset by Google, and there's no helpful guide to using VertexAI as its replacement. I and other students (based on the forums) have spent hours and hours trying to debug Vertex errors to no avail. 3. Week 6 suddenly and unnecessarily goes very hardcore into math and calculus relating to neural networks in a very fast pace, without actually explaining or teaching what any of it means.
This is a really good course that provides a solid overview of machine learning and some of the primary methods for doing so. As a product manager, I would say there should be a little less math in the lectures because it distracts from the essence of what you are trying to teach. Overall, a very good course.
The course itself was quite good, a thorough introduction to machine learning. So why the two stars? For the final project, the course offers three possible methods: programming in Python, via VBA in Excel, or using Google Cloud AI. The first two were not an option for me, as I do not code. What I wish I knew was that, in order to make Google Cloud feasible, I would have to spend hundreds of dollars on hosting the (relatively modest) dataset. The course description was not at all clear about this.
Too technical. The quizzes don't align well to the lectures, and the lectures are simply way too technical for most to grasp. The capstone item is absurd for a certificate. I don't recommend this course.
I am writing this honest review as I am standing in front a cement wall of incomprehension and deception. I paid, I did all the modules, made the deadlines, and I passed all the quizzes with flying colors. But I am failing because this course teaches ABOUT different kinds of models and techniques. Not how to build a model. Yet here I am at the last 10% needed to pass the course and I am asked to build a ML model. It's like showing someone a built house in some details and telling them to go buy the tools, the materials and build a house. I simply don't have the resources and knowledge or experience needed. The course certainly didn't provide the necessary tools even after 6 weeks of work. Before I started, I specifically checked who this course was for, what prerequisites and experience was needed to pass and I was told no experience was necessary. Google's course on the other hand offers the same table des matières course but they list their prerequisites and prework necessary to actually complete their course. Turns out you actually need to know programming, be confortable with histograms, algebra and math equations etc etc Here is a star, half to be able to warn others and half for the beige and legit teacher of this course.
Great course and even more applications exemples would be even better :)
Final course assignment should be shared as a PDF file. Not everyone may want their video to be on YouTube, and participation in the video should not be mandatory.
Awesome content, with a good degree of difficulty, it's been foundational for my deep dive into AI products and have face to face conversations with Data and ML teams
Excellent introduction to product management for machine learning. It covers the basics so you can understand the language and terminology of machine learning. Final project wasn't very relatable to the content but was useful in helping design a basic regression model which is just a heuristic model and truly a machine learning model. All in all it was well worth the time and effort and you do learn a lot if you are new to machine learning applications and projects.
It is a good introduction into machine learning concepts that finds the right balance between required depth and and time efficient knowledge transfer.
As the title indicates, it is a good introduction on management level and is not suited to train data scientists.
A negative point: The instructor speaks incredibly slow and is rather unenthusiastic. However putting the speed on 1.5-2 times fixes this.
Excellent course material ,well-structured course work and detailed instructions. Explaining detailed algorithms was really helpful in understanding the core concepts.
Additional information on industry best practices and case studies with industry experts would be more helpful as the course evolves in the coming days.Nice work and thank you!
You may see a lot of mathematic formulas, but fret not. You don't need to memorize any of them. Instructor Jon explains concepts very well and guides you through the course material as well as anyone could. You're in good hands, and you'll gain a good understanding of the foundations of ML.
A great course for Project and Product Managers. I found the practice questions very effective to think on practical aspects. The content is comprehensive. Kudos to the Trainer.
The course is a quick review. It would be better if there is recommended learning web links given at the end of each chapter if someone wants to know further about that concept.