Chevron Left
Back to Introduction to Machine Learning

Learner Reviews & Feedback for Introduction to Machine Learning by Duke University

2,518 ratings
592 reviews

About the Course

This course will provide you a foundational understanding of machine learning models (logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, etc.) as well as demonstrate how these models can solve complex problems in a variety of industries, from medical diagnostics to image recognition to text prediction. In addition, we have designed practice exercises that will give you hands-on experience implementing these data science models on data sets. These practice exercises will teach you how to implement machine learning algorithms with PyTorch, open source libraries used by leading tech companies in the machine learning field (e.g., Google, NVIDIA, CocaCola, eBay, Snapchat, Uber and many more)....

Top reviews

Aug 4, 2020

I felt that I took the best descition in taking this course, because the professors took this course with atmost clarity and made even the difficult concepts understand easily.\n\nThank you Professors

Nov 26, 2020

Thanks Coursera and Duke University for this course. It is very insightful to get understood the basics of ML and applied ML in numerous fields. It really made me to move ahead with ML domain.

Filter by:

26 - 50 of 594 Reviews for Introduction to Machine Learning

By Tunde O

Apr 21, 2021

Very instructional with lucid explanations, the hands-on practical or lab sessions helped me to actually practice what I have learnt.

By Sameera K

Sep 19, 2018

Very Good course explaining the theoretical concepts related to deep learning . Thank you

By Tarun Y

Apr 22, 2019

A very fine tuned Course,used as a warm up course for deep learning,highly recommended

By Sean C

Dec 23, 2020

Labs are great hands-on training, but the lectures and lab texts don't sufficiently prepare the student for the assignments. Watching them and reading the text will not give the student the skills to solve the assignments, forcing the student to search online for a better tutorial. I recommend providing the complete code to create a MLP, CNN, SWEM, RNN, LSTM and GRU.

With a basic template created, the lab questions can then have the student change epochs, batch sizes, etc.

I am aware that some basic templates were provided, but providing a SWEM and having the student convert it to a RNN is a huge jump. I took detailed notes during lectures and read the lab notes, but ultimately had to find other resources to complete the assignments, because the answers were not provided by this course.

I hope this critique does not come across as a personal attack. I teach electrical engineering and understand how difficult it can be to fully explain complex topics. Thank you, all four of you, for creating this course!

By Dziem N

Apr 22, 2020

I would like to thank Prof. Carin for a very lucid and intuitive explanation of the major concepts in Machine Learning covered in this class. This is the best explanation of the concepts of CNN and Reinforcement Learning that I have found so far !!!

I am also a little bit disappointed by the set of Programming Exercises at the end of some the lectures by other teachers. I think instead of giving students examples of programming using raw, low-level TensorFlow APIs because it overwhelms the main concepts. It is better to use high-level back end tool like Keras (NOT Slim !!!)

By Nam N

May 12, 2021

Course gives us the fundamental knowledges of Deep Learning (mainly) and Machine Learning, I see it very clearly and easy to understand, the Instructors are very dedicated, especially Larry . But the disadvantage of this course is that you maybe gain nothing about Pytorch, as all the lab/assignment are optional. Yeah, there are no practical lesson at all!

If you are the type of learning-eager, this course is also good for you. But if you want more pressure in learning that needs you to exert, I will not recommend it.

By Chen S

Feb 24, 2020

It is a very basic introductory course to important fields in machine learning. It tells important models like CNN and RNN and LSTM. but it does not go deeper into the technical levels of these models. Some parts about mathematics are not very satisfying. Also I feel like the course doesn't provide enough training for the coding work. Nonetheless, it is a good course to start with machine learning and the instructors repeat the concepts from the previous class, which helps me a lot in understanding the concepts.

By Noah R

Apr 5, 2019

Great course for beginners, did a lot to fill in the gaps in my knowledge. There could be a little more help with the actual coding parts of the project, the work done in ipython notebook is largely self-taught.


Oct 24, 2018


By Rasmus R

Apr 15, 2020

The practicals are not at all aligned with their introduction. Specifically, in 2B you're asked to perform something that hasn't been introduced, and 3B could really use some hints. Also, you have no way to ensure that you actually complete the practicals as intended.


Jul 16, 2020

The codes can be explained in videos rather than giving them in texts in the open lab. This can make coding even more understandable and applicable.


Apr 17, 2021

The concept is explained in a great way but i didn't understood even a single part of programming part as no one explained that

By Umme A

May 8, 2020

Too tough

By Aimee M

May 20, 2020

I was an engineering major at Duke, but never took any sort of computer science/machine learning classes because I didn't have time. This class was super straight forward. Everything just made sense. I don't know how to say it other than that. It was great to see how much of the math and signal processing things I learned could be applied to something like machine learning. Before this class, I had no clue what machine learning was, and now I feel like I understand the main gist and the basis for all of the math behind it.

By Soni K

May 15, 2021

It's a very informative and well structured course for beginners. All instructors has made the entire course easy to understand with various real life examples and implimentation. I am very grateful to Duke University to come up with such an introductory course and I am thankful to all the professors of the University to make it easy for a beginner to understand and follow. And lastly I applaud for the coursera team for providing educational platform and resources for the learners.


May 12, 2021

very helpful course and all teachers are very expert and their teaching method is also simple but very helpful. I'm happy to take this course.


Shivam Tyagi


May 4, 2021

Really loved this course. Duke university and the course instructors has done a great job in compiling and presenting this course. This course really is a gateway to the vast and ever developing world Machine Learning and AI. The use of real world examples and latest methods along with presentation of history of ML makes this course provides a solid understanding of ML basics. Best courses one take to get started in the field of Machine Learning.

By José E G P

May 15, 2021

In my opinion, this is an excellent way to be introduced into Machine Learning. The course concerns itself mostly with exploring concepts and the theory behind the most simple and representative neural networks of the last 20 years. Also the explanations are developed with excellent visual material and there are some PyTorch labs provided with the intention to give an insight to the development side of machine learning models.


May 25, 2021

Excelent course. However, it would be a better course with more REAL LIFE examples of applications of the models. In other words, in the sections where it is explained a LSTM put words in vectors and make the operations with a set of words. I know there was an example like that but it was very simple. The Model LSTM is very difficult so it would be nice to have an example more extended and described. Thanks.

By Ozair K

May 11, 2021

I am very happy to be a part of this amazing course. I was looking for it, and this course taught me more than my expectation. It gave me a tough time, but I manage things successfully and finally got my certificate. I would like to mention that professor Lawrence Carin was absolutely amazing throughout the course. I really thank him for his stunning contribution to this course.

By Remi C

Jul 19, 2020

Very nice introduction to machine learning with great exemples and teachers. Each lab time (1h each) was overly underestimated in my case for a newbie, 1h would translate into half a day or a full day. And I think a lot more could be explained about PyTorch coding exemples given in the labs, like the choices for filter size dimensions, but overall it was doable.

By Ivan R

May 15, 2021

It's a really great course to introduce to machine learning. The concepts were well explained, without deep into mathematical demonstrations. Also, I think useful the brief tutorial of Python at the beginning, mostly for whose haven't a good base in Python. But the assignments seem a little bit difficult with respect to the theory.

By Sanjana G

Jun 24, 2020

Really well explained! It was very interesting to learn and as it was from the start it was easy to understand as well. Just one suggestion for the programming assignments also provide the solutions and explain how the coding have been done as understanding that is a bit difficult. Else it was a great course and i really loved it.

By Nikhil P

Apr 11, 2021

Very well explained with programmed slides and excellent narration of each topic. Clears the very basic concepts of Logistic regression, Multi layer perceptron, Convolution Neural Networks, Image and text processing. Each week progress gives more confidence.Thanks For giving me the opportunity to be part of this programme.

By Le M T

May 19, 2021

This was my first serious and systematic study of machine learning. By this course, I also started learning Python which I wanted to do a long time ago. Though I am still an amateur, all the knowledge I've got from the course is really helpful in the today world. Thanks Coursera and Duke for bringing us such good courses.