Very good overview of ML. The GraphLab api wasn't that bad, and also it was very wise of the instructors to allow the use of other ML packages. Overall i enjoyed it very much and also leaned very much
The course was well designed and delivered by all the trainers with the help of case study and great examples.
The forums and discussions were really useful and helpful while doing the assignments.
by Massimiliano C•
very good course, complex topics explained through intuitive and practical use cases, in short time provides an overview on Machine Learning and gives the student the chance to go in depth if necessary.
I liked it very much.
by Kenneth L•
An excellent overview of Machine Learning. Whether catching up with nascent developments in the recent years or first diving in, this class provides a stable, well rounded and well thought out starting point on the subject.
by Enrique d P•
Great! I found it really interesting! It's a great introduction to Machine Learning, different areas, solutions and applications. You can apply different methods to real data, but you only need basic programming knowledge,
by Robin H•
Very essential knowledge about how to get on track of ML and it did very handy for the beginner, who has qualified with the criterions of class candidate. Thanks for the effort in the class arrangement and online teaching!
by Thales P d P•
Excellent material to introduce such a broad topic as Machine Learning. The highlight is definitely the video lectures with Carlos and Emily whom never lose sight of the didacticpurpose and target audience for the course!
by SATYAM S•
An amazing course with interesting content and course structure, an in-depth explanation of various machine learning concepts and multiple worksheets that require hands-on practice of the concepts taught in the lectures.
by Jaisimha S•
Very good course. Great material, good challenging programming assignments. Emily and Carlos are superb. --- > Wow. Amazing. Love it...now you know I'm using all the positive feature words in their sentiment analyzer!!!
by Dinesh P•
This course fulfills its promises. Foundations and relevant tools are introduced via case study. Both theoretical as well as practical reviews are done before leaving for next topic. All in all, good introductory course.
by Andrew T•
I enjoyed this course a lot! The case study approach is very helpful to quickly understand how to apply the theory to the real world problems. The course materials are very well organized, especially the lab assignments.
by Willem v G•
Both instructors are very good at explaining the concepts of ML. Also the practical part of the course working with Python and Jupyter notebooks definitely helps in understanding the concepts and apply them right away.
by Balaji S•
The course is a perfect introduction to machine learning. I hope the upcoming course will reveal the abstraction of algorithms used in this course. The instructors are awesome. The materials are very easy to understand
by Balaji C G•
The case study approach for explaining machine learning concepts is commendable. This kind of approach will not only help in cementing the concepts but helps in making decisions when it comes to real-life applications.
by Robert G•
These instructors are among the very best I have encountered as a veteran of dozens of MOOCs. Their expertise in the subject matter, presentation and pleasant manner made this a highly pleasurable learning experience.
by Abdulrazak Z•
REAL-LIFE artificial intelligence applications. The examples were so good and real match to the reality, so in this course, I wasn't bored by theoretical information but I have seen its benefits with the code I write.
by Daniel A•
Great course covering the key models, concept and applications in machine learning. Instructors showed good pedagogy, teaching complicated concepts in ways easily understood. Requires some basic knowledge of Python.
by Gustavo B•
For me this is the best course for Machine Learning Foundations that I watch. It was challenging for me because I did the assigment with R packages. I hope on the future for doing other courses for the specialization.
by Uduak O•
Excellent course content with emphasis on real-life applications
Great teaching tools and I particularly love the teaching style of Carlos and Emily. Going on with this specialization till the very end.
Great work guys!
by Mehar C S•
It was a really nice way of presenting ML concepts using Case Studies. Giving students an idea of deployment right from the start helps in thinking of an architecture of the system for any project that comes forward.
by Soumen D•
Love the way the subject is introduced. The course increased my interest for machine learning and also made me understand the power of machine learning first hand. Thank you, Prof Carlos , Prof Emily and entire team.
Un curso muy bien explicado, fácil de entender y unos profesores que consiguen mantener la atención y absorberte en el tema.
Lo recomiendo 100% para iniciarse en los modelos y entender los algoritmos simples de ML.
by Brian S•
Loved the case study approach and how it relates to real world problems. Utilizing graphlab also helped abstract away a lot of the details, but I look forward to diving deeper with the rest of the specializations!
by Luiz B J•
Excelent material and instructors. There is at least one of the assignments that needs reviewing because probably the data has changed since the first time the course was offered but the autograder wasn't updated.
by anirban d•
This stream along with Andrew NGs is the best ML course available in Coursera. The lectures, especially from Emily's are one of the best. It is perfect for both experienced and newbies. Thanks, Emily and Carlos.
by Shekhar P•
Awesome course ....Both Professors are very intelligent and teaching perfectly....Step by step explanation and also never feel bore because presentation styles are also very best. Thanks professors and Coursera.
by Aniket R•
The case study approach makes it fun to learn machine learning. The introduction to various topics through specific examples increases curiosity and sets the tone for the following courses in the specialization.