8 de sep. de 2017
This course is ideally designed for understanding, which tools you can use to do machine learning tasks in python. However, for deep understanding ML algorithms you should take more math based courses
26 de nov. de 2020
great experience and learning lots of technique to apply on real world data, and get important and insightful information from raw data. motivated to proceed further in this domain and course as well.
por Чижов В Б•
15 de nov. de 2017
Very interesting and informative! The material outlined in the course, difficult to understand, IMHO, but the organizers and the teacher managed to present it in an accessible form. Special thanks to Kevyn Collins-Thompson for his lectures and Sophie Grenier for her work and attention to the forum.
por 251_NEELANJAN M•
6 de abr. de 2020
Coursera has made possible for millions of students worldwide to access the best quality of education through their medium. An opportunity to learn and develop as an individual changes a person's life substantially and most importantly Coursera is providing this opportunity to millions for free.
por Sridhar I•
21 de dic. de 2017
A great crash course in some of the basics of machine learning on Python. Although not explicitly covered, the assignments helped me gain an understanding on the Jupyter framework & pandas.
The final assignment was definitely a cherry on top that let me gain a very vivid insight into the field.
por Jakob P•
2 de sep. de 2017
Fundamental, but still thorough, course in applied machine learning using Python. The lecturer is really good, and the quiz/problem sessions are challenging, but sufficient information is provided in the videos -- a HUGE improvement compared with the first two courses in this specialization.
por Youdinghuan C•
25 de jun. de 2017
This is a great course. Content is highly organized. The amount of lecture material was just about right. The professor is an excellent lecturer. Assignments and quizzes really helped reinforce my learning. If the Autograder is less demanding, this course would have been better in my opinion.
por Andrew R•
24 de dic. de 2019
The Applied Data Science with Python specialization continues to deliver with Applied Machine Learning. Both quizzes and assignments are challenging but exceptionally well architected. I'm walking away with a great deal of beginner to intermediate skills in machine learning and scikit-learn!
por Roger S•
15 de jun. de 2020
Gives a good overview on ML-Techniques. I liked the evaluation part. "Applied" means - they provide no technical/mathematical details of the different methods. You should get it somewhere else.
Everything is well set up. You need the knowledge of the previous courses of this specialization.
por Rajan G•
6 de jul. de 2020
The course was very good. It has covered a lot of topics in a small time and has provided a good insights about all of them. It would be good if some hints can be provided with each question during the assignment as while facing confusion or problem it can help us to progress further.
por Sumit M•
19 de feb. de 2019
This is a very good course about How to apply Machine Learning but I think before taking this course the student should take the Andrew Ng machine learning course by Stanford University to Learn the Important Mathematics behind the ML algorithms
But Enjoyed this course a lot
por Abhishek B•
2 de may. de 2020
The course definitely provided me with great insight. It allowed me to see different things & try out manifold elements in my own projects at work. Getting to know extensively on classification was really good. Just the only thing missing was the same depth for regression problems.
por Mark H•
1 de feb. de 2018
Excellent course! Well paced lectures, challenging quiz questions that also require insight and understanding, and programming assignments with explicit instructions leading to very little auto grader frustration. The perfect python complement to Andrew Ngs machine learning course.
17 de jun. de 2019
Initially i had issues in getting in to video learning mode, got accustomed to it. One of the best way to learn in your own time as and when it suits you. Submission issues got sorted when discussed with peer. Maybe a SPOC for each course can be of more help to do it more quicker.
por Kunal c•
21 de jun. de 2017
Wonderful course. The video lectures are very much to the point and this course is especially useful for someone who is more interested in application of Ml algorithms rather than their development. The intuition for all the algorithms are good and the course is very comprehensive
por XL T•
21 de may. de 2020
wonderful course. It requires a lot of self learning time to be honest. For my case, I have to do a lot of google search and background reading so to keep up to the learning pace of this mooc. However, I am very happy to be able to finish the assignments and it feels productive.
por David H•
4 de ago. de 2018
Helped me to get the solid concept of Machine Learning. Since this course is mainly focused on the ways to use the machine learning skills in the real world problems, if you are interested in the mathematical approach of each skill, you might need to look into the other courses.
por Subham B•
11 de jun. de 2020
Consider about buying this course if you have some pre-knowledge about ML....Understand that this is not a full ML Course, but a course that describes a lot about applications of this and different ML Algorithms. But this a very good course cause it does what it says very well.
por Chrisada S•
2 de ene. de 2018
I really like that this course focuses on the application of machine learning methods, at the same time still provide enough insight of the working of each model. I do have the math background to follow the proofs, but I would rather spend my time doing rather than proofing.
por Angadvir S P•
24 de feb. de 2019
The course was very useful, however, few of the assignments (specifically assignment 2) had a few errors in accurately displaying the question content and grading method was found to be slightly inconsistent with what was asked in the cells (Jupyter notebook).
por Sashi B•
31 de jul. de 2017
One of the best courses I have taken online! The professor lectures are great and very well laid out. The assignments are very challenging and meant to teach you real life scenarios. Highly recommend to anyone who wants to learn the basics of machine learning using Python.
por Atilio T•
20 de mar. de 2020
Excellent course. Not only show how to use python for machine learning, it also teaches the key points in order to achieve a good model. Highly recommended, The instructor provides a clear message about the general idea of machine learning and the most important aspects.
por Tusaddique A A•
20 de ago. de 2020
This course is my first machine learning course. The instructor was very much helpful. Thank you Coursera and University of Michigan for providing this course online to help thousands of machine learning beginners to pave the way of advanced machine learning. Thank you.
por Kristóf U•
8 de mar. de 2018
Really really good introduction to applied machine learning. It resolves the fear from the difficult application of complex mathematical formulas. It demystifies the topic of machine learning and provides a perfect introduction how to approach real world problems.
3 de nov. de 2017
One of the best courses I have ever taken. I wish I would have taken this course earlier. it gives provides you with a lot of practical tools in a shortest time. This course is perfectly designed and the instructor conveys information in the most efficient way.
por Christos G•
1 de sep. de 2017
Following the first 2 sessions of this specialisation, this one seems easy and gives the student a lot of confidence. Make sure you follow the sequence suggested in this specialization, even if you do not plan to continue with Text Mining and Social Networks.
por John B•
18 de mar. de 2018
Challenging but worthwhile mix of essential theory (explained well) and hand-on practice with good, sensible exercises to help one get a confident grasp of scikit learn packages which one can use in the real world. Many thanks to the organisers and Coursera.