The course was highly informative and very well presented. It was very easier to follow. Many complicated concepts were clearly explained. It improved my confidence with respect to programming skills.
I'm extremely excited with what I have learnt so far. As a newbie in Machine Learning, the exposure gained will serve as the much needed foundation to delve into its application to real life problems.
By VIGNESHKUMAR R•
By Lakshmi N•
By Yangala R G L S S K R•
By syed s•
By piyush s•
By Pagadala G s•
By Malte H•
PRO: Good overview and basic introduction of common machine learning techniques.
- The final assignment is peer reviewed! I saw no mention of this before purchasing the course. This means you are at the mercy of other students who may have less experience than you and may notbe qualified in grading assignments. Also it may mean you have to wait a long time before you get your certificate. It would be better to implement a Kaggle-style assessment of the models and use that to obtain a score and turn that into a grade. This would be transparent and instantaneous.
Some of the forum answers provided by the teaching staff are half baked and often inconsistent. e.g. they give example code for making a figure and and also a figure. But the figure is obviously not made with the provided code and the code contains typos. This is frustrating and makes learning harder than it should be.
Some of the code in the lab exercises don’t obey good practices. e.g. in every lab the data is normalised before train/test splitting. In the final project there is a comment that this should be done the other way around (and it really should!). Why not do it the right way in all the examples throughout the course?
By Isabel L•
The course provides a good overview of the topic over 5 weeks plus the project week. With previous knowledge of Python, the coding is easy to follow. The videos are good. However, the Python Jupyter notebooks provided could be significantly improved. The content could be of better quality and more rigorous. The notebooks have many spelling mistakes, few explanations, unnecessary imports, a few bits of code that are incorrect and need to be fixed, some unnecessary or incorrect statements, etc. Some of the exercises proposed in the notebooks are meaningless for learning. Better practice tasks could be thought. Different notebook parts are clearly written by different people with different coding styles, which can sometimes be confusing for the learner. The assessment (classifier of loan repayment data) could also be improved as it was confusing in terms of what data sets should be used for training and testing. Peer-review is perhaps not the best for assessment grading either. Overall I enjoyed it and learnt, it's a good first impression of the subject but I would have expected higher quality of the materials from IBM - Coursera. Also, it would be good if notes or slides were provided.
By Norma L•
There are some labs that are amazing (towards the end) with all the steps explanations and all, but there are others full of errors, without answers, without explanations.
Even the sample notebook for grading your peers is wrong when it uses the split X_train, y_train for training the set after having found the best K, but then as well for all the other algorithms, and in a 1 year old post even a teaching staff agrees with this.
Also final lab is not properly explained leading to people not understanding what they need to do and resulting in very poor final projects
I´ve enjoyed the course anyway, because I´m more than capable of see what´s an error and what´s not and to find my way through all the flaws by digging in the internet and all, and because I love the subject
But given that we pay for the training, and many of this errors have been highlighted for months and even more than 1 year, I dont get this not being sorted.
Also the lack of support of the teaching staff has been amazing...
By Fuxia J•
The video lectures are informative and rich in information. Generally speaking the labs have way more glitches than the previous courses that I've taken as part of the professional certificate program. As I can see from the discussion forums, many of the issues had been raised more than 2 years ago, yet there did not seem to be effort to fix them for the newer students. Although teaching staff was able to answer some questions, it took a lot of struggle and waste of time to figure out things. I strongly recommend the teachers and/or the teaching staff periodically and more frequently review and update any issues that are raised both in the discussion forums. I did learn a lot from the course but expected a better learning experience!!! Thank you!
By Piyush G•
Though this course is a good introduction to machine learning concepts, but i believe it was a little superficial about the inner working of the core concepts( evades the relevant mathematics on many occasions).
What you will learn: An overview of the working of various elementary ML algorithms from data wrangling to implementation.
What you won't learn: The maths behind various learning techniques.
Suggestions to improve: Implementation of the Algorithms from scratch, emphasizing the mathematical background of each technique would help a lot to the first time learner, though it might narrow down the target audience a bit, but would be much beneficial to those who are willing to put some extra hours to brush up those requirements at their own end.
By Areeb A•
This course excellently explained the mathematical and theoretical foundations behind some of the machine learning algorithms, but how to program these algorithms in Python was not explained in the videos and it was left to the viewers to learn themselves in coding assignments, which is the disadvantage of this course. I was just able to do it because I previously had learnt upto some extent from some other websites.
So my advice is that if you still want to take this course, then after learning python, learn python libraries of Pandas, Numpy, Scipy and Matplotlib, and after that learn the sklearn libraries along with some theoretical background, and after that enroll in this course.
By Venkat N N•
Course provides a good introduction to different machine learning algorithms, how they work and when they can be used. Prior math knowledge will be more helpful in following algorithms and understand each of the algorithms in detail, though it is not necessary since libraries implement the same. Labs were power packed and contain a lot of code that is not covered in this course. Labs assume prior python knowledge with some of libraries used.
Overall i enjoyed the course, but had to look up online to understand some of the concepts explained and also more detailed comments in the labs would have been helpful.
By Muhammad A S•
The difference between teaching and taking quizzes and final coding assignment is too big because you make it optional to see the coding in the lectures and in final assignment you give a huge assignment which is technically not equivalent to the teaching process. So, my advice is that please make the lectures more attentive or make the programming exercises more compulsory and more suggestion and hints to understand it better, so that we can actually do the final assignment on our own. I have completed 8 courses of IBM Data Science specialization, believe me I have faced this issue in almost all of them.
By Aime L•
The videos are fantastic at explaining the concepts, and all the practical work is in the lab (sometimes there's no overlap in content other than the subject). However, the forum is mostly useless as there are few answers by staff, and a couple answers are links to other forums where you still have to figure out what the answer is among the posted discussions. Some of the labs have broken links or deprecated code. The final assignment is a nightmare, the instructions are very general so while not hard you can get to the final results in multiple ways and therefore peer grading is complicated at best.
By Niko J•
Great course for learning ML with Python BUT includes surprisingly many mistakes and typos. Even in the final test there are very misleading copy/paste type of error in the description of the assignment. And many students in the forum have point out those mistakes already two years ago. Not fixing those clear and well reported errors is weird move from the creators and stops me giving more than 3/5 for otherwise superb course.
By Eric G•
The parts on regression are previously covered in other courses that are part of the IBM Data Science professional certificate. Overall, there is a lot of information covered in this course but it feels rushed and done in not enough depth. It is an ok course for an overview of machine learning methods, but sits in a weird spot of trying to be too broad while being detailed, but too shallow for a rigorous study of each method.
By Alex M•
I understand that this is a higher level course, so it may be designed in such a way to require learners to take bigger leaps, but I did not feel the explanations of what was required on the final were very clear, and once I graded other people's finals, it was clear that it was not clear for almost anyone.
Not a terrible course, the material and the topics were good, but better explanations are needed, I think.
By Advaith G•
While the course does give a pretty good introduction to the concepts behind most machine learning algorithms and enables us to realize how ML works, the problem lies in the code. None of the code is explained in detail, so the course is extremely theoretical. It basically tells you to copy the code for your own use with small edits but does not explain how to write the code in the first place.
By Ankur G•
A good course to learn know-how of Machine Learning using Python language so as to facilitate analysis and visualization of data to make effective decisions. I thank the professors to make this course interesting and worth it. Only thing is, videos can be made in a better way so as to facilitate people with non programming background. Maybe some basics of programming would help.
By Harry T•
Good introduction, but not complete.
The course does well in introducing Machine Learning, and covers a good range of classification algorithms. However I feel doesn't go the full length. The labs very briefly cover implementation but I find that it falls short. There's a lack of polish in the material, while typos are minor, the labs are can be jarring and hard to follow.
By Nicolas F G•
The course gives a useful insight into machine learning algorithms and model creation using the python library sklearn. I liked the content, even though a little bit more mathematical background would have been nice. The exercises were good, but there was much of it already written in advance for us to use, so I didn't learn as much as I would have liked to.
By Sergio T•
The course presents a useful overview of basic machine learning techniques without going into mathematical detail. The weekly test questions can be improved to assess the non-qualitative aspects of the topics covered. Using scikit-learn is well illustrated by labs using Jupyter Notebooks. There is plenty of room to update and improve the contents.
By Chetan K D•
Overall, I found this course to be enriching. However, there were more than a few errors and unclear directions in instructions for the final assignment. I hope that the course team is/will update the assignment instructions so that they are in line with current version of the required libraries and will make the instructions more precise.
By Sean D•
Very much enjoyed the course and am thankful for the great content, however the peer-grading process created some unnecessary headaches. On how to improve this I posted in the forum here: https://www.coursera.org/learn/machine-learning-with-python/discussions/weeks/6/threads/JmWRnLUqSfClkZy1Kinw6Q
Thank you nonetheless for a great course!