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PRO: Good overview and basic introduction of common machine learning techniques.

CON:

- 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?

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.

This course is pretty dense in mathematics concepts for evident reasons but there is a lot of repeat on "beyond the scope of this course" so maybe, the course should focus more on what they want to teach. This is the ninth course I took as part off the IBM professional curriculum and they all are formatted in the same way: the videos explains the concept in simple terms but you are left alone with the hands-on labs where you mostly learn nothing as you just execute the cells one by one until the end where you have to GUESS what should be written when the solution is not in the notebook. Now, during the whole length of this course, the labs are focused on how to create the different algorithms with an abrupt ending but the final submission leaves you having to come back to the week 3 trying to understand how to APPLY the models on another dataset. Given the global level of these courses which is supposed to be targetting beginners, I found the last submission to be harsh and from what I have read for the next course of the curriculum, the next one is even worse.

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...

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!

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.

A quick summary of all classical ML algorithms. Quality video content and quizzes, but severely lacks hands-on projects.

All Jupyter labs are optional and are of "click-through" or "copy-and-paste" nature with no need to write your own code and experiment, which takes away a huge learning opportunity. And there is no auto-grading for your code either.

Makes you wonder for how long will one be able to retain the acquired knowledge without any substantial practice.

Additional notes: although this course in particular is not too bad, I highly advise not taking this specialization due to later courses (#3 in particular) being almost useless. The lack of graded coding practice will make you retain almost no knowledge or practical skills.

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.

The course is good actually. It helps understand the required basics of machine learning. However, I would prefer to recommend this course to people who have basic programming knowledge, since in the labs, it didn't provide any explanation of the code. You need to search and understand the code yourself. I do learn how to use scikit-learn after the course because I study computer science and can read extra documents online. However, sometimes I still feel a bit lost in the code. Hence I don't think that people with no programming background can enjoy the course. To improve the course, I will recommend adding more explanation of the code.

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.

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.