Machine learning skills are becoming more and more essential in the modern job market. In 2019, Machine Learning Engineer was ranked as the #1 job in the United States, based on the incredible 344% growth of job openings in the field between 2015 to 2018, and the role’s average base salary of $146,085 (Indeed). This four-course Specialization will help you gain the introductory skills to succeed in an in-demand career in machine learning and data science. After completing this program, you’ll be able to realize the potential of machine learning algorithms and artificial intelligence in different business scenarios. You’ll be able to identify when to use machine learning to explain certain behaviors and when to use it to predict future outcomes. You’ll also learn how to evaluate your machine learning models and to incorporate best practices. By the end of this program, you will have developed concrete machine learning skills to apply in your workplace or career search, as well as a portfolio of projects demonstrating your proficiency. In addition to receiving a certificate from Coursera, you'll also earn an IBM Badge to help you share your accomplishments with your network and potential employer. You can also leverage the learning from the program to complete the remaining two courses of the six-course IBM Machine Learning Professional Certificate and power a new career in the field of machine learning.
The instructor are great to demo and teach what it is. He sounds professional and the notebook are useful and the example are essential with guiding the questions 1 by 1.
amazing but I think need more real-life examples to connect the idea better
The course is well designed and easy to follow. (communication and feedback mechanism with Coursera could be improved).
Excellent use of labs to study material. Lectures were very informative and quizzes well designed.
It was a very code course, however, it would be nice if the code was available on a notepad while videos played to make things faster. Also, some of the online notebooks were not working.
AN amazing course and contain really time values content only regret is that coursera doesn't come in dark mode
Well-structured learning path. If you dont have previous python experience you can catch up after a couple of weeks as the workflow is similar regardless of the algorithmn you are using
It is a beautifully crafted course that looks at various clustering algorithms. More importantly, show the pros and cons of each algorithm/technique based on different patterns.
Very helpful for beginner but must have some basic knowledge on python and other libraries such as sklearn, spicy, pandas, etc,.... Thanks very much!
Very well presented. This is without doubt the best series for Machine Learning on Coursera.
Fantastic presentations and detailed course material make this course really worth it!
Great course and very well structured. I'm really impressed with the instructor who give thorough walkthrough to the code.
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This particular course as many others in Coursera, provides minimum possible knowledge with the lowest level of course quality. I will elaborate my point as following;
1) Instructor does not even know the actual mathematical foundations of what he is presenting. He provides example notebooks supposedly process a particular data which does even not exist. I personally and very discretely provided my comments regarding his conceptual mistakes in his presentations without receiving yet any feedback or observing a change in course material.
2) The final projects, even though presenters make money out of this course, are evaluated by peers. With that in hand I have a PhD in Physics, but somehow a random course taker who did not even acquire 10% of my math and coding throughout his/her education is evaluating my final project. Moreover, this person does not even understand well what is written in my project and gives me some random grades. As a result, I don't even get a feedback at all about my grade and or details of his/her grading.
Now, let me put these together. Coursera was a go-to place back in time. Nowadays its quality is not even close to be called 'mediocre'. I had the belief that at least some information can be gained and somehow it was worth taking class(es) back in time. After this horrible and totally not valuable experience, I do not think Coursera is doing a notable or at least an average job. I also have no faith in the comments that you guys publish here from your course takers. I have no reasons to believe them. I would like to clearly indicate that I am neither planning to take another course from Coursera, nor I am planning to suggest anyone to take a course from Coursera in near future.
I feel like the instructor's inability to explain things in detail stems from the fact the he doesn't really understand it as well. feels like:
Boss: "hey I need you to present this tutorial"
Instructor: "Sure thing boss, I just need to read it right?"
Boss: "Yes, but you also need to pretend that you actually understand it"
Peer reviews are also filled with a bunch of trolls who will give you a grade of 0 just for the fun of it - this was the final nail for me. I cancelled my subscription.
Really Poor Teaching. Concepts that were clear earlier was made unclear due to poor intuitive examples. Few concepts were taught really well. But especially around the Hypothesis Testing part, the quality dropped very steeply.
Not clear pre-requisites. Instructions far off from the learning objectives mentioned in the beginning which makes it difficult to catch up.
In my opinion this course is really bad, the content was not that good and honestly it is not up to the level of a Professional Certificate.
ADVICE BEFORE YOU DO THIS COURSE -- Look at the assignment and choose a data set that you can work with. Try and replicate the techniques from the explanation videos on your data set as you go through the course and then you'll be pretty much have a completed assignment by the time you finish the videos.
A slight problem with this course is the hypothesis testing bit of the assignment. The problem could be as deep as the ocean. If you choose a data set that you know you can get a good binary test from you'll cut down your completion time without losing any valuable learning experience.
Does not go into detail and explain how to really code for hypothesis testing
One jupyter notebook is not able to run because a dataset and a python module needed for running the notebook is not provided. Lots of classmates ask about help in the discussion forums, however, no TA or any help is provided.
The concepts are not explained in details. The instructor seems to read from a transcript which may not be the best way of teaching. However, content is great and it can help build a strong foundation.
Excellent course . Covers all the necessary information for beginners. Although I noticed people from non-statistic backgorund have a lot of misunderstaning about hypothesis testing and p-values which is briefly talked about in the course. I would recommend bootstrapping for non-statistic background students ( https://moderndive.com/ - Although in 'R', still an excellent site that teaches about bootstrapping in very simple language for beginners. I highly recommend it for all non-stats students)
I have one more suggestion, it would be really nice, if the course can add some examples about usage of hypothesis testing in machine learning besides research purposes like A/B testing, binning of categorical features and so on.
Excellent, very detailed. However, if the lessons can be expand for hypothesis testing and some of their common test like T test, Anova 1 and 2 way, chi square,..it would be better further.
It does provide useful information but not much. There is very less hands-on practice provided.
As with every IBM course, they tell you "not to hard code" but every project/practical exercise from IBM is littered with hard code. To the point where the projects are unable to be completed, without the help from one or two forum posts from a random student who has spent the time to find a solution. This is a growing problem with IBM's courses. I've learned more from other students, finding workarounds for your mess, than I have from the actual course work. Also, the content for this course, and any examples of code, was produced in Jupyter Notebooks. You didn't even create content in your own IDE, IBM Watson Studio, which says everything a student needs to know about IBM products.
This course is not good at all. It is like the teacher is just the reading the screen and you wont understand anything. Not recommended professional certificate too.
Excellent presentation. Learnt quite a lot.
great course content overall. couple thoughts related to improvement opportunities: 1.could you consider sharing more python sample code for each section? These samples do not have to be talked through - just there available for students to download and keep. 2. I had trouble submitting my course assignment initially due to the confusing instructions on the webpage. The page said Additional Comment box was Optional but it turned out that one would still have to put in "No Additional Comments". Otherwise assignment could not be turned in. This was a frustrating experience that could be avoided for others if the webpage instruction was more clear and consistent.
not very clear and not detailed. jumping through courses without teaching basics
The course is exceptional and a huge learning opportunity for Exploratory Data Analysis. The final project is the best part of the course and helps to apply the concepts to real life data.
IBM courses are most valuable courses, quite a lot of learning happens here. I recommend students when it is time to chose a Brand IBM can be considered in top 5 List. Happy learning.
if you really make the exercises and the final assignment the course really contributes you to better understand Data Analysis