Nice!

Good!

Good

GOOD

good

Nope

good

Good

good

.

-

It's better than the other courses of this specialization, but still I wouldn't say that the course is particularly good. Also, the instructors don't appear to care for the learning progress of the learners. There is next to no help via forums, for example. What I think was good is that the instructor attempts to explain the algorithms of the machine learning methods visually and comprehensively.

What I think is a joke is the way the quizzes are organized. The questions almost never deviate from a 'change a number or copy the code' style. Like this, you do not really learn anything instead of copying code and changing something. The quizzes need some additional parts where it is important to apply what is learned to new contexts. ADditionally, the instructors need to put more focus on explaining what certain parts of the code do and why certain parts of the codes are improtant- Otherwise, this course won't be worth more than learning by doing alone.

Not so happy... it would be a little bit better if I attended this one before the ML course by Andrew NG...

Here, the topics are just introduced and poorly demonstrated using Knime and Spark.

Maybe, I had wrong expectations but, given the course title, you need to push more on Spark and leave the ML introduction to better courses like Andrew's one or a dedicated one.

Don't spare too much time with stuff like Course 2 and get some risks

Some parts of the course are quite interesting, in concrete, the introduction to the Knime tool (so useful and open source tool which I will try to take a deep look on it as the course only provide a slightly overview). Otherwise, i think that the content is not enough, i don´t feel that I have fully understand the core of Machine Learning and its difference with other BD applications.

I would like to give a three-star rating because of the following reasons:

1.Very Few Exercises

2.No challenging exercise

3.Only discussed Decision tree classifier

4.There are other important machine learning algorithms.

5.Overall I don't like the design of this course. It could have been degined to prepare learners for the industrial job

Un curso introductorio a las técnicas de machine learning. Los ejercicios en Knime permiten entender el paso a paso de un proyecto de ML, mientras que los ejercicios en Python son prácticamente replicar el código ofrecido y no agrega valor a menos que conozcas muy bien este lenguaje de programación

I liked the theory parts, but had a to of problems with the hands on exercises: I spent a tremendous amount of time on installing/trying to install the necessary software. And not everything worked properly on my Mac Laptop.

I like this course, but is very old and doesn't have methods for programming like python or other. Please check the content and upgrade the software, for me, it doesn't work Cloudera VM and is very sad. More Quality.

A bird's-eye-overview introduction of the field. It teaches you some terms and it gives you ideas about which fields might be interesting for you if you want to really learn how to do machine learning with big data.

The course start excellent talking about categorical predictions but I would like see a similar explanation for regression or numeric predictions. However, the course offer an excellent quality.

The course tries to cover too many areas of Machine Learning, which ends up reducing the amount of time per topic, as well as the information you'll get to see.

It would have been better if more case studies to work were given. I am surprised that there is no working case study given for regression analysis.

The course as such is not too bad ... BUT it's nearly impossible to do the hands-on exercises as Cloudera doesn't support virtual machines anymore.

The descriptive topics were The Handson exercise could be more elaborative. Many of the commands are just written but not explained.

many basic of machine learning but not so specific to big data, only hands-on with pyspark is big-data related