Magnífico curso, todo muy bien explicado y con ejercicios geniales.

Absolutely needed introduction to algorithms.

Thank You. :)

Unique, Helpful, I am always eager to solve problems

Good lectures and programming assignments !

it was good learning and excellent

An amazing course for Algo Lovers.

It was a very productive course

Extremely useful course.

SELF PACED BEST COURSE

Exceptional material!

it is a nice course!

Highly Recommended.

Excellent intro

Outstanding

excellent

good

The course content is really great. It helps with learning algorithms in a very neat and organised way.

However, the grading system is little bit hard to use. It is not interactive by any means and breaks sometimes with changing behavior.

Pretty good, lots of good practice but it gets extremely difficult later in the course. And since this is just the first course of the specialization, I think this specialization is going to be tough as hell

language of professor in a dynamic programming part 1 is tough to understand and makes he concept even harder to understand through videos.(this is only for week5)

The lectures are hit and miss. Some are helpful, and others are pretty hard to follow. The coding challenges are helpful.

I'm giving 3 stars out of respect for the hard work the instructors, Coursera community and course mentors put together to make this course happen, but the quality of the course is at most 1-2 stars. I finished 100% of the assignments even though half of that was required to pass the course, and I have a few concerns about this course:

1 - Poor Use of Pseudo Codes. While Pseudo codes are perfectly fine and sometimes extremely helpful, none of the Pseudo codes in this course were intuitive and can be efficiently translated into real codes. First of all, variable names are confusing and do not tell you what this symbol holds at the first glance, just like how the whole course was taught in a mathematical way rather than programming way, variable names are all like i, j, s, t, l .... when we could have made them into something meaningful and readable.

2 - Since only Pseudo codes were given, it's hard for newer students to learn how a working algorithm actually looks like and how it runs at each step. I feel that either you already know how to do it, or you can't come up with one at all before debugging for hours. So it is more important to show something that actually works from the beginning, then students can imitate -> improvise -> create. Again the Pseudo codes are terrible examples.

3 - DP sections were badly explained, really really bad ........ any of the YouTube videos and GeeksforGeeks explanations are 10X clearer and more intuitive. I feel like the instructors just wanted to teach the math instead of how to program. But the math isn't any difficult to understand, the key is to convert ideas into codes, and this part was completely ignored.

There is barely any support for this course. On most assignments, if your code doesn't work, you get zero direction in regards to having any clue on how to proceed.

The way of communicating can be improved, rest is good.

problems asked are not explained properly in videos

Algorithmic Toolbox consists of a series of slides containing slimmed down explanations on introductory algorithmic concepts, followed up with programming assignments. The slides are the centrepiece of the course, as the presenters rarely stray from the bullet points and pseudocode they're comprised of.

I learned a lot during this course. Although, to gain confidence in your knowledge, this is a course that will require you to seek out additional materials to supplement your learning. Perhaps unsurprising being an introductory course, but the presenters struggle when faced with setting expectations.

Throughout the course presenters often gloss over fairly complex concepts, treating them as they were trivial knowledge. This applies to mathematical definitions, proofs where most steps are skipped, tree diagrams without the context of their underlying theory, or bullet points used in place of what could be detailed explanations.

All material is left equally weighted. Rather than providing explanations like: "We don't need to go into detail on this, only x concept from it is important for what we want to focus on. Reference this chapter in this book for more detail." presenters would read mathematical definitions verbatim from the slides and move on. I was often unsure of how much I would need to know about such concepts.

In terms of communication ability, the presenters don't hold up against many of the free/low-cost services I'm accustomed to using, for example: MIT OpenCourseWare, Udacity, edX, Khan Academy, Code School, Treehouse, etc. Perhaps unsurprising, as these competing services often feature professional communicators rather than professional researchers. But the marketplace for quality online education is definitely becoming a competitive one. Users now expect nothing less than presenters with exceptional communication/teaching ability.

In most videos the presenters read verbatim from the slides and motion with their hands to explain concepts that would be better broken down on a whiteboard. Rarely straying from the slides, the times the presenters go into more depth on a concept, you get a scribble in the corner of a slide, lacking the clarity I've come to expect when approaching complex concepts from master educators like YouTuber PatrickJMT or Khan Academy. After a couple weeks into the course, I just went straight to the slides, read MIT's Introduction to Algorithms, and skipped most of the course videos.

But all things considered, the course served as a good curriculum to guide my focus through the introductory concepts, regardless of where I sought it out.