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Hchan

Hchan

Completed ( 20 )

StatLearning: Statistical Learning

Written 3 years ago
First, a disclaimer: the online exercises of this course are extremely thin, so your score in this class is neither necessary or sufficient to gain mastery of the material. It helps if you think of this course as supplementary material for the book (An Introduction to Statistical Learning by James, Witten, Hastie, Tibshirani). In this light, the course becomes an exceptional gem, because the book is really incredibly good. My recommendation is to take the time to read the book cover to cover, trying many of the excellent exercises in it. Then, as a recap or a refresher, go through this online course. The lectures highlight the most important parts of each chapter and are beautifully paced and presented. You will find that they are a perfect complement to the book and many concepts will become clearer and more concretely established in your mind. However, if you try to take this as a stand-alone course, you will be disappointed and likely not learn or retain very much.
My rating
Hchan completed this course, spending 3 hours a week on it and found the course difficulty to be easy.

Learning from Data (Introductory Machine Learning course)

Written 3 years ago
The best online machine learning course I've taken (I've completed courses by Andrew Ng as well as Hastie and Tibshirani et al), this course covers rigorous theory as well as practical aspects, setting you up for a very solid foundation for future study in machine learning. Assignments are challenging and really require you to understand and engage with the material. Prof Abu-Mostafa's teaching quality is amazing and even highly complex concepts are clearly presented.
My rating
Hchan completed this course, spending 12 hours a week on it and found the course difficulty to be hard.

Machine Learning

Written 3 years ago
My rating
Hchan completed this course, spending 8 hours a week on it and found the course difficulty to be very easy.

Applications of Linear Algebra Part 2

Written 3 years ago
Amazing course that teaches you how to "play with" linear algebra and do some very cool things. Tim Chartier takes you through a veritable wonderland of real-world applications and visualizations enabled by the basic results in your typical introduction to linear algebra textbook. Exploratory activities and provided source code help guide you through how to work through your own, personal, versions of each of these applications. One word of warning: this is a course to play with applications, not to learn linear algebra for the first time. Before this course, you should have completed at least one introductory linear algebra course to get the full benefit. Second point: the exploratory activities are exactly that: for exploration. You are given a sandbox and a box of toys and tools, and it's up to you what you want to build and discover. You will certainly get as much out of it as you put in! People looking for rigid, structured homeworks with a correct answer sheet should look elsewhere. For the rest of us, approaching this with a curious mind, this course is the best way to cement those formerly dry and abstract concepts of linear algebra and really internalize and recognize them for the powerful and gloriously fun tools that they are.
My rating
Hchan completed this course, spending 4 hours a week on it and found the course difficulty to be easy.

Mathematical Biostatistics Boot Camp 1

Written 3 years ago
This class moves extremely quickly over the high level topics of statistics. It is focused on applications and there are very few derivations or intuitions. I would advise not attempting this as a first statistics class. Instead, if you are new to stats, learn statistics from a more comprehensive intro class (there are a large number of these available from various platforms like Udacity, Edx, and Coursera). This course is most helpful if you already have prior exposure to the basic concepts of statistics but want a quick refresher to bring yourself back up to speed. In the latter sense it can be quite helpful.
My rating
Hchan completed this course, spending 2 hours a week on it and found the course difficulty to be easy.

Introduction to Recommender Systems

Written 3 years ago
This is probably still the best introduction to recommender systems available, better than some of the textbooks that have been written on the topic. It does an excellent job of covering the basic topics and providing pointers for further study. However in its current on-demand form it suffers from a number of problems in execution. 1. Numerous errors, in both the technical content and the assignments. 2. The on-demand format is difficult to navigate (e.g., you cannot download the lecture slides) 3. The programming assignments do not make use of the coursera unit-testing grader and simply ask you to manually fill in the recommender results, this is both time consuming and non-informative when you get it wrong 4. Programming exercises have been reformulated to be doable in Excel without relying on R or Matlab (I imagine 99% of serious students still end up using R, Matlab or Python regardless, so not sure what their goal is here), so they are incredibly simple. There is no sense of the relative improvements/tradeoffs of each method implemented, since there is no additional code to evaluate them. 5. Extremely long lectures with low information density. The amount of technical coverage is low, and the nontechnical sections are so long that I wish I had a x4 playback speed option or could just read the transcript and move on.
My rating
Hchan completed this course, spending 4 hours a week on it and found the course difficulty to be very easy.

Introduction to Probability - The Science of Uncertainty

Written 3 years ago
Many online courses are watered down in some way, but this one feels like a proper rigorous exercise-driven course similar to what you'd get in-person at a top school like MIT. The professors present concepts in lectures that have obviously been honed to a laser focus through years of pedogogical experience - there is not a single wasted second in the presentations and they go exactly at the right pace and detail for you to understand the concepts. The exercises will make you work for your knowledge and are critical for really internalizing the concepts. This is the best online course I have taken in any subject.
My rating
Hchan completed this course, spending 8 hours a week on it and found the course difficulty to be medium.

Introduction to R for Data Science

Written 2 years ago
This course is actually more like half a course, but it does that half extremely well. This class will give you solid foundations in the basic data structures of R, and it does so very efficiently and very well - just a few minutes of lectures and exercises and you've learnt what is needed - demonstrating the effectiveness of Datacamp's platform. There is no coverage on control flow, functions, or vectorized operations, which is needed for an actual working knowledge of R. I believe the intention is for you to continue your education at Datacamp, but at the moment of writing that is not free.
My rating
Hchan completed this course, spending 1 hours a week on it and found the course difficulty to be very easy.

Intro to DevOps

Written 2 years ago
A very quick 3-lesson intro to the basic concept of devops. Honestly I found the practice exercises to be more trouble than they were worth - they're basically just uninteresting exercises in setting up various environments, rather than actual "how to use it" tutorials. This is understandable for the extremely short length of the course, because a module on each individual tool alone (e.g., Chef/Puppet/Vagrant) would probably cover many weeks per tool. My recommendation is to zoom through the lectures, paying special attention to the overall concepts of the devops movement, and skip the programming assignments. Based on what you need to use at work, you can search around for more comprehensive tutorials on each individual tool - you won't be able to learn them in this course whether or not you spend time setting up the environments provided.
My rating
Hchan completed this course, spending 1 hours a week on it and found the course difficulty to be easy.

In-Memory Data Management

Written 2 years ago
This course is basically an extended tech talk about the architecture of SansoucciDB, which is the R&D precursor of the famous SAP HANA database. Prof Plattner introduces several key design choices in the design of SansoucciDB. Primary among these are the (1) ability to load the entire working store into main memory (2) columnar orientation and the deisgn to support this - dictionaries and attribute vectors (3) additional operationalization architecture such as the differential store and the hot/cold stores. It is important to understand that these are a review of some solid design choices in a single implementation rather than an introduction to a broad range of concepts across the landscape, so the content of the course is a little less general than implied. However, it remains one of the best modern database implementation online courses available at the intermediate level.

The execution of the course is excellent, with very comprehensive reading materials and helpful quizzes at the end of every week. Prof Plattner's lecturing style needs some getting used to since he often does not cover the material in the slides, but engages in a discussion of his opinions and experience that influences these design decisions, often with helpful anecdotes. Don't be put off by this - listening to an industry veteran (with decades of experience as one of the technical founders of one of the largest database systems company in the world) directly discuss their experience and opinions is a rare treat, and is much better than having him just read the basic material that you should have read yourself anyway.

Couple of caveats: (1) I feel this course is biased towards the design of a single system. For example I cannot consider a course to be a good OS course if it only covers the design choices of Windows; in the same way I cannot consider this to be a sufficiently balanced and broad databases course. With that caveat aside, after taking the course you will really understand the design implications of SAP HANA, which is always very helpful for system architects. (2) when the course does digress towards breadth or other technologies, it is at its weakest. For example there's a small section on mapreduce which really does not fit in with the rest of the course. There's a short section explanaining concepts like joins and aggregates but surely anyone taking an intermediate-level systems implementation course would know those very well already, and if not, then the coverage does not seem to be at the level that would be helpful. And so on. Be prepared to skip or skim those sections for maximum use of your study time in this course.
My rating
Hchan completed this course, spending 3 hours a week on it and found the course difficulty to be very easy.

Mining Massive Datasets

Written 2 years ago
Excellent course by the authors, covering the content of the book of the same name http://www.amazon.com/gp/product/1107077230. It is the MOOC version of http://cs246.stanford.edu. Many useful topics in large scale data processing algorithms are covered including mapreduce, pagerank, networks and graph analysis, streaming algorithms, just to mention a few. The level is advanced undergrad or postgrad, with some chapters covering topics in research papers published within the last decade.

Pacing is faster than most other MOOCs (I estimate about 2x the workload of a typical MOOC). But the material is very useful and rewarding. Exercises are comprehensive and the forums are very useful for checking your understanding.

My rating
Hchan completed this course, spending 10 hours a week on it and found the course difficulty to be hard.

Learning From Data (Introductory Machine Learning)

Written a year ago
My rating
Hchan completed this course.

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