Most of the course is used to advertise GoogleCloud, in particular BigQuery, instead of teaching more general concepts. At least now I know what BigQuery is and, as a python programmer, I won't be using it since I don't want to learn a new language used and managed only by Google. The AI notebooks are awesome though! Exactly the opposite of BigQuery: uses a language and tools that everyone knows (python notebook) but on a virtual machine managed in the cloud. About the teachings, I learn a bit more about time series, but just the tip of the iceberg really, nothing applicable. Looking forward to the real deal, opefully in the next course.

The course isn't really for complete beginner. It requires additional readings and googling on your own to understand the gaps. The labs are great to provide hands-on application (albeit requiring some knowledge of Pandas, scikit-learn and statsmodels) and but I feel that some of the content could have been discussed more in details in the video lectures or as supplementary readings. Nevertheless if you are willing to spend extra time researching to understand better, this course is still great for you.

I found the course introductory, not intermediate. There is no logical connection between the GCP part and its direct application to trading. Simple Notebooks would have been a better choice. The examples are pretty rudimentary but you can get some ideas here and there. The last week covering Deep Learning does not touch anything at all about Trading.

Not a memorable course, but at least it is lightweight. The Google part does not show any value in my opinion, it is like two independent courses in one.

Pulling the lessons and homeworks from other courses was probably necessary to keep the costs down and to make sure everyone is on the same page with the tools and procedures, so you're forgiven for doing that. I am looking forward to the next class so we can hopefully build on ARIMA and go beyond it to explore correlations between multiple time series and also how to do feature engineering that takes into account multiple time scales simultaneously (min, hour, day, week, year).

Like the finance aspect of things but the machine learning part seems to be pieced together from other lecture series and doesn't have great relevance to the trading topic. Also I am not here to learn the way Google do things albeit its power. You can run a separate ad channel if you want but why Coursera?

The last assignment cannot be done in Hong Kong as the Jupyter cannot be opened using Singapore or Australia server.

Also, the AI platform has been upgraded by Google Cloud and it is now Vertex AI and not the AI platform before. The look and the button are placed in different areas.

Very well covered the theory of Training and its representation as a problem solving tool when it comes to predicting future prices, however the Machine Learning Part is not at all integrated to the Specialization (Trading) and also its content is not interconnected!

This course tries to be an introduction but it's not enough to give you an overview of ML and not well prepared also.

Labs are not encouraging to solve problems, it gives very trivial problem without focusing on enhancing your abilities on the learned subject.

Useful as an introduction but feels like a patchwork of components that have not been developed in a consistent manner. For example the week 4 material is by and large a replication of a subset of what is found in the "How Google does AI/ML.

the platform they chose for submitting homework was not very well. most of the time it took me 15min to get to the platform to code the homework. also, it doesn't dig deep into topics, which is fine because it is a comprehensive course.

The course has a few good points, but the lectures are often superficial. Also it seems like they are taking lectures from different courses and assembling them into one class, in a way that the lectures often seem disconnected.

Decent intro for ML but very limited in how it relates to trading. I would not say I feel comfortable creating an algo after this course. Also, felt very much like a Google ad quite often.

A decent introduction but with minimal hands on learning. Most of the initiative is on the student to learn about and apply the topics described here. Also tends to promote GCP a lot.

The course seems incomplete or not organized well. The concepts come and go out of nowhere. I knew lot of concepts beforehand and could figure out, but it was very fragmented.

The course is loosely organized. Some of the concepts in lab have not been gone through in the lecture. Anyway, the lectures presented by Jack is good.

Theory of the financial part was interesting. "Coding" part was more or less running some fully implemented scripts from a github repository.

The course seems to be more focused on advertising google cloud platform and there is hardly any focus on how to use ML or AI for trading.

Good introduction but would be better served with labs that force you to code more rather than do execute pre-existing commands

Too layman. NO real ML techniques for real trading, just some intro that you can easily find on stackoverflows or something.

The course was great from Google's point but from the "New York Institute of Finance", it was confusing and not helpful.

I expected to understand how we'd interact with the exchanges and then run mdeols in realtime for trading outcomes

This is a good introduction course, fourth week completely different or not aligned with course title.

So many words in that course and so little knowledge. For me, it was wasting time on 80%.

Nice theory very poor explaining in application not very useful to make you build a model

Some good parts, but several sections were cobbled together from other courses I've taken