I think this course is in the middle of a simple introduction and a practical course. You should not enroll if you expect to be able to be able to build a RL system. You should not enroll if you are expecting some simple intuitive introduction of RL. This is more difficult than an introduction but tells you nothing more than some introduction, so it is an introduction done in a difficult way. I think it is better to avoid it.

Only learned small pieces of concepts about quant trading, reinforcement learning parts are not connected well at all, it's all about advertising Google Cloud services.

Disapponting.

Last project week 3 does not have any connection to the topic.

Most of week 3 lessons are hand waving general recommendations, not real teaching or discussions

I feel deceived.

Really general level concepts and does not go deep into the code of reinforcement models. The labs are scarce and not helpful at all.

I do not recommend this course to my friends.

Exercises are not prepared to help learners to understand ML for Trading.

This course seemed like movie trailer where there many jargons are introduced which are definitely worth but the information on the same is very limited which does not make students comfortable.

This course was more towards introducing the facility in Google Cloud than on the Title of the course.

It was easy to follow but not easy. I learned a lot and I now have the confidence to implement Reinforcement learning to my own FX trading strategies. Thank you so much.

I learned new perspectives of trading - great

It was ... OK. The lectures by the NYIF guy were immediately relevant to me, worth taking the course for. They should just have removed the Google stuff entirely and just started with an assumption of a basic knowledge of ML - just focus on the financial applications. So, bottom line: the good content is good, but mixed with a bunch of generic, time-wasting junk... that at least can be skipped over.

The content was not bad, however it was really oriented towards promotion of GCP services.

Also, there was no tutorial how to really develop a strategy with reinforcement learning ( only few advices).

Very unusual course. Some useful theory on RL but very little practical coded examples of RL for trading. Heavy on pushing Google cloud services.

Really good stuff, thank you! The Deep Q networks were a bit over my head, I will need to keep studying. It was good theory, but I would have like to see these models trade in the markets to really understand how they act in live trading environments.

Very knowledgable theories from Jack Farmer and the AutoML lab was quite straight forward. However, it would've been good to have the week 3 Portfolio Risk Management code added included as an actual lab exercise instead of talking through it.

Great introduction to some very interesting concepts. Lots of hands on examples, and plenty to learn

perhaps an applied trading notebook would have been nice...I understand that liability issues might have arisen, but there might have been a reasonable avenue with repeat disclaimers, etc

Good course introducing concepts in RL. Wish course provided more examples of using RL in stock prediction.

It has good practical stuff, BUT not any practical RL related to trading.

No practical, and useless for people who only wants more details about implementation of RL algo in trading rather than details about GCP.

It's an exceptionally difficult task to predict financial time series, and even harder to design an automated trading methodology that can take into account those forecasts while monitoring the trading environment (trading costs, other traders, sentiment). This final course is an ambitious attempt to expose learners to the most advanced concepts in the field.

To be able to comprehend the Reinforcement Learning materials appears to require expertise in deep learning far beyond sequential models, and also appears to need the volume and integrity of data only available to high-frequency trading firms. Thumbs up to the specialization curators for providing a non-trivial introduction.

The module that rescued the course (and lifted my rating to 5 stars) was the AutoML demonstration. I was reassured to see that Gradient Boosted Trees were chosen as the appropriate methodology, as this is what I have casually observed as being the most effective methodology in use today for end-of-day data problems. Looks like an amazing product, if you have the money!

This content really is ahead of the Business As Usual.

Congrats!

perfect!

Material is a bit of a mix - the content repurposed from other GCP courses doesn't really mesh that well. Last lab is a bit of a disappointment - there's only really one way to approach it given the time available, and it doesn't give us the time to experiment with other ideas. Would've been nice to have e.g. 24 hours for this lab, but that'd probably be considerably more expensive. That said, I got what I wanted out of the course overall, which was a background in DRL that I could apply to my trading

Great course, exactly what I was looking for! But there were some technical difficulties on practical tasks ...

Nice with the RL classes, it is a bit random.

There are some lectures on RL and some on Trading. But there aren't enough materials on the application of RL to Trading. It just talks about some high level concepts on how it could be used. We could get this from any basic article on RL and Trading. Even the last exercise is not RL on Trading. It's just a machine learning exercise to predict S&P500's direction. Basically there is zero example and exercise on RL for Trading Strategies, which is the main topic.