This course is a pre-sales demo of BigQuery. I thought GCP was partnering with the NY Institute of Finance to create the labs on VMs using GCP (which makes sense), but it turns out that the core information about the introduction to trading and ML is minimal, about 20 minutes of substance per week and the rest of the time is Google advertisement for BigQuery and GCP. The skewed theory from the videos ends up with a Google product guide of a specific, more skewed way of running a feature of a product. I thought this course would give me a proper introduction of ML for trading but instead is more focused on the google ecosystem.

I would not recommend this course unless you want to learn more about Google Cloud Platform, BigQuery, TensorFlow or any other google product. The NY Institute of Finance fails to deliver the theory, it seems like google is telling the instructor what to talk about so google can show and tell about its products.

You are better off buying a good book about ML for trading and read the google documentation about the products they have (if you want to use google), it'll be cheaper and more time effective.

Many very interesting concepts, presented in a VERY superficial way. In the practical part, executing a series of instructions that have not been explained is not a synonym for learning. And if we consider that many errors may happen at every step, and there is no contingency guide, that all is terrible!

This is not a good course: a little bit of everything: Frontal theory lectures without much interaction. Exercises that need more copy paste than they provide leanings and commercials for Google. It seems like the units have been reassembled from other courses.

Though the introduction said the course would focus on just trading and ML, it really is just a set of disjointed modules, often with nothing to do with trading, with little to keep it together. There is a lot of repetition between modules and they really look like they have just been pulled out of other courses. In the final week, the one video even mentions and exercise that does not exist.

This course is very introductory and sometimes sounds more like advertisement of GCP than a lecture. Moreover, probably only 40% of the content is orginal. The rest is taken from other courses (i.e. Serverless Deep Learning with TensorFlow on GCP).

I usually like online learning, but this was a very weird course to say the least. It honestly felt very piecemeal, like some leftovers from a bunch of other courses had been thrown together without any second thought. I would have been better off just getting the Jupyter files and search for what was missing (that's what I did, anyway). Oh, but the Google infomercial at the end was totally necessary. Seriously, people...

Very few information. Poor practical homeworks. Poor quiz. It seems the course really starts on the second course. The first course doesn't worth the investment. It is more a show off of google products than a real course.

I generally have high respect for whoever teaches me something useful, but I have to tell my try opinion here. Maybe passing other online courses has risen my expectations, and that it the reason I give two stars to this course. So here are my critiques to this course: 1- The contents are at different difficulty levels in this course, for example, the explanation on ML are very elementary, while the programming assignments are for advanced programmers proficient in SQL, Python, BiqQuery, ...

2- The material is not coherent. It doesn't start with general and straightforward explanations and then gradually elaborate on the details—3- The method of lecturing. I had to close my eyes while listening to some of the lecturers because the way of lecturing is very unnatural, and it is a distraction. Sometimes watching the presenter helps in learning because you can connect to their mind, but sometimes their hand movements, gestures, the way they look at the camera, and all these things are simply distractions.

But I must also mention some advantages of this course. 1- You will learn about some keywords on the topic of Trading using ML. You can generally understand what's going on in this area and what are the tools being used. 2- You'll find some links to useful resources so you can self-study and go through the direction you desire.

Anyway, I am sure this course will gradually improve after feedback from learners.

I am a physicist and I find it very basic, It did not provide me with any new knowledge whatsoever even with the little amount of knowledge I had of the subjects.

having completed "AI for everyone" and 3 out of 5 for the "deep learning specialization", I found quite a few concepts such as the "short history of ML" quite basic, especially week 4's content which was very basic and not trading related. But overall it was good having a refresher from a different perspective.

On the positive side, as someone who has worked in Quant funds prior to the popularity of ML neural networks, I really enjoyed listening to Jack Farmer's in-depth walkthrough of the ML applications in quant strategies. I'm quite looking forward to the rest of the courses in this specialization and will give this 5 stars despite a few minor glitches which are expected for a new course.

The course gave me a basic understanding of few types of models including linear regression, time series and neural networks. A few more offline exercises that we can perform on our own would be a good addition to assert the acquired knowledge even more.

This course is a good initial taste of the topics. These topics could be complex but I believe it handles them adequately for an introductory course. I think the use of GCP shows that packaged solutions are a way to be efficient and move faster in industry.

Exactly what I was looking for and at the adequate level. I'm a trader and a machine learning developer, and this course helped me in both topics

Other courses recommended before doing this one! Basics of ML, Basics of the stock market, python and sql

Would be nice to have some extra step at the end of the lad to actually build a trading strategy instead of stopping at the fitting of the model

A collection of videos from other sources/courses, combined with some specifically for this course. A bit too basic for what I needed (not their fault).

Some minor complaints:

Audio quality/volume was a mixed bag, would be nice if they normalized audio across all videos.

Painful process to use the 'QwikLabs'. I mean, you couldn't make a clunkier user experience if you tried.

The course felt like an ad for Google products in parts. Instructions for how to use the Google products are now old so lots of menus have moved or changed names. So, not a very good ad because everything seemed more difficult to use than it should be.

This course makes everything easier. It is structured very user friendly for understanding the forest rather than the trees. I have taken courses in Python, Jupyter Notebook, ARIMA forecasting, Linear regression and other Finance and Probability courses to fully appreciate this course. If you dont have these basic knowledge and if they ask you to code every detail, then it would have been impossible. Good job Google and New York Institue of Finance.

Excellent course by Google and NY Institue of Finance. Course is well-structured and provides high quality content with good labs. I much enjoyed Jack Farmer putting Quant strategies in a nice and clear structure while explaining complex topics in an intuitive and simple way. Time series and ARIMA modeling with the related lab is also a very good part. I hope the latest Tensorflow 2.x version will be used in next course.

Very good course us introduction to Trading, ML models for trading, ML, Neural networks concept and approaches, Google cloud platform.

Very nice course to understand liner regression, ARIMA and ML. Also, the practice on GCP with notebook is exciting to me