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Learner Reviews & Feedback for Introduction to Trading, Machine Learning & GCP by Google Cloud

4.0
stars
819 ratings

About the Course

In this course, you’ll learn about the fundamentals of trading, including the concept of trend, returns, stop-loss, and volatility. You will learn how to identify the profit source and structure of basic quantitative trading strategies. This course will help you gauge how well the model generalizes its learning, explain the differences between regression and forecasting, and identify the steps needed to create development and implementation backtesters. By the end of the course, you will be able to use Google Cloud Platform to build basic machine learning models in Jupyter Notebooks. To be successful in this course, you should have advanced competency in Python programming and familiarity with pertinent libraries for machine learning, such as Scikit-Learn, StatsModels, and Pandas. Experience with SQL is recommended. You should have a background in statistics (expected values and standard deviation, Gaussian distributions, higher moments, probability, linear regressions) and foundational knowledge of financial markets (equities, bonds, derivatives, market structure, hedging)....

Top reviews

AK

May 28, 2020

Very interesting course, I totally agree that there are very few courses that cover time-series analysis. I haven't tried BigQuery before. Looking forward to next courses in this specialization.

MS

Jan 29, 2020

Excellent! But, I am missing some of the prerequisites since I just wanted to take a chance and try things out, but feel like proceeding further might lead to some stumbling blocks.

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126 - 150 of 221 Reviews for Introduction to Trading, Machine Learning & GCP

By Michael M

Apr 19, 2020

Pretty great course. Sometimes there was too much detail and other times not enough but overall I loved it.

By Brian B

Apr 8, 2021

Was pretty good. Would be nice to have some links to resources on BQML specific query language.

By Soren B

Apr 21, 2020

Good intro. Could use some additional work on the ARIMA model lab on tuning the parameters.

By Anirban S

Jan 19, 2020

Introduces concepts in a lucid way albeit depending on some prerequisite knowledge at times.

By Alejandro A S

Jul 14, 2020

the course is not very organized, the material presented are not clever in order

By Andrew S

Jun 23, 2020

Shortage of practice but good for learning something new about stock markets.

By Iskander R

Jun 6, 2020

Good as introductory course. Looking forward for more in depth topics. Thanks

By Alvar S I

Sep 4, 2020

Muy bueno por conjugar muy bien el mercado de capitales con la programación

By Sergio G

Apr 26, 2020

Easy to follow. It lacks of a more applied number of examples and cases.

By Ocin L

Apr 10, 2020

More explanation on the lab and the function being use would be great!

By Ronnie Y

May 9, 2020

Course content should include more practical in each section

By RENATO V M S

Jul 2, 2021

Nice course but showing only the peak of the GCP iceberg!!!

By Kong

Jan 1, 2021

Overall good experience. But first lab is confusing.

By domenico r

Apr 13, 2020

I was expecting more coding on python

By Robin L

Dec 21, 2020

please add more hands-on lab

By Wolfgang B

May 4, 2020

Yes. Introduction level.

By David C C R

Apr 19, 2020

Introductory course.

By Henry M

Jan 19, 2020

Good introduction

By Rayantha S

May 7, 2020

Very good course

By Sergio O

Mar 27, 2020

Good!

By Charles C

Nov 4, 2021

k

By Paolo D

Jul 17, 2021

I found this course to be very approximate as if it just wanted to give a high-level idea of the concepts it covered. But maybe, that is the actual goal of the course: give an idea of how ML concepts can be applied to the finance domain and then let the student deepen and practice with the techniques shown. The parts that I've found to give more interesting, even though they have not been covered in detail, are the quant strategies and the time series one. The ML part, coming from an ML background, is well explained but they have been formulated only to give a high-level idea without going into the mathematical details(which I think it's outside of the scope of this course). Regarding the lab part, I didn't enjoy the BiqQuery part while I've loved the lab with Jupiter notebooks (I'm a little biased here). I would have liked more math details, but again that is just a personal preference.

By Alexey L

Jan 19, 2020

First 3 weeks were quite good, although I found lack of lab practice. The time limitations on using GCP account were slightly pushing to complete it fast without having time for thorough thinking and experimenting. Although they could be restarted - the work had to be recreated again when this happened. Last week was very shallow and non-consequent and looked like it should be the first week as there were explanations of ML and GCP AI Notebooks. Which had been used during already during the first 3 weeks. Although I'm impressed with GCP platform and its AI capabilities, I felt like it had been highly advertised and selling though the course, where my personal preference would be learning more of algorithms and experimenting and using GCP just as one a tool.

By Biagio B

May 29, 2020

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.

By Loo T T

Mar 1, 2020

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.