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Вернуться к Introduction to Trading, Machine Learning & GCP

Отзывы учащихся о курсе Introduction to Trading, Machine Learning & GCP от партнера Google Cloud

Оценки: 726

О курсе

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)....

Лучшие рецензии


29 янв. 2020 г.

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.


20 нояб. 2020 г.

I thought this was excellent. Some familiarity with standard SQL is needed to get the most benefit from the materials, and the course is clearly aimed at GCP users.

Фильтр по:

76–100 из 195 отзывов о курсе Introduction to Trading, Machine Learning & GCP

автор: hamed a

1 апр. 2021 г.

Thank you so much

it was really great for me

Best wishes

автор: Roberto R

9 янв. 2020 г.

Great start material for ML and Cloud Computing

автор: MOUAFEK A

26 янв. 2021 г.

Amazing Course and content. Thank you =)

автор: praburam u

2 сент. 2020 г.

good course but not much of programming

автор: Michael B J

17 мая 2020 г.

The Instructors were spot on!!! Thanks!

автор: nneoyi a

20 июля 2020 г.

A key course for the next generation.

автор: Steve H C F

16 янв. 2020 г.

Good intro to ML and using GCP.

автор: Muhammad M

20 дек. 2020 г.

very informative nad helpful

автор: Branderson J A C

13 сент. 2021 г.

Excelente , muy interactivo

автор: Seshadri S K

13 янв. 2020 г.

Great course with Basics

автор: Donkoo J

24 мая 2020 г.

Very good to learn GCP

автор: Juan R M O

9 янв. 2020 г.

A lot of possibilities

автор: Ronny F

20 янв. 2021 г.

This is very useful.

автор: Robin M

22 нояб. 2021 г.

Very enlightening!

автор: Azip S

24 дек. 2020 г.

Excellent training

автор: Leonardo A

22 дек. 2020 г.

Really good course

автор: J G

2 мар. 2020 г.

good course

автор: Gregory G J

23 янв. 2021 г.

Thumbs Up!

автор: LiengPhu T

26 июня 2021 г.


автор: Marcin G

6 февр. 2021 г.


автор: Andrei L

16 апр. 2020 г.

I can only agree with previouss comments:

1) Overall Ok as an introductory course

2) Week 4 should be week 1

3) Week 4 videos are somewhat disjoint and by the references they make are clearly just fragments of other courses.

4) Concepts used in labs should be explained a bit better beforehand

5) Each lab should have a at lease one concrete try-this followed by an explanation of why the result is different, better or worse

автор: Jair R

2 июня 2020 г.

The course provides valuable content. It requires more than Python fundamentals in some topics, but it is ok because the student must investigate out of the course's material.

There is an emphasis on Google's tools, which are very interesting, but the course should be more agnostic on this matter in order that the student has wider spectrum of resources available.

автор: Jakub K

28 авг. 2020 г.

I learned a few cool things. The main problem with this specialization is that the Machine Learning Stuff and Finance stuff are really separated (Google, NY univ). What I was looking for is the place where two concepts meets. Also i felt like ML stuff went too deeply too fast. Still... Cool Introduction.

автор: Angelo M

25 июля 2021 г.

The first module has a lot of specific knowledge of the financial market that it would not be easy to gather by conducting an extensive internet search. I realized that the professors have an excellent experience in the financial market, which greatly improves the final quality of the course.

автор: Carlos V

19 янв. 2020 г.

Good course on the applications of ML to stock trading, examples are quite nice and the labs provide explanations on how to utilize the ML libraries available, recommended for anyone interested on more time-series type of analysis and ML