Introduction to Trading, Machine Learning & GCP
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Rating | 3.4★ based on 69 ratings |
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Length | 5 weeks |
Effort | 4 weeks of study, 5 hours per week |
Starts | Jul 3 (39 weeks ago) |
Cost | $79 |
From | Google Cloud, New York Institute of Finance via Coursera |
Instructors | Jack Farmer, Ram Seshadri |
Download Videos | On all desktop and mobile devices |
Language | English |
Subjects | Data Science Business Programming |
Tags | Data Science Business Machine Learning Finance |
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What people are saying
other courses
Appears to cobbled together from other courses.
There is a lot of repetition between modules and they really look like they have just been pulled out of other courses.
Rather easy Other courses recommended before doing this one!
Good Some good parts, but several sections were cobbled together from other courses I've taken Rather good content but I believe it is not always presented in the right order.
It honestly felt very piecemeal, like some leftovers from a bunch of other courses had been thrown together without any second thought.
The rest is taken from other courses (i.e.
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ml for trading
The course gives you a very limited introduction to ML for trading.
I thought this course would give me a proper introduction of ML for trading but instead is more focused on the google ecosystem.
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google cloud
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.
I does not goes very deep, but hat some useful exercises and practice with google cloud.
But mostly, it’s about google cloud.
I would not recommend this course unless you want to learn more about Google Cloud Platform, BigQuery, TensorFlow or any other google product.
some explanation in trading was hard, it was realy good to be able to test google cloud services, but needs more practical examples Very helpful.
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machine learning
A good intro to machine learning in finance.
Great introduction for people who want to get started in ML, GCP and Fin Tech Good course that gives a lot of breadth as an introduction to machine learning in finance.
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.
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together from
using gcp
The time limitations on using GCP account were slightly pushing to complete it fast without having time for thorough thinking and experimenting.
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.
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.
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seems like
Material sometimes seems like a patchwork in random order.
It seems like the units have been reassembled from other courses.
very basic
It can jump from very basic concepts to suddenly covering complex topics in the in a 2 minute video.
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.
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feels a bit
Granted I can research that on my own, but more guidance would have been appreciated.More broadly, this course feels a bit chaotic, jumping from one topic to the other, and then getting back at a previous one.
Particularly, the introduction to Jupyter notebooks coming at the end of the course, after three labs, feels a bit frustrating.
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Rating | 3.4★ based on 69 ratings |
---|---|
Length | 5 weeks |
Effort | 4 weeks of study, 5 hours per week |
Starts | Jul 3 (39 weeks ago) |
Cost | $79 |
From | Google Cloud, New York Institute of Finance via Coursera |
Instructors | Jack Farmer, Ram Seshadri |
Download Videos | On all desktop and mobile devices |
Language | English |
Subjects | Data Science Business Programming |
Tags | Data Science Business Machine Learning Finance |
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