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Hello,

We’re building a tool for data scientists and developers to automatically select, train and deploy machine learning models, as a service.

JustML finds the right scikit-learn or xgboost estimator for a given supervised machine learning problem, along with the optimal hyperparameters.

As we’re still building the tool, we’d really appreciate your feedback or suggestions. We’re here to troubleshoot any issues you might encounter. You can leave a comment or PM if you need to unlock a few extra credits.

1 point by juverstraeten 1 day ago | link | parent | on: The Rise of Board Games

I counted the number of board games published every year and mapped it on a plot. It greatly demonstrates the current growth trend of the board game industry. #Kaggle #KernelsAward #boardgames #data #boardgamegeek

Thanks! Seems great.

Pseudo code is used to solve the Fibonaci problem and other problems in this post, so readers using any programming languages can read it. Comments and other solutions using any other programming languages are welcome!

https://www.youtube.com/watch?v=ZyjCqQEUa8o

"The Unexpected Effectiveness of Python in Science" Jake Vanderplas PyCon 2017


this is straight up an advertisement. come on..

Cardano (ADA) weekly update for investors.

Folks, is there a way to moderate such posts?

I know a better place to share with other friends, LinkedIn :)

I just like this article and so i thought i have to share with other friends

what is this article doing here?

Thought people might be interested; looking for any useful feedback...

do they use this: https://github.com/auchenberg/volkswagen

What are the critical steps to get a job in data science? We share the proven formula that helped many data enthusiasts secure job offers as data scientist/analyst, data engineer and machine learning engineer.

Build a shinydashboard that features an adapted JAWS statistic for evaluating nominees and members of a MLB franchise's Hall of Fame

First post in a while comparing two of the largest ticket resellers. Looking to dig even deeper into the data in the coming months.

can you provide free coupon for this course

This is a great topic. With a similar idea, I have been focusing on better model evaluation techniques. Here is an article about it https://www.oreilly.com/ideas/interpreting-predictive-models...

You are absolutely right. Our main idea was to show how RNNs could be used for time series forecasting. We have chosen public bitcoin price data because this problem is well-known for general public. There is no doubt, that such difficult problem cound be solved using only previous prices. But this article may be a good starting point to understand potential methods to approach such problems.

As can be seen from PAC, lookback should be 1, larger values give worst result, but perhaps the model remains more stable to overfittting. There is no constant term.

While none of these books are included, Humble Bundle is offering a large set of books until March 26th.

https://www.humblebundle.com/books/artificial-intelligence-b...


https://www.wired.de

There is an updated version of this article with completely different books except one (Naked Statistics).

https://hackernoon.com/aspiring-data-scientists-start-to-lea...


To be honest, I don’t think any of these books are really very useful. I’ve been doing data science professionally for over two years, and if I had to go back in time and advice myself to read some of the books on data science, it wouldn’t be any of these. I would recommend ESL or ISL (books and youtube lectures), Max Kuhn’s book, doing some live tutorials of scikit-learn, data.table and pandas “5-minute” vignettes, and just going straight into Kaggle.

Data Science at the Command Line comes close, though. I’ve looked at that book early enough, and it has some nice CL tools that were very helpful.


Happy to discuss if there are any questions/suggestions.

I think ML is hard for anything, were incorrect/unexplaineable results can cost lots of money, or even physical damage.

Which is why 'ML in <DomainX> so hard', where the DomainX are: Medicine, day trading, autonomous vehicles, military and counter-terrorism operations, cyber-security activity attribution

Vs domains such as Consumer sentiment detection, some image recognition, satisfaction analysis, etc. Because those domains do not penalize heavily for over-sold/under delivered promises.

1 point by vimarshk 34 days ago | link | parent | on: Google AI Interview Questions

Happy to take feedback if there is any.

Happy to answer questions if there are any.

Predicting markets with machine learning is always dangerous, because unlike most types of statistics, markets do not evidently have a single representation that is slowly uncovered; "gravity can flip upside down overnight".
1 point by goodone 37 days ago | link | parent | on: Telegram Data Science news feed

The channel has regular Data Science updates from a lot of different sources (at least twice per day) Also, anyone interested can join a Data Science discussion group in Telegram for English speakers: https://t.me/velintelligencegroup
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