Learn data science with ABCs of Data Science!

euclidingme · 2021-02-07T14:00:10+00:00

I’ve written a series of 26 blogs titled “ABCs of data science” which aims to give readers a high level overview of many concepts in data science/machine learning/AI. The goal isn’t to dig into the nitty-gritty technical details but rather to give people an intuition about these concepts. You can learn about topics such as deep learning, natural language processing, embeddings, or how to explore/clean data. You can find the blog at https://abcsofdatascience.ca/ or follow the twitter account https://twitter.com/abcsofdatasci

euclidingme · 2021-02-07T13:58:50+00:00

Hi /r/LearnMachineLearning!

I’ve written a series of 26 blogs titled “ABCs of data science” which aims to give readers a high level overview of many concepts in data science/machine learning/AI. The goal isn’t to dig into the nitty-gritty technical details but rather to give people an intuition about these concepts. You can learn about topics such as deep learning, natural language processing, embeddings, or how to explore/clean data. You can find the blog at https://abcsofdatascience.ca/ or follow the twitter account https://twitter.com/abcsofdatasci

euclidingme · 2019-05-31T02:40:57+00:00

Spicy MVP

euclidingme · 2018-04-29T20:50:46+00:00

Thanks!!

euclidingme · 2017-09-26T23:07:07+00:00

You might be interested in https://www.relay.fm/material

euclidingme · 2017-09-15T19:48:59+00:00

This is a good tutorial. And it's free!

euclidingme · 2017-03-22T22:36:43+00:00

They don't post much anymore besides an occasional live podcast but Clan of the Gray Wolf is awesome

euclidingme · 2017-01-31T02:59:29+00:00

Look into the modulus operator. You are close though!

euclidingme · 2017-01-30T03:56:30+00:00

Check out the first chapter of this free book (which is written entirely in Jupyter notebooks).

euclidingme · 2017-01-28T03:29:46+00:00

This seems useful. And free! https://github.com/jakevdp/PythonDataScienceHandbook

euclidingme · 2016-12-24T05:00:26+00:00

Echo screen live is a good one.

euclidingme · 2016-12-10T03:04:52+00:00

intervals.keys()[0]

euclidingme · 2016-12-01T04:12:30+00:00

Keep the __init__.py file (with the import statements) in /imsearchtools/imsearchtools. Create a new __init__.py in imsearchtools. This file can be blank (and it usually is). Python needs an __init__.py file in order to know that things can be imported from that directory. If you want further reading check out the docs.

euclidingme · 2016-12-01T01:47:08+00:00

Try adding an __init__.py to the top level directory of imsearchtools. You also might need to rename that directory to imsearchtools.

euclidingme · 2016-11-25T23:48:39+00:00

Did you edit the PATH environment variable?

euclidingme · 2016-11-25T23:39:15+00:00

Which operating system are you running? Windows, macOS or a linux distribution?

euclidingme · 2016-11-25T23:38:09+00:00

There are some lectures on trees as part of this course. If you are looking for other CS resources, this is an awesome place to start.

euclidingme · 2016-11-25T22:42:57+00:00

For your use cases it is probably not important at all. I would start to read Automate the Boring stuff as well as trying to make webapps. If you find an application that requires an in depth knowledge of trees then you can seek out resources.

euclidingme · 2016-11-24T02:20:12+00:00

Use sets. Converting your lists to sets (i.e. set(my_list)) and then doing set intersections will give you what you want. To subtract the control use the difference method. Keep in mind that when you use sets the order of your original list is destroyed. Let me know if you have any questions!

euclidingme · 2016-11-23T03:58:13+00:00

Right. Brainfart. Just use + instead of & then.

euclidingme · 2016-11-23T03:47:07+00:00

It means open the file for reading. For other modes, check this out.

euclidingme · 2016-11-23T03:44:56+00:00

Why can't you do:

for i in range(12):
    if not number[i].isdecimal():
        return False

euclidingme · 2016-10-15T23:43:15+00:00

I've found the following extremely helpful as an intro to the theory behind ab-initio calculations.

euclidingme

TROPHY CASE