discussion on how we can use statistical methods to measure and improve the efficacy of http://freeCodeCamp.com
@mmalinda welcome!
Bioinformatics related, here are some I've found useful:
- Ben goes through algorithms and data structures commonly used in bioinformatics, and programs live in Python, so great way to learn both https://www.youtube.com/playlist?list=PL2mpR0RYFQsBiCWVJSvVAO3OJ2t7DzoHA
- If you need some programming practice, http://rosalind.info/problems/locations/ is a great resource to think about the kinds of algorithms used in bioinformatics.
- If you want bioinformatics/genomics papers, https://github.com/jtleek/genomicspapers and https://ocw.mit.edu/courses/biology/7-91j-foundations-of-computational-and-systems-biology-spring-2014/readings/ are great lists to read from
If you have specific questions for bioinformatic data science, feel free to ask around here :smile:
Hi, long time no see. But good to hear you are all still around. I’m currently enrolled in a tech startup program. I have met great entrepreneurs who mentor us. I also met a few data scientists thus so far good news. I also want to thank you because without your support I would never have jumped to the opportunity. I hope to catch up a little while loaded with assignments. I have to incorporate this year thus a lot of work to do. Have a great weekend.
Here's the code and other plots I made for those interested https://github.com/erictleung/disneyplus-twitter-analysis
Hello, could anyone tell me guidelines for this group, I did a search on machine learning and this group showed up, not sure where to read about suitable topics for discussion and rules about this group. And if anyone knows a similar active group to talk about machine learning problems please forward them. Thanks.
@frankieliu there's not much to the guidelines here, just be friendly, don't veer too far off topic, and no self-promotion. You can have a quick read through the Code of Conduct if you'd like :)
@frankieliu welcome! Feel free to use this room as like a sounding board for ideas or general help. We have a wide range of interests and expertise. I don't think it would be too out of scope to share your journey of machine learning here as well. Are you studying anything in particular in machine learning right now?
...there's not much to the guidelines here, just be friendly, don't veer too far off topic, and no self-promotion. You can have a quick read through the Code of Conduct if you'd like :)
"...no self-promotion." @becausealice2 Lol whoops. I hope my earlier link wasn't too self-promotion-y! (Aren't I a moderator too? I should know the bounds too haha.) Just sharing an analysis I thought others might be interested in seeing :smile:
I've been trying to review some probability and statistics and came across this https://stanford.edu/~shervine/teaching/cme-106/cheatsheet-statistics It is a little math-y, but I like cheatsheets because they give some roadmap for things to further investigate.
Sharing analyses done without expectation of compensation just doesn't feel like self-promotion to me, especially in a Gitter channel focused on data science. It would be a very stale channel if we couldn't share pet analyses here. Maybe I'm misunderstanding the rules but I've been interpreting "self-promotion" as being anyone coming in specifically to direct attention to their product they've come to peddle.
For those wanting to dig deeper into the "black boxes" of machine learning, here's a book that might be a good resource https://christophm.github.io/interpretable-ml-book/.
Can you use a decision tree to identify images?
# Create en empty Dict
file_dict = {}
txt_files = [i for i in os.listdir(direct + '\\' + my_dir_path) if os.path.splitext(i)[1] == ext]
# Iterate over the txt files selected.
for f in txt_files:
# print(f)
# Open them an assign to the empty Dict.
with open(os.path.join(direct + '\\' + my_dir_path, f)) as file_object:
file_dict['mic' + f.strip('.txt')] = file_object.read()
i did this creating a dictionary in where the filename is the key and the content the value. But then how can i convert this into individual dFs... is there a better way?
Hi guys, you can check out my repo of mostly python notes here :- https://github.com/l0k3ndr/programming-notes
Also, feel free to ping hi, if you are working on something and just need a sound board for discussion.. especially datascience, ML, algorithm, DS and anything pythonic :D
@Adser89 I don't know if you've found an answer to your question, and I am not able to help, but you can try asking in the
Programming Help category of FreeCodeCamp's forum
Hello , I have been working with Spark for my past two years . I am fascinated by its ease of use and the performance . I want to learn about its internals . I found some online resources but I do not understand in which I should proceed to dismantle the code . Whenever I want to learn from others code , I go to main function and then move inside . In this case , it is confusing . Any suggestions
If you guys are still planning on having a resource discussion group, might I suggest either the Data Science or Machine Learning subforums? The FCC forum already has a bit of momentum and it would allow a more broad participation. Besides, those subforums could use a little push into activity :wink:
I have two doctor appointments that day
On that Tuesday* I don't know that I'll be up for going out on Wednesday, either