How to Implement Logistic Regression in Python From Scratch
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- Опубликовано: 31 окт 2021
- The Notebook: colab.research.google.com/dri...
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Was waiting for this one, thanks!!
Well, you no longer have to! 😃
Awesome!!!
Thanks!
I got this at the right time.
Great!
plzz continue this series
Okay noted!
Hey mate, I am actually cursed and keep missing your lives do you mind if I ask you here?
Or would you like me to wait for the next live?
Haha no problem; feel free to ask a question here.
@@GregHogg hey mate, thanks for this opportunity!
First of all, I hope that you're doing great!
secondly,
This question might take some time, and I think that there are other people like me.
background: non-math/stat/anything that is worth it related 'biology'
programming skills: zero (I just started learning python)
Goal: basically make money by working online.
My question is, is it worth it learning data analytics/science if my only purpose is to make money as a freelance or remote worker?
What will separate me from millions of other data scientists/analysts (by the time I finish learning, I would have a "meh" portfolio) and I am sure that there would be at least 100.000 other people in India that are better than me (I learn from them lol) so how can I compete?
I know that nationality plays a big role in landing a remote job (seen it with my own eyes xd) and mine kind of sucks 'even traveling abroad is out of the question since I have a weak passport.'
Thanks for reading this!
For all viewers I suggest R in data-analytics! Not Python!!
27:00 in accuracy score, first arg is y_actual, 2nd arg is y_pred.
You passed the other way round. LMAO
The order of these two doesn't matter :)
@@GregHogg doesn't matter in this example cuz phyton can understand it. But it really matters in reality. The functions arguments should be called in the right order.
plzz use dark mode if possible
Haha okay
The video is very good. Although he didnt have the mathematical knowledge to explain what the coeficient and the intercept means in a logistic regression. As a senior data scientist i give this free for youtube community. the coeficient tell us how much the probability of having heart disease increase or decrease when we go up in x by 1.0 value (max HR in this example ). In this case we can see that for every 1.0 increase in MaxHR the probability of having Heart Disease decrease about 3%. The intercept tell us the probability of having heart disease when the x=0 (maxHR=0), ofc in this case dont make any sense, because if u have 0 heart rate you would be dead. Although the model says you have 500% probability of having heart disease. Maths its the base to understand ML phyton and modules are tools. You can never make a proper analize of an ML output if you dont understand maths.
No.
@@GregHogg yes
@@tiagosilva856 there is a 600% probability you're incorrect
@@GregHogg bro i don't remember the values. You get the picture here.
@@tiagosilva856 Yep, I think I have a pretty good picture of what's going on here