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- I'm in my 30's and never had a "real job" - I have depression and anxiety, how do I get my life in order?
- Learn data science step by step in 6 months😉
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2022 Goals
Here is a guide and resources on these in the 6-12 weeks sections. It's from Chandoo on YT, who has a ton of videos on learning data analytics.
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Andrew Ng or more hands-on course?
I will give it a look into Ng's course, try out some of the resources in this github and look into this website too
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Weekly Entering & Transitioning Thread | 14 Feb 2021 - 21 Feb 2021
Data analysis roadmap
ml-coursera-python-assignments
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[D] Backpropagation is not just the chain-rule, then what is it?
check this out in particular. It's the week 4 homework from Ng's course, redone by someone to be in Python instead of Octave. It's got a built in grader, so you can grab the jupyter notebook, run it locally and it'll tell you when you've got the answer right. I'd recommend taking a crack at it, then when you figure out how to code it, take a look at that micrograd library and see how you could achieve something similar using an object oriented approach.
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How does Andrew Ng's courses compare to OMSCS ?
Python version of assignments which you can submit: https://github.com/dibgerge/ml-coursera-python-assignments
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Is the new Andrew Ng specialisation course worth it if I finished the original one with Python exercises?
Basically title. I'm halfway thru the original Stanford University Machine Learning course by Andrew Ng, but instead of using the Octave/Matlab exercises, I went with a Python repo. Now, I know the new specialisation course came out and is updated with newer content, more relevant to the state of the industry today. I have the following choices:
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Is the Andrew Ng course worth having to learn Octave?
A language is only worth learning if it is useful to know. But the only reason 99% of people would learn Octave is just to take that course lol. Besides, (a) the original course can be completed in Python using this repo, and now his new course is actually offered in Python.
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What do you think of Andrew Ng's new Machine Learning Specialization that launched last week on Coursera?
FWIW there is a repo you can use to complete the first one in Python. I used it and can vouch that it works perfectly as advertised.
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Andrew Ng updates his Machine Learning course
You can do them in python and submit them! https://github.com/dibgerge/ml-coursera-python-assignments
- Andrew Ng’s Machine Learning course is relaunching in Python in June 2022
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[NEWS] Not sure if this has been posted before, but ML course from Coursera is going to be updated in a new version in June (it will include python)
Andrew Ng ML-Coursera Assignments in Python
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New to ML
Last piece... Octave is super easy to get into. I don't personally think it's worth doing Python versions of the homework, but if you really can't stand screwing around with a new language, this repo has alternate versions of the homework to follow that will use Python instead. You can do either these or the original versions, so don't let the Octave scare you. You don't have to use it if you really don't want to, but... like I said, it's not a big deal either way, I just did it in Octave.
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Has anyone here done Andrew Ng's ML Course in Python and could help me out with the first assignment?
Specifically, I'm referring to this github repository: https://github.com/dibgerge/ml-coursera-python-assignments/blob/master/Exercise1/exercise1.ipynb. I'm currently doing Assignment 1.
What are some alternatives?
fastprogress - Simple and flexible progress bar for Jupyter Notebook and console
coursera-machine-learning-solutions-python - A repository with solutions to the assignments on Andrew Ng's machine learning MOOC on Coursera
alphalens - Performance analysis of predictive (alpha) stock factors
Removeddit - View deleted stuff from reddit
AeroPython - Classical Aerodynamics of potential flow using Python and Jupyter Notebooks
deeplearning-notes - Notes for Deep Learning Specialization Courses led by Andrew Ng.
pydna - Clone with Python! Data structures for double stranded DNA & simulation of homologous recombination, Gibson assembly, cut & paste cloning.
RStudio Server - RStudio is an integrated development environment (IDE) for R
BestPractices - Things that you should (and should not) do in your Materials Informatics research.
ml-coursera-python-assignments-master - Python Machine Learning Exercises
wordle-solver - For educational purposes, a simple script that assists in solving the word game Wordle.
Machine-Learning-Andrew-Ng - Coursera Machine Learning by Stanford University : Andrew Ng: Assignment Solutions