ISLR-python
ISLR.jl
| ISLR-python | ISLR.jl | |
|---|---|---|
| 10 | 2 | |
| 4,209 | 139 | |
| - | - | |
| 0.0 | 2.6 | |
| over 1 year ago | over 3 years ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | - |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
ISLR-python
- How is this for a Data Analysis roadmap?
-
DATA SCIENTIST ROADMAP
3) Then, work through the ISLR book: Book: https://www.statlearning.com/ Video: https://www.youtube.com/playlist?list=PLoROMvodv4rOzrYsAxzQyHb8n_RWNuS1e Jupyter Notebooks (Python version): https://github.com/JWarmenhoven/ISLR-python
-
[E] Please recommend statistics books for a beginner for data science.
Also, if you know you’re going to be using Python, you can find repos out there with the code converted from R to Python on Github. Ex: https://github.com/JWarmenhoven/ISLR-python
-
Have you found it impeding that the exercises in Intro to Statistical learning are in R?
Lots of people redid the labs in Python if you really want Python instead, a quick google search gave me https://github.com/alexandrasouly/ISLR-but-python and https://github.com/JWarmenhoven/ISLR-python
-
ESL vs ISLR books?
Here or here for the Python versions of ISLR.
-
Low Level Books or Courses
I don't think many people know this, but there's actually a very helpful Python Github repo to accompany the ISLR book (which uses R). Hope this helps :D
-
Looking for resources to get better at statistics
People have also recreated the examples in Python if that is your preferred language.
-
I want to learn machine learning and I want to make small projects in the future. Where do I start? Any tips and advice?
ISLR Code in Python
- Help with An Introduction to Statistical Learning: Figure 1.3
-
Maths for Deep Learning (mainly computer vision)
I am facing the same problem! I worked through the fastai tutorials for land classification purposes and felt there was a real gap between what I was doing in Python and what I actually understood. I found the fastai tutorials were a great start when followed by a math refresher like the introduction to statistical learning (ISLR) from Stanford. I know Stanford was changing its online class format on the Lagunita platform, but hopefully its still free and online. The profs wrote it in R, but here is a python wprkthrough. (https://github.com/JWarmenhoven/ISLR-python).
ISLR.jl
-
An Introduction to Statistical Learning with Applications in Python
Honestly, I dont think they will have Julia version soon. I converted all the code in this book to Julia sometimes ago:
https://github.com/tndoan/ISLR.jl
-
Tutorial on beginner inferential statistics?
Julia version of ISL https://github.com/tndoan/ISLR.jl
What are some alternatives?
An-Introduction-to-Statistical-Learning - This repository contains the exercises and its solution contained in the book "An Introduction to Statistical Learning" in python.
StatsWithJuliaBook
ISL-python - Porting the R code in ISL to python. Labs and exercises
Julia-DataFrames-Tutorial - A tutorial on Julia DataFrames package
ISLR-but-python - This repository contains labs rewritten in Python for the book An Introduction to Statistical Learning with Applications in R by James, Witten, Hastie, Tibshirani (2013)
ISLR - Introduction to Statistical Learning
PythonDataScienceHandbook - Python Data Science Handbook: full text in Jupyter Notebooks
islr-for-gis - A repository for the content that is used and generated while completing independent study on using statistical learning methods in GIS.
AI-Hacktoberfest - Welcome to the Hacktoberfest Challenge for Artificial Intelligence / Machine Learning ! Today we will be assessing your skills to Predict Forest Fire Areas given its various parameters!
HypothesisTests.jl - Hypothesis tests for Julia
the-elements-of-statistical-learning - My notes and codes (jupyter notebooks) for the "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani and Jerome Friedman
pydata-book - Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media