https://github.com/melling/islr
Introduction to Statistical Learning
https://github.com/melling/islr
Last synced: about 1 year ago
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Introduction to Statistical Learning
- Host: GitHub
- URL: https://github.com/melling/islr
- Owner: melling
- Created: 2020-12-01T18:16:58.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2021-03-18T20:45:25.000Z (over 5 years ago)
- Last Synced: 2025-05-07T21:45:47.066Z (about 1 year ago)
- Language: R
- Size: 4.64 MB
- Stars: 61
- Watchers: 3
- Forks: 10
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# ISLR
Introduction to Statistical Learning: http://www.StatLearning.com
The PDF book is available for free on the author's site above.
Working through the book and the labs.
[Videos](https://www.r-bloggers.com/2014/09/in-depth-introduction-to-machine-learning-in-15-hours-of-expert-videos/)
Our solutions:
- [Chapter 2 Solutions](chapter02)
- [Chapter 3 Solutions](chapter03)
- [Chapter 4 Solutions](chapter04)
- [Chapter 5 Solutions](chapter05)
- [Chapter 6 Solutions](chapter06)
- [Chapter 7 Solutions](chapter07)
- [Chapter 8 Solutions](chapter08)
- [Chapter 9 Solutions](chapter09)
- [Chapter 10 Solutions](chapter10)
## Course Slides for Videos
- [Chapter 1: Introduction ](https://web.stanford.edu/~hastie/MOOC-Slides/introduction.pdf)
- [Chapter 2: Statistical Learning ](https://web.stanford.edu/~hastie/MOOC-Slides/statistical_learning.pdf)
- [Chapter 3: Linear Regression ](https://web.stanford.edu/~hastie/MOOC-Slides/linear_regression.pdf)
- [Chapter 4: Classification ](https://web.stanford.edu/~hastie/MOOC-Slides/classification.pdf)
- [Chapter 5: Resampling Methods ](https://web.stanford.edu/~hastie/MOOC-Slides/cv_boot.pdf)
- [Chapter 6: Linear Model Selection and Regularization ](https://web.stanford.edu/~hastie/MOOC-Slides/model_selection.pdf)
- [Chapter 7: Moving Beyond Linearity ](https://web.stanford.edu/~hastie/MOOC-Slides/nonlinear.pdf)
- [Chapter 8: Tree-Based Methods ](https://web.stanford.edu/~hastie/MOOC-Slides/trees.pdf)
- [Chapter 9: Support Vector Machines ](https://web.stanford.edu/~hastie/MOOC-Slides/svm.pdf)
- [Chapter 10: Unsupervised Learning](https://web.stanford.edu/~hastie/MOOC-Slides/unsupervised.pdf)
## Other Solutions
- http://blog.princehonest.com/stat-learning/
- https://github.com/asadoughi/stat-learning/
- https://github.com/ppaquay/IntroStatLearning
- https://github.com/jilmun/ISLR
- https://github.com/KamranMK/ISLR-Solutions
- https://laptrinhx.com/solutions-to-labs-and-excercises-from-an-introduction-to-statistical-learning-as-jupyter-notebooks-1723363054/
- https://altaf-ali.github.io/ISLR/index.html
- https://github.com/evertonjlima/Notebooks
- https://waxworksmath.com/Authors/G_M/James/james.html
## Python Solutions
- https://github.com/tdpetrou/Machine-Learning-Books-With-Python/tree/master/Introduction%20to%20Statistical%20Learning
- https://www.franzoni.eu/machine-learning-a-sound-primer/
- https://github.com/JWarmenhoven/ISLR-python
## Julia Solutions
- https://github.com/tndoan/ISLR.jl
# Machine Learning Articles
- [The Matrix Calculus You Need For Deep Learning](https://explained.ai/matrix-calculus/index.html)
- [Deriving convolution from first principles](https://towardsdatascience.com/deriving-convolution-from-first-principles-4ff124888028)
# Other Math Introductory Books
- http://www.cs.cmu.edu/~zkolter/course/linalg/linalg_notes.pdf
- http://joshua.smcvt.edu/linearalgebra/
- http://vmls-book.stanford.edu/
- https://ses.library.usyd.edu.au/handle/2123/21370
- https://projects.iq.harvard.edu/stat110/home