the-elements-of-statistical-learning VS ISL-python

Compare the-elements-of-statistical-learning vs ISL-python and see what are their differences.


My notes and codes (jupyter notebooks) for the "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani and Jerome Friedman (by maitbayev)


Porting the R code in ISL to python. Labs and exercises (by emredjan)
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the-elements-of-statistical-learning ISL-python
1 4
382 178
- -
1.9 0.0
over 1 year ago about 1 year ago
Jupyter Notebook Jupyter Notebook
MIT License -
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Posts with mentions or reviews of the-elements-of-statistical-learning. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2020-12-31.


Posts with mentions or reviews of ISL-python. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-10-12.
  • ESL vs ISLR books?
    2 projects | /r/datascience | 12 Oct 2021
    Here or here for the Python versions of ISLR.
  • The Hundred-Page Machine Learning Book
    2 projects | | 25 Jan 2021
    I typically recommend a few different books to everyone who finishes the bootcamp, based on a self-assessment they take. I recommend some books based on their strengths, so they can find a career path sooner, and some books based on their weaknesses, so they can widen their cone of oppportunity within ML.

    In our consultancy, data science is done in Python and SQL (and PySpark, but I don't hand out books on that during bootcamp!), and ML delivery is a combination of math, software engineering, and architecture/product owner disciplines.

    If you're strong in software engineering, I recommend Machine Learning Mastery with Python by Jason Brownlee as it's very hands-on in Python and helps you run code to "see" how ML works.

    If you're weak in software engineering and Python, I recommend A Whirlwind Tour Of Python by Jake VanderPlas, and its companion book Python Data Science Handbook.

    If you're strong in architecting / product management, I recommend Building Machine Learning Powered Applications by Emmanuel Ameisen since it explains it more from an SDLC perspective, including things like scoping, design, development, testing, general software engineering best practices, collaboration, etc.

    If you're weak in architecting / product management, I typically recommend User Story Mapping by Jeff Patton and Making Things Happen by Scott Berkun, which are both excellent how-tos with great examples to build on.

    If you're strong in math, I recommend Understanding Machine Learning from Theory to Algorithm by Shalev-Shwartz and Ben-David, as it has all the mathematics for ML and actually has some pseudocode for implementation which helps bridge the gap into actual software development (the book's title is very accurate!)

    For someone who is weak in the math of ML, I recommend Introduction to Statistical Learning by Hastie et al (along with the Python port of the code ) which I think does just enough hand holding to move someone from "did high school math 20 years ago" to "I understand what these hyperparameters are optimizing for."

    Anyway, I've spent a lot of time reading and reviewing books about ML, and my key takeaway is ones that get you closer to writing actual code to solving actual problems for actual people are the ones to focus on.

What are some alternatives?

When comparing the-elements-of-statistical-learning and ISL-python you can also consider the following projects:

ISLR-python - An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code

ISLR - Introduction to Statistical Learning

homemade-machine-learning - 🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained

Sharing_ISL_python - An Introduction to Statistical Learning with Applications in PYTHON

paip-lisp - Lisp code for the textbook "Paradigms of Artificial Intelligence Programming"

ydata-profiling - 1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.