ISLR-python
ISL-python
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Jupyter Notebook | Jupyter Notebook | |
MIT License | - |
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ISLR-python
- How is this for a Data Analysis roadmap?
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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
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[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
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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
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ESL vs ISLR books?
Here or here for the Python versions of ISLR.
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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
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Looking for resources to get better at statistics
People have also recreated the examples in Python if that is your preferred language.
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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
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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).
ISL-python
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Andrew Ng's Machine Learning Specialization or Introduction to Statistical Learning? For someone who's comfortable with mathematics.
https://github.com/emredjan/ISL-python this GitHub has the exercises in python but I am so pumped the python version is coming out this summer.
- Hey I wanna learn Statistics with python can anyone suggest me a good book and a good YouTube tutorial because i am really poor at it I don't know the basic concepts about it
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ESL vs ISLR books?
Here or here for the Python versions of ISLR.
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The Hundred-Page Machine Learning Book
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 https://github.com/emredjan/ISL-python ) 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?
An-Introduction-to-Statistical-Learning - This repository contains the exercises and its solution contained in the book "An Introduction to Statistical Learning" in python.
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
ISLR.jl - JuliaLang version of "An Introduction to Statistical Learning: With Applications in R"
paip-lisp - Lisp code for the textbook "Paradigms of Artificial Intelligence Programming"
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)
fecon235 - Notebooks for financial economics. Keywords: Jupyter notebook pandas Federal Reserve FRED Ferbus GDP CPI PCE inflation unemployment wage income debt Case-Shiller housing asset portfolio equities SPX bonds TIPS rates currency FX euro EUR USD JPY yen XAU gold Brent WTI oil Holt-Winters time-series forecasting statistics econometrics
PythonDataScienceHandbook - Python Data Science Handbook: full text in Jupyter Notebooks
ydata-profiling - 1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
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!
ML-foundations - Machine Learning Foundations: Linear Algebra, Calculus, Statistics & Computer Science
pydata-book - Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media