ISL-python
fecon235
ISL-python | fecon235 | |
---|---|---|
4 | 15 | |
181 | 1,088 | |
- | - | |
0.0 | 0.0 | |
over 1 year ago | over 1 year ago | |
Jupyter Notebook | Jupyter Notebook | |
- | GNU General Public License v3.0 or later |
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.
ISL-python
-
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
-
ESL vs ISLR books?
Here or here for the Python versions of ISLR.
-
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.
fecon235
What are some alternatives?
ISLR-python - An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code
eip1559_analysis - Can we estimate the economic impact of EIP-1559 on miners? This repository try to estimate the loss of miners' revenue coming from transactions fees, using Ethereum historical data.
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
priceR - Economics and Pricing in R
paip-lisp - Lisp code for the textbook "Paradigms of Artificial Intelligence Programming"
FinMind - Open Data, more than 50 financial data. 提供超過 50 個金融資料(台股為主),每天更新 https://finmind.github.io/
ydata-profiling - 1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
cvxportfolio - Portfolio optimization and back-testing.
ML-foundations - Machine Learning Foundations: Linear Algebra, Calculus, Statistics & Computer Science
Business-Valuation-with-Discounted-Cash-Flow-In-Python - Stocks and Assets Valuation via Discounted Cash Flow with Python
docassemble-HousingCodeChecklist - UpToCode - Form tool for tenants to get help with housing conditions issues.
ipl-2023-best-players - A rating system for IPL players who played in 2023, along with data scrapped from howstat