Harvard-IACS / 2021-CS109BLinks
☆47Updated 3 years ago
Alternatives and similar repositories for 2021-CS109B
Users that are interested in 2021-CS109B are comparing it to the libraries listed below
Sorting:
- Interpretable Machine Learning with Python, published by Packt☆475Updated last week
- ☆116Updated 3 years ago
- A series of Python Jupyter notebooks that help you better understand "The Elements of Statistical Learning" book☆903Updated 4 years ago
- Hands-On Gradient Boosting with XGBoost and Scikit-learn Published by Packt☆215Updated last week
- ☆141Updated 3 years ago
- My notes and codes (jupyter notebooks) for the "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani and Jerome Frie…☆419Updated last month
- This is a companion to the ‘Mathematical Foundations’ section of the book, Mathematics for Machine Learning by Marc Deisenroth, Aldo Fais…☆295Updated 5 years ago
- Solutions to labs and excercises from An Introduction to Statistical Learning, as Jupyter Notebooks.☆344Updated last year
- An Introduction to Statistical Learning with Applications in PYTHON☆551Updated 3 years ago
- Python-centered read-along of Forecasting: Principles and Practice☆510Updated last month
- ☆151Updated 3 years ago
- Porting the R code in ISL to python. Labs and exercises☆203Updated 3 years ago
- COMS W4995 Applied Machine Learning - Spring 20☆243Updated 3 years ago
- Summary of each chapter of the book- Introduction of Statistical Learning (ISL), along with Python code & data.☆185Updated last year
- Labs and Project from the course "How to Win a Data Science Competition: Learn from Top Kagglers"☆148Updated 6 years ago
- A collection of resources for learning and research.☆97Updated 6 months ago
- Notes for 18.6501x, Fundamentals of Statistics on edX☆96Updated 3 years ago
- STAT 451: Intro to Machine Learning @ UW-Madison (Fall 2020)☆443Updated last year
- Cracking the Data Science Interview☆361Updated 5 years ago
- ☆553Updated last year
- Code and files to go along with CS329s machine learning model deployment tutorial.☆615Updated 3 years ago
- Course notes for MSDS621 at Univ of San Francisco, introduction to machine learning☆347Updated 3 years ago
- Notes of MITx 6.86x - Machine Learning with Python: from Linear Models to Deep Learning☆332Updated last year
- ☆66Updated 2 years ago
- Code repository for the online course Feature Engineering for Machine Learning☆404Updated last year
- Applied Machine Learning Explainability Techniques, published by Packt☆246Updated last week
- Python Feature Engineering Cookbook, published by Packt☆483Updated last week
- Repository for CS109A Fall 2018☆150Updated 5 years ago
- 🌀 Stanford CS 228 - Probabilistic Graphical Models☆90Updated 6 years ago
- Chapter by Chapter notes, exercises and code for a variety of machine learning books using Python☆939Updated 2 years ago