jtenini / applied_ml_course
This repository contains notes and code for the DSBA 6156: Applied Machine Learning course taught at the University of North Carolina at Charlotte in the spring of 2023.
☆9Updated 2 years ago
Alternatives and similar repositories for applied_ml_course:
Users that are interested in applied_ml_course are comparing it to the libraries listed below
- Lecture notes, slides, and exercises for the Medical Image Analysis course at Aalto University and Technical University of Denmark☆13Updated 5 months ago
- Solutions to the exercises in Grinstead and Snell's Introduction to Probability☆86Updated 2 years ago
- The MOOCs I learnt myself. The repo is kept as a record for myself.☆8Updated 2 years ago
- Solutions for All of Statistics by Wasserman☆12Updated 4 years ago
- My notes and codes (jupyter notebooks) for the "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani and Jerome Frie…☆408Updated 3 years ago
- Repository for my master's degree graduation work☆18Updated 3 years ago
- A repository with solutions to the assignments on Andrew Ng's machine learning MOOC on Coursera☆279Updated 2 years ago
- STAT 451: Intro to Machine Learning @ UW-Madison (Fall 2020)☆419Updated 6 months ago
- Collection of solutions for assigned questions from Combinatorics and Graph Theory Second Edition.☆3Updated 5 years ago
- lecture notes that I wished were available somewhere☆15Updated 2 years ago
- A course in numerical methods with Python for engineers and scientists: currently 5 learning modules, with student assignments.☆919Updated last month
- My notes from the theoretical physics degree at Edinburgh University☆49Updated 8 months ago
- Jupyter notebooks for summarizing and reproducing the textbook "The Elements of Statistical Learning" 2/E by Hastie, Tibshirani, and Frie…☆287Updated 6 years ago
- ☆39Updated 3 months ago
- A series of Python Jupyter notebooks that help you better understand "The Elements of Statistical Learning" book☆879Updated 3 years ago
- Repository of my solutions to the problems of "Learning from Data"☆274Updated 5 years ago
- VIP cheatsheets for Stanford's CME 106 Probability and Statistics for Engineers☆732Updated 4 years ago
- Project code for MIT MOOC 6.86x on edX☆16Updated 5 years ago
- CME211 Notes☆255Updated 2 years ago
- Unofficial solutions to Understanding Analysis by Stephen Abbott (1st Edition)☆109Updated last year
- Introduction to Analytics Modeling - Georgia Tech OMDS☆52Updated 5 years ago
- Solutions to labs and excercises from An Introduction to Statistical Learning, as Jupyter Notebooks.☆328Updated 9 months ago
- These are my solutions for 4 weeks of Principal Component Analysis course in Python.☆14Updated 4 years ago
- Notes for 18.6501x, Fundamentals of Statistics on edX☆93Updated 2 years ago
- Deep Learning specialization☆57Updated 6 years ago
- Chapter by Chapter notes, exercises and code for a variety of machine learning books using Python☆932Updated 2 years ago
- All notes and materials for the CS229: Machine Learning course by Stanford University☆215Updated 3 years ago
- A guide on STEM PhD admissions☆438Updated 5 months ago
- A collection of notebooks of my Machine Learning class written in python 3☆44Updated 6 years ago
- Introductory course in Computational Physics, including linear algebra, eigenvalue problems, differential equations, Monte Carlo methods …☆378Updated 3 years ago