This page contains links to my Colab/Jupyter notebooks, as well as useful paid and free online resources. Also, see my GitHub Repo here: https://github.com/DataHurdler/UdemyPracticeCode.
Project Notebooks
Simple TF-IDF Movie Recommender
This is an exercise from Section 2 of Udemy's Machine Learning: Natural Language Processing in Python by the Lazy Programmer. The exercise builds a movie recommender with ONLY TF-IDF (no learning).
Time Series Analysis of Movie Sales in Shanghai
I analyze movie sales in Shanghai from 2012-2015 with exponential smoothing (from statsmodels) and Facebook's Prophet. The data is reduced from the one used in my research paper "The impact of air pollution on movie theater admissions".
Online Resources
YouTube
- Learn Python - Full Course for Beginners [Link]
- Python Tutorial - Python Full Course for Beginners [Link]
- Intermediate Python Programming Course [Link]
- Machine Learning Course in Python [Link]
- SQL Tutorial - Advanced Concepts [Link]
Coursera/Udemy Courses
- Machine Learning [Link]
- 100 Days of Code: The Complete Python Pro Bootcamp [Link]
- Complete Machine Learning and Data Science Bootcamp [Link]
- Machine Learning: Natural Language Processing in Python V2 [Link]
- Advanced SQL: MySQL Data Analysis and Business Intelligence [Link]
Datasets
Books
- Causal Inference: The Mixtape [Link]
- Discrete Choice Methods with Simulation [Link]
- Forecasting: Principles and Practice [Link]
- An Introduction to Statistical Learning [Link]
- Deep Learning [Link]
- Introduction to Probability for Data Science [Link]
- Scientific Visualization: Python + Matplotlib [Link]
- Reinforcement Learning: An Introduction [Link]
- R for Data Science (2e) [Link]
Web Sites/Pages
- Comprehensive list of activation functions in neural networks with pros/cons [Link]