Pinned Repositories
applying-gradient-descent-data-science-intro-000
applying-gradient-descent-lab-data-science-intro-000
applying-nearest-neighbors-data-science-intro-000
architectural_styles
Version 1.2 of TensorFlow - US Architectural Style Image Classification
auto_sales_prediction
IN PROGRESS - Web Scraping, NLP(BERT), Random Forrest, Decision Trees, Linear Regression (Python, MongoDB)
byte
music_recommendation_systems
Recommendation system using content-based and collaborative-filtering (Surprise! and Amazon SageMaker)
nyc_restaurant_ratings
Web Scraping (Beautiful Soup), Database Creation (SQLAlchemy), Front-End Graphs (Dash, Flask)
predicting_wine_varietals
Web Scraping (Selenium), Natural Language Processing (NLP), Machine Learning (Scikit-Learn)
us_house_architecture
Image Classification for U.S. House Architectural Styles (CNN using TensorFlow on Google Cloud)
evanavaughan's Repositories
evanavaughan/dsc-1-11-05-dealing-with-categorical-variables-lab-nyc-career-ds-102218
evanavaughan/dsc-1-11-06-multicollinearity-of-features-nyc-career-ds-102218
evanavaughan/dsc-1-11-07-multicollinearity-of-features-lab-nyc-career-ds-102218
evanavaughan/dsc-1-11-13-adjusted-r-squared-lab-nyc-career-ds-102218
evanavaughan/dsc-1-12-022-data-science-processes-nyc-career-ds-102218
evanavaughan/dsc-2-13-03-lingalg-motivation-nyc-career-ds-102218
evanavaughan/dsc-2-13-05-lingalg-linear-equations-quiz-nyc-career-ds-102218
evanavaughan/dsc-2-13-07-linalg-vector-addition-codealong-nyc-career-ds-102218
evanavaughan/dsc-2-13-08-linalg-vector-matrices-numpy-lab-nyc-career-ds-102218
evanavaughan/dsc-2-13-09-linalg-mat-multiplication-codealong-nyc-career-ds-102218
evanavaughan/dsc-2-13-10-linalg-dot-product-properties-lab-nyc-career-ds-102218
evanavaughan/dsc-2-13-11-linalg-python-vs-numpy-lab-nyc-career-ds-102218
evanavaughan/dsc-2-13-12-linalg-lineq-numpy-codealong-nyc-career-ds-102218
evanavaughan/dsc-2-13-13-linalg-lineq-numpy-lab-nyc-career-ds-102218
evanavaughan/dsc-2-14-03-introduction-to-derivatives-nyc-career-ds-102218
evanavaughan/dsc-2-14-04-introduction-to-derivatives-lab-nyc-career-ds-102218
evanavaughan/dsc-2-14-05-derivatives-of-non-linear-functions-nyc-career-ds-102218
evanavaughan/dsc-2-14-06-rules-for-derivatives-nyc-career-ds-102218
evanavaughan/dsc-2-14-07-rules-for-derivatives-lab-nyc-career-ds-102218
evanavaughan/dsc-2-14-08-derivatives-the-chain-rule-nyc-career-ds-102218
evanavaughan/dsc-2-14-09-derivatives-conclusion-nyc-career-ds-102218
evanavaughan/dsc-2-14-10-introduction-to-gradient-descent-nyc-career-ds-102218
evanavaughan/dsc-2-14-11-gradient-descent-step-sizes-nyc-career-ds-102218
evanavaughan/dsc-2-14-12-gradient-descent-step-sizes-lab-nyc-career-ds-102218
evanavaughan/dsc-2-14-13-gradient-descent-in-3d-nyc-career-ds-102218
evanavaughan/dsc-2-14-14-the-gradient-in-gradient-descent-nyc-career-ds-102218
evanavaughan/dsc-2-14-15-gradient-to-cost-function-nyc-career-ds-102218
evanavaughan/dsc-2-21-11-PE-MLE-nyc-career-ds-102218
evanavaughan/dsc-2-22-03-MLE-Gaussian-nyc-career-ds-102218
evanavaughan/dsc-2-24-05-polynomial-regression-nyc-career-ds-102218