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-2-22-12-naive-bayes-and-nlp-in-sklearn-nyc-career-ds-102218
evanavaughan/dsc-3-31-02-introduction-to-PAC-learning-theory-nyc-career-ds-102218
evanavaughan/dsc-3-31-03-introduction-to-decision-trees-nyc-career-ds-102218
evanavaughan/dsc-3-31-04-entropy-information-gain-nyc-career-ds-102218
evanavaughan/dsc-3-31-06-decision-tree-scikitlearn-codealong-nyc-career-ds-102218
evanavaughan/dsc-3-31-07-decision-tree-scikitlearn-lab-nyc-career-ds-102218
evanavaughan/dsc-3-31-08-decision-tree-pruning-hyperparameter-optimization-lab-nyc-career-ds-102218
evanavaughan/dsc-3-31-08-decision-tree-pruning-hyperparameter-optimization-nyc-career-ds-102218
evanavaughan/dsc-3-31-09-regression-cart-trees-codealong-nyc-career-ds-102218
evanavaughan/dsc-3-31-11-regression-trees-model-optimization-lab-nyc-career-ds-102218
evanavaughan/dsc-3-32-02-ensemble-methods-nyc-career-ds-102218
evanavaughan/dsc-3-32-03-random-forests-nyc-career-ds-102218
evanavaughan/dsc-3-32-04-tree-ensembles-and-random-forests-lab-nyc-career-ds-102218
evanavaughan/dsc-3-32-05-gridsearchcv-nyc-career-ds-102218
evanavaughan/dsc-3-32-10-xgboost-nyc-career-ds-102218
evanavaughan/dsc-3-32-11-xgboost-lab-nyc-career-ds-102218
evanavaughan/dsc-3-33-02-introduction-to-support-vector-machines-nyc-career-ds-102218
evanavaughan/dsc-3-33-03-building-an-svm-from-scratch-lab-nyc-career-ds-102218
evanavaughan/dsc-3-33-04-building-an-svm-using-scikit-learn-nyc-career-ds-102218
evanavaughan/dsc-3-33-05-the-kernel-trick-nyc-career-ds-102218
evanavaughan/dsc-3-33-06-kernels-in-scikit-learn-lab-nyc-career-ds-102218
evanavaughan/dsc-3-34-07-pca-implementation-visualization-python-numpy-nyc-career-ds-102218
evanavaughan/dsc-3-36-02-Introduction-to-pipelines-nyc-career-ds-102218
evanavaughan/dsc-3-36-03-comparing-machine-learning-techniques-using-pipelines-lab-nyc-career-ds-102218
evanavaughan/dsc-4-37-02-introduction-to-nlp-with-nltk-nyc-career-ds-102218
evanavaughan/dsc-4-37-03-introduction-to-regular-expressions-nyc-career-ds-102218
evanavaughan/dsc-4-37-04-regular-expressions-lab-nyc-career-ds-102218
evanavaughan/dsc-4-37-05-feature-engineering-for-text-data-nyc-career-ds-102218
evanavaughan/dsc-4-37-06-corpus-statistics-lab-nyc-career-ds-102218
evanavaughan/text_examples