{"id":20275094,"url":"https://github.com/danhenriquex/data_science_and_machine_learning","last_synced_at":"2026-04-13T04:03:28.543Z","repository":{"id":156306569,"uuid":"612645658","full_name":"danhenriquex/Data_Science_and_Machine_Learning","owner":"danhenriquex","description":"A.I, Data Science \u0026 Machine Learning and Deep Learning with Tensorflow","archived":false,"fork":false,"pushed_at":"2024-08-28T20:44:40.000Z","size":23126,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2026-01-03T22:05:34.918Z","etag":null,"topics":["deep-learning","deep-neural-networks","machine-learning","matplotlib","numpy","pandas","tensorflow"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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and deep learning, such as:\n\n### Machine Learning\n- Pandas\n- NumPy\n- MatPlotlib\n- Scikit-Learn\n- Splitting into training, validation and test set\n- Cleaning, transforming and reducing dataset\n- Working with categorical classification ( OneHotEncoder )\n- Handling missing values\n- Regression, Classification, Decision trees and others machine learning algorithms\n- Making predictions and evaluating models with score, cross validation, accuracy, ROC Curve, confusion matrix, classification report, MAE, MSE\n- Tuning Hyperparameters with GridSearch and RandomizedSearchCV\n- Saving and Loading model.\n\n### Deep Learning\n\n- Deep Learning with tensorflow\n- Turning data Labels into Numbers\n- Preprocess images and turning data into batches\n- Building a deep learning model using some architectures such as Mobilenet.\n- Handling overfitting and underfitting.\n- Evaluating the 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