├── .DS_Store ├── .cache └── plugin │ └── social │ ├── 98f3b526069394c6fc35ea0856045851.png │ ├── Roboto-Black.ttf │ ├── Roboto-BlackItalic.ttf │ ├── Roboto-Bold.ttf │ ├── Roboto-BoldItalic.ttf │ ├── Roboto-Italic.ttf │ ├── Roboto-Light.ttf │ ├── Roboto-LightItalic.ttf │ ├── Roboto-Medium.ttf │ ├── Roboto-MediumItalic.ttf │ ├── Roboto-Regular.ttf │ ├── Roboto-Thin.ttf │ ├── Roboto-ThinItalic.ttf │ └── ac0d77e7857963d896b8b172cdbcf03f.png ├── .gitignore ├── 010_mlops └── 1_mlops_guide.md ├── 0_python_for_ml ├── .DS_Store └── intro_to_python.ipynb ├── 1_data_computations_with_numpy ├── 1_intro_to_Numpy_for_data_computation.ipynb └── detailed_notes_on_numpy.pdf ├── 2_data_manipulation_with_pandas ├── .ipynb_checkpoints │ └── 2_data_manipulation_with_pandas-checkpoint.ipynb ├── 2_data_manipulation_with_pandas.ipynb ├── housing.csv ├── housing_dataset ├── housing_excel.xlsx └── winequality-red.csv ├── 3_data_visualization ├── 1_data_visualizations_with_matplotlib.ipynb ├── 2_data_visualization_with_seaborn.ipynb ├── 3_data_visualization with_pandas.ipynb └── Savedfigure.png ├── 4_real_world_data_analysis_and_preparation ├── 1_exploratory_data_analysis.ipynb ├── 2_intro_to_data_preparation.ipynb ├── 3_encoding_categorical_features.ipynb ├── 4_feature_scaling.ipynb └── 5_handling_missing_values.ipynb ├── 5_intro_to_machine_learning ├── .DS_Store └── ml_fundamentals.md ├── 6_classical_machine_learning_with_scikit-learn ├── .ipynb_checkpoints │ └── 11_a_practical_intro_to_principal_components_analysis-checkpoint.ipynb ├── 0_intro_to_sklearn.ipynb ├── 10_intro_to_unsupervised_learning_with_kmeans_clustering.ipynb ├── 11_a_practical_intro_to_principal_components_analysis.ipynb ├── 1_linear_models_for_regression.ipynb ├── 2_linear_models_for_classification.ipynb ├── 3_support_vector_machines_for_regression.ipynb ├── 4_support_vector_machines_for_classification.ipynb ├── 5_decision_trees_for_regression.ipynb ├── 6_decision_trees_for_classification.ipynb ├── 7_random_forests_for_regression.ipynb ├── 8_random_forests_for_classification.ipynb └── 9_ensemble_models.ipynb ├── 7_intro_to_artificial _neural_networks_and_tensorflow ├── 1_intro_to_neural_networks.ipynb ├── 2_intro_to_tensorflow_for_deeplearning.ipynb ├── 3_neural_networks_for_regresion_with_tensorflow.ipynb └── 4_neural_networks_for_classification_with_tensorflow.ipynb ├── 8_deep_computer_vision_with_tensorflow ├── .DS_Store ├── 1_intro_to_computer_vision_and_cnn.ipynb ├── 2_cnn_for_real_world_data_and_image_augmentation.ipynb └── 3_transfer_learning_convnets.ipynb ├── 9_nlp_with_tensorflow ├── 1_intro_to_nlp_and_text_preprocessing.ipynb ├── 2_using_word_embeddings_to_represent_texts.ipynb ├── 3_recurrent_neural_networks.ipynb ├── 4_using_cnns_and_rnns_for_texts_classification.ipynb └── 5_using_pretrained_bert_for_text_classification.ipynb ├── Data ├── .DS_Store ├── Mobiletest.csv ├── Mobiletrain.csv ├── Readme.md ├── dataset_21_car.csv ├── dataset_2216_machine_cpu.csv └── electricity-normalized.csv ├── LICENSE ├── Readme.md ├── docs ├── .DS_Store ├── 00_intro_to_python.ipynb ├── 01_intro_to_Numpy_for_data_computation.ipynb ├── 02_data_manipulation_with_pandas.ipynb ├── 03_data_visualizations_with_matplotlib.ipynb ├── 04_data_visualization_with_seaborn.ipynb ├── 05_data_visualization with_pandas.ipynb ├── 06_exploratory_data_analysis.ipynb ├── 07_intro_to_data_preparation.ipynb ├── 08_encoding_categorical_features.ipynb ├── 09_feature_scaling.ipynb ├── 10_handling_missing_values.ipynb ├── 11_ml_fundamentals.md ├── 12_intro_to_sklearn.ipynb ├── 13_linear_models_for_regression.ipynb ├── 14_linear_models_for_classification.ipynb ├── 15_support_vector_machines_for_regression.ipynb ├── 16_support_vector_machines_for_classification.ipynb ├── 17_decision_trees_for_regression.ipynb ├── 18_decision_trees_for_classification.ipynb ├── 19_random_forests_for_regression.ipynb ├── 20_random_forests_for_classification.ipynb ├── 21_ensemble_models.ipynb ├── 22_intro_to_unsupervised_learning_with_kmeans_clustering.ipynb ├── 23_a_practical_intro_to_principal_components_analysis.ipynb ├── 24_intro_to_neural_networks.ipynb ├── 25_intro_to_tensorflow_for_deeplearning.ipynb ├── 26_neural_networks_for_regresion_with_tensorflow.ipynb ├── 27_neural_networks_for_classification_with_tensorflow.ipynb ├── 28_intro_to_computer_vision_and_cnn.ipynb ├── 29_cnn_for_real_world_data_and_image_augmentation.ipynb ├── 30_cnn_architectures_and_transfer_learning.ipynb ├── 31_intro_to_nlp_and_text_preprocessing.ipynb ├── 32_using_word_embeddings_to_represent_texts.ipynb ├── 33_recurrent_neural_networks.ipynb ├── 34_using_cnns_and_rnns_for_texts_classification.ipynb ├── 35_using_pretrained_bert_for_text_classification.ipynb ├── assets │ ├── .DS_Store │ ├── git_cover.png │ ├── logo.png │ └── profile-pic.jpg ├── extras │ ├── .DS_Store │ ├── ack.md │ ├── resources.md │ └── tools-overview.md ├── index.md └── outline.md ├── images ├── ai-ml-dl.png ├── class-reg.png ├── 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