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..
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assets
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extras
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00_intro_to_python.ipynb
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01_intro_to_Numpy_for_data_computation.ipynb
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02_data_manipulation_with_pandas.ipynb
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03_data_visualizations_with_matplotlib.ipynb
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04_data_visualization_with_seaborn.ipynb
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05_data_visualization with_pandas.ipynb
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06_exploratory_data_analysis.ipynb
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07_intro_to_data_preparation.ipynb
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08_encoding_categorical_features.ipynb
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09_feature_scaling.ipynb
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10_handling_missing_values.ipynb
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12_intro_to_sklearn.ipynb
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13_linear_models_for_regression.ipynb
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14_linear_models_for_classification.ipynb
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15_support_vector_machines_for_regression.ipynb
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16_support_vector_machines_for_classification.ipynb
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17_decision_trees_for_regression.ipynb
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18_decision_trees_for_classification.ipynb
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19_random_forests_for_regression.ipynb
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20_random_forests_for_classification.ipynb
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21_ensemble_models.ipynb
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22_intro_to_unsupervised_learning_with_kmeans_clustering.ipynb
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23_a_practical_intro_to_principal_components_analysis.ipynb
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24_intro_to_neural_networks.ipynb
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25_intro_to_tensorflow_for_deeplearning.ipynb
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26_neural_networks_for_regresion_with_tensorflow.ipynb
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27_neural_networks_for_classification_with_tensorflow.ipynb
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28_intro_to_computer_vision_and_cnn.ipynb
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29_cnn_for_real_world_data_and_image_augmentation.ipynb
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30_cnn_architectures_and_transfer_learning.ipynb
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31_intro_to_nlp_and_text_preprocessing.ipynb
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32_using_word_embeddings_to_represent_texts.ipynb
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33_recurrent_neural_networks.ipynb
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34_using_cnns_and_rnns_for_texts_classification.ipynb
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35_using_pretrained_bert_for_text_classification.ipynb
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.DS_Store
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11_ml_fundamentals.md
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index.md
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outline.md
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