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mkdocs.yml
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site_name: Complete Machine Learning Package
site_author: Jean de Dieu Nyandwi
repo_url: https://github.com/Nyandwi/machine_learning_complete/
repo_name: Nyandwi/machine_learning_complete
site_description: >-
Learn Machine Learning through understanding and doing!
copyright: Created by Jean de Dieu Nyandwi
edit_uri: ""
theme:
name: material
favicon: assets/logo.png
logo: assets/logo.png
icon:
logo: logo
repo: fontawesome/brands/github-square
# Necessary for search to work properly
include_search_page: false
search_index_only: true
palette:
- scheme: default
primary: black
accent: indigo
toggle:
icon: material/toggle-switch
name: Switch to dark mode
- scheme: slate
primary: indigo
accent: indigo
toggle:
icon: material/toggle-switch-off-outline
name: Switch to light mode
# Default values, taken from mkdocs_theme.yml
language: en
font:
text: Roboto
code: Roboto Mono
features:
- content.code.annotate
- navigation.indexes
- navigation.tabs
- navigation.top
- navigation.tracking
- search.highlight
- search.share
- search.suggest
- toc.follow
markdown_extensions:
- meta
- pymdownx.highlight
- pymdownx.superfences
- pymdownx.tasklist:
custom_checkbox: true
plugins:
- mkdocs-jupyter
- search
- social
nav:
- Home: 'index.md'
- Programming:
- '00 Python for Machine Learning': '00_intro_to_python.ipynb'
- Working with Data:
- Data Computations with NumPy:
- '01 Introduction to NumPy': '01_intro_to_Numpy_for_data_computation.ipynb'
- Data Manipulation with Pandas:
- '02 Pandas': '02_data_manipulation_with_pandas.ipynb'
- Data Visualization:
- '03 Data Visualization with Matplotlib': '03_data_visualizations_with_matplotlib.ipynb'
- '04 Data Visualization with Seaborn': '04_data_visualization_with_seaborn.ipynb'
- '05 Data Visualization with Pandas': '05_data_visualization with_pandas.ipynb'
- Data Analysis and Preparation:
- '06 Exploratory Data Analysis': '06_exploratory_data_analysis.ipynb'
- '07 Intro to Data Preparation': '07_intro_to_data_preparation.ipynb'
- '08 Feature Encoding': '08_encoding_categorical_features.ipynb'
- '09 Feature Scaling': '09_feature_scaling.ipynb'
- '10 Handling Missing Values': '10_handling_missing_values.ipynb'
- Machine Learning:
- Machine Learning Fundamentals:
- '11 ML Fundamentals': '11_ml_fundamentals.md'
- Classical Machine Learning with Scikit-Learn:
- '12 Intro to Scikit-Learn': '12_intro_to_sklearn.ipynb'
- '13 Linear Models for Regression': '13_linear_models_for_regression.ipynb'
- '14 Linear Models for Classification': '14_linear_models_for_classification.ipynb'
- '15 SVM for Regression': '15_support_vector_machines_for_regression.ipynb'
- '16 SVM for Classification': '16_support_vector_machines_for_classification.ipynb'
- '17 Decision Trees for Regression': '17_decision_trees_for_regression.ipynb'
- '18 Decision Trees for Classification': '18_decision_trees_for_classification.ipynb'
- '19 Random Forests for Regression': '19_random_forests_for_regression.ipynb'
- '20 Random Forests for Classification': '20_random_forests_for_classification.ipynb'
- '21 Ensemble Models': '21_ensemble_models.ipynb'
- '22 Unsupervised learning': '22_intro_to_unsupervised_learning_with_kmeans_clustering.ipynb'
- '23 PCA': '23_a_practical_intro_to_principal_components_analysis.ipynb'
- Deep Learning:
- Introduction to Deep Learning:
- '24 Intro to Neural Networks': '24_intro_to_neural_networks.ipynb'
- '25 Intro to DL with TensorFlow': '25_intro_to_tensorflow_for_deeplearning.ipynb'
- '26 Neural Nets for Regression': '26_neural_networks_for_regresion_with_tensorflow.ipynb'
- '27 Neural Nets for Classification': '27_neural_networks_for_classification_with_tensorflow.ipynb'
- Deep Computer Vision:
- '28 Intro to ConvNets for Computer Vision': '28_intro_to_computer_vision_and_cnn.ipynb'
- '29 ConvNets and Data Augmentations': '29_cnn_for_real_world_data_and_image_augmentation.ipynb'
- '30 Transfer Learning with Pre-trained ConvNets': '30_cnn_architectures_and_transfer_learning.ipynb'
- Natural Language Processing:
- '31 Intro to Text Processing with TensorFlow': '31_intro_to_nlp_and_text_preprocessing.ipynb'
- '32 Word Embeddings': '32_using_word_embeddings_to_represent_texts.ipynb'
- '33 RNNs': '33_recurrent_neural_networks.ipynb'
- '34 ConvNets for Text Classification': '34_using_cnns_and_rnns_for_texts_classification.ipynb'
- '35 Using Pre-trained BERT': '35_using_pretrained_bert_for_text_classification.ipynb'
- MLOps:
- MLOps Guide: '36_mlops_guide.md'
- Others:
- Complete Outline: 'outline.md'
- Further Resources: 'extras/resources.md'
- Tools Overview: 'extras/tools-overview.md'
- Acknowledgment: 'extras/ack.md'
extra:
social:
- icon: fontawesome/brands/twitter
link: https://twitter.com/Jeande_d
- icon: fontawesome/brands/github
link: https://github.com/Nyandwi
- icon: fontawesome/brands/youtube
link: https://www.youtube.com/channel/UCSPFIgLyc2t-pNim-CdyBNQ
- icon: fontawesome/brands/linkedin
link: https://www.linkedin.com/in/nyandwi
- icon: fontawesome/brands/medium
link: https://jeande.medium.com/
- icon: fontawesome/brands/instagram
link: https://www.instagram.com/nyandwi.de
analytics:
provider: google
property: G-662D4ZFE3K