This collection features a diverse set of data and artificial intelligence (AI) notebook templates, covering a wide range of machine learning topics. These notebooks are curated to provide valuable resources, including models, analytics, code snippets, and more.
"Cool-Notebooks" is a comprehensive collection of Jupyter notebooks designed to facilitate learning and implementation in the field of machine learning. Whether you are a beginner exploring the basics or an experienced practitioner seeking advanced models, you'll find a wealth of resources in this repository.
Explore notebooks covering a variety of machine learning topics, including but not limited to:
- Machine Learning
- Deep Learning
- Natural Language Processing
- Computer Vision
- Experimental Notebooks
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Clone the Repository:
git clone https://github.com/ML-Nagpur/Cool-Notebooks.git
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Install Dependencies: Before running the notebooks, ensure you have the necessary dependencies installed. You can typically install them using a package manager like pip. Example:
pip install -r requirements.txt
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Explore Notebooks: Navigate to the
Notebooksdirectory and explore the available Jupyter notebooks. Each notebook covers a specific machine learning topic, and you can find detailed explanations, code snippets, and more. -
Run the Notebooks: Open Jupyter Notebook in your preferred environment and run the notebooks. Experiment with the code, modify parameters, and observe the results.
- Visit CONTRIBUTOR.md
Join our vibrant community on Discord for discussions, questions, and updates. Connect with fellow learners and practitioners to enhance your ML journey.
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├── CODE_OF_CONDUCT.md
├── CONTRIBUTING.md
├── Computer Vision
│ ├── Face_Detection
│ │ ├── Face_Detection.py
│ │ └── requirements.txt
│ └── Hand_Tracking
│ ├── Hand_Tracking.py
│ └── requirements.txt
├── Deep Learning
│ ├── Convolutional Neural Networks (CNN)
│ │ └── Python
│ │ ├── Convolution Neural Network for MNIST Handwritten Digits Classification.ipynb
│ │ ├── Convolution Neural Network for MNIST Handwritten Digits Classification.py
│ │ ├── convolutional_neural_network.ipynb
│ │ └── convolutional_neural_network.py
│ ├── Artificial Neural Networks (ANN)
│ │ ├── Python
│ │ │ ├── Churn_Modelling.csv
│ │ │ ├── artificial_neural_network.ipynb
│ │ │ └── artificial_neural_network.py
│ │ └── Stochastic_Gradient_Descent.png
│ ├── Gated Recurrent Unit (GRU)
│ │ ├── Gated_Recurrent_Unit_(GRU).ipynb
│ │ ├── Mastercard_stock_history.csv
│ │ └── gated_recurrent_unit_(gru).py
│ ├── Long Short Term Memory (LSTM)
│ │ ├── Long_Short_Term_Memory_(LSTM).ipynb
│ │ ├── Mastercard_stock_history.csv
│ │ └── long_short_term_memory_(lstm).py
│ ├── Multi-layer perceptron (MLP)
│ │ ├── Multi_Layer_Perceptron_(MLP)_Model.ipynb
│ │ └── multi_layer_perceptron_(mlp)_model.py
│ ├── Recurrent Neural Networks (RNN)
│ │ ├── Recurrent_Neural_Networks_(RNN).ipynb
│ │ └── recurrent_neural_networks_(rnn).py
│ └── single-layer perceptron (SLP)
│ ├── single_layer_perceptron_(SLP)_Model.ipynb
│ └── single_layer_perceptron_(slp)_model.py
├── Experimental Notebooks
│ ├── Automated ML Classification Template
│ │ └── AutomatedClassification.ipynb
│ └── Categorise-Data-From-Single-Feature-Using-NLTK-main
│ ├── All Sports Fitness and Outdoors.csv
│ ├── CategorisationOfData_.ipynb
│ └── sports.csv
├── Generative AI
│ └── Transformers_(GPT_2)_for_Text_Generation
│ ├── Transformers_(GPT_2)_for_Text_Generation.ipynb
│ └── transformers_(gpt_2)_for_text_generation.py
├── LICENSE
├── Machine Learning Notebooks
│ ├── Classification
│ │ ├── Logistic Regression
│ │ │ └── Python
│ │ │ ├── Color Blind Friendly Images
│ │ │ │ ├── logistic_regression_test_set.png
│ │ │ │ └── logistic_regression_training_set.png
│ │ │ ├── Social_Network_Ads.csv
│ │ │ ├── logistic_regression.ipynb
│ │ │ └── logistic_regression.py
│ │ ├── Decision Tree Classification
│ │ │ └── Python
│ │ │ ├── Color Blind Friendly Images
│ │ │ │ ├── decision_tree_classification_test_set.png
│ │ │ │ └── decision_tree_classification_training_set.png
│ │ │ ├── Social_Network_Ads.csv
│ │ │ ├── decision_tree_classification.ipynb
│ │ │ └── decision_tree_classification.py
│ │ ├── K-Nearest Neighbors (K-NN)
│ │ │ └── Python
│ │ │ ├── Color Blind Friendly Images
│ │ │ │ ├── knn_test_set.png
│ │ │ │ └── knn_training_set.png
│ │ │ ├── Social_Network_Ads.csv
│ │ │ ├── k_nearest_neighbors.ipynb
│ │ │ └── k_nearest_neighbors.py
│ │ ├── Kernel SVM
│ │ │ └── Python
│ │ │ ├── Color Blind Friendly Images
│ │ │ │ ├── kernel_svm_test_set.png
│ │ │ │ └── kernel_svm_training_set.png
│ │ │ ├── Social_Network_Ads.csv
│ │ │ ├── kernel_svm.ipynb
│ │ │ └── kernel_svm.py
│ │ ├── Naive Bayes
│ │ │ └── Python
│ │ │ ├── Color Blind Friendly Images
│ │ │ │ ├── naive_bayes_test_set.png
│ │ │ │ └── naive_bayes_training_set.png
│ │ │ ├── Social_Network_Ads.csv
│ │ │ ├── naive_bayes.ipynb
│ │ │ └── naive_bayes.py
│ │ ├── Random Forest Classification
│ │ │ └── Python
│ │ │ ├── Color Blind Friendly Images
│ │ │ │ ├── random_forest_classification_test_set.png
│ │ │ │ └── random_forest_classification_training_set.png
│ │ │ ├── Social_Network_Ads.csv
│ │ │ ├── random_forest_classification.ipynb
│ │ │ └── random_forest_classification.py
│ │ └── Support Vector Machine (SVM)
│ │ └── Python
│ │ ├── Color Blind Friendly Images
│ │ │ ├── svm_test_set.png
│ │ │ └── svm_training_set.png
│ │ ├── Social_Network_Ads.csv
│ │ ├── support_vector_machine.ipynb
│ │ └── support_vector_machine.py
│ ├── Clustering
│ │ ├── K-Means Clustering
│ │ │ └── Python
│ │ │ ├── Mall_Customers.csv
│ │ │ ├── k_means_clustering.ipynb
│ │ │ └── k_means_clustering.py
│ │ └── Hierarchical Clustering
│ │ └── Python
│ │ ├── Mall_Customers.csv
│ │ ├── hierarchical_clustering.ipynb
│ │ └── hierarchical_clustering.py
│ ├── Data Preprocessing
│ │ └── Python
│ │ ├── Data.csv
│ │ ├── data_preprocessing_template.ipynb
│ │ ├── data_preprocessing_template.py
│ │ ├── data_preprocessing_tools.ipynb
│ │ └── data_preprocessing_tools.py
│ └── Regression
│ ├── Polynomial Regression
│ │ └── Python
│ │ ├── Position_Salaries.csv
│ │ ├── polynomial_regression.ipynb
│ │ └── polynomial_regression.py
│ ├── Decision Tree Regression
│ │ └── Python
│ │ ├── Position_Salaries.csv
│ │ ├── decision_tree_regression.ipynb
│ │ └── decision_tree_regression.py
│ ├── Multiple Linear Regression
│ │ └── Python
│ │ ├── 50_Startups.csv
│ │ ├── multiple_linear_regression.ipynb
│ │ └── multiple_linear_regression.py
│ ├── Random Forest Regression
│ │ └── Python
│ │ ├── Position_Salaries.csv
│ │ ├── random_forest_regression.ipynb
│ │ └── random_forest_regression.py
│ ├── Simple Linear Regression
│ │ └── Python
│ │ ├── Salary_Data.csv
│ │ ├── simple_linear_regression.ipynb
│ │ └── simple_linear_regression.py
│ └── Support Vector Regression (SVR)
│ └── Python
│ ├── Position_Salaries.csv
│ ├── support_vector_regression.ipynb
│ └── support_vector_regression.py
├── Natural Language Processing
│ ├── Flipkart_Sentiment_Analysis
│ │ ├── Flipkart_Sentiment_Analysis.ipynb
│ │ ├── flipkart_product_.csv
│ │ └── flipkart_sentiment_analysis.py
│ └── Spam_or_Ham_Classification
│ ├── SMSSpamCollection.csv
│ ├── Spam_or_Ham_classifier_nlp.ipynb
│ └── spam_or_ham_classifier_nlp.py
├── SECURITY.md
└── directory_tree.txt
64 directories, 105 files
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