/Cool-Notebooks

An extensive collection of data and artificial intelligence (AI) notebook templates, curated to encompass models, analytics, code snippets, and a variety of resources. ~ Team ML Nagpur

Primary LanguageJupyter NotebookMIT LicenseMIT

Typing SVG

ML Nagpur Logo

Repository Visitors

Welcome to the "Cool Notebooks" repository by Team ML Nagpur!

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.

Table of Contents

  1. Introduction
  2. Notebook Categories
  3. How to Use
  4. How to Contribute
  5. Connect with Us

Introduction

"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.

Notebook Categories

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

How to Use

  1. Clone the Repository:

    git clone https://github.com/ML-Nagpur/Cool-Notebooks.git
  2. 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
  3. Explore Notebooks: Navigate to the Notebooks directory and explore the available Jupyter notebooks. Each notebook covers a specific machine learning topic, and you can find detailed explanations, code snippets, and more.

  4. Run the Notebooks: Open Jupyter Notebook in your preferred environment and run the notebooks. Experiment with the code, modify parameters, and observe the results.

How to Contribute

Connect with Us

Join our vibrant community on Discord for discussions, questions, and updates. Connect with fellow learners and practitioners to enhance your ML journey.

Directory Tree

.
├── CODE_OF_CONDUCT.md
├── CONTRIBUTING.md
├── Computer Vision
│   ├── Face_Detection
│   │   ├── Face_Detection.py
│   │   └── requirements.txt
│   └── Hand_Tracking
│       ├── Hand_Tracking.py
│       └── requirements.txt
├── Data Visualization
│   └── Visualization Tutorial.ipynb
├── 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
│   ├── Fine Tuning LLMA in Colab
│   │   └── Fine_tune_Llama_2_in_Google_Colab.ipynb
│   ├── GPT2 based Text Generation
│   │   ├── Transformers_(GPT_2)_for_Text_Generation.ipynb
│   │   └── transformers_(gpt_2)_for_text_generation.py
│   ├── ImageToTextGenerator
│   │   ├── ImageToTextGenerator.ipynb
│   │   └── imagetotextgenerator.py
│   └── Langchain based Chat Retrival Chatbot
│       ├── Mumbai1.csv
│       ├── app.py
│       ├── cached_data.json
│       └── requirements.txt
├── 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

68 directories, 113 files