├── .github ├── FUNDING.yml ├── ISSUE_TEMPLATE │ └── bug_report.md └── workflows │ └── stale.yml ├── .gitignore ├── A guide to Ensemble Learning ├── A guide to Ensemble Learning.ipynb ├── AveragingModels.py ├── BaggingModels.py ├── BlendingMondels.py ├── MajorityVote.py ├── StackingModels.py ├── StackingModelsRetrained.py ├── WeightedAveragedModels.py └── requirements.txt ├── Deploying your ML Model ├── README.md ├── flask_hello_world.py ├── make_request.py ├── requirements.txt ├── rest_api_example.py ├── templates │ └── index.html └── website_example.py ├── Discord Sentiment Analysis Bot ├── discord_bot_example.py ├── requirements.txt ├── sentiment_bot.py ├── vader_example_1.py ├── vader_example_2.py └── vader_example_3.py ├── FastAI ├── Multi-label prediction with Planet Amazon dataset.ipynb ├── Animal detector from Google images.ipynb ├── Book Recommendation System.ipynb ├── Image segmentation on CamVid dataset.ipynb ├── README.md ├── Twitter US Airline Sentiment.ipynb └── requirements.txt ├── Introduction to Data Visualization in Python └── Introduction to Data Visualization in Python.ipynb ├── Introduction to Deep Learning with Keras └── Introduction to Deep Learning with Keras.ipynb ├── Introduction to Machine Learning in C# with ML.NET └── CreditCardFraudDetection │ ├── .vs │ └── CreditCardFraudDetection │ │ ├── DesignTimeBuild │ │ └── .dtbcache │ │ └── v16 │ │ ├── .suo │ │ └── Server │ │ └── sqlite3 │ │ ├── db.lock │ │ ├── storage.ide │ │ ├── storage.ide-shm │ │ └── storage.ide-wal │ ├── CreditCardFraudDetection.sln │ └── CreditCardFraudDetection │ ├── CreditCardFraudDetection.csproj │ ├── DataModels │ ├── ModelInput.cs │ └── ModelOutput.cs │ ├── ModelBuilder.cs │ ├── Program.cs │ ├── assets │ └── model.zip │ ├── bin │ └── Debug │ │ └── netcoreapp2.2 │ │ ├── CreditCardFraudDetection.deps.json │ │ ├── CreditCardFraudDetection.dll │ │ ├── CreditCardFraudDetection.pdb │ │ ├── CreditCardFraudDetection.runtimeconfig.dev.json │ │ └── CreditCardFraudDetection.runtimeconfig.json │ └── obj │ ├── CreditCardFraudDetection.csproj.nuget.cache │ ├── CreditCardFraudDetection.csproj.nuget.dgspec.json │ ├── CreditCardFraudDetection.csproj.nuget.g.props │ ├── CreditCardFraudDetection.csproj.nuget.g.targets │ ├── Debug │ └── netcoreapp2.2 │ │ ├── CreditCardFraudDetection.AssemblyInfo.cs │ │ ├── CreditCardFraudDetection.AssemblyInfoInputs.cache │ │ ├── CreditCardFraudDetection.assets.cache │ │ ├── CreditCardFraudDetection.csproj.CoreCompileInputs.cache │ │ ├── CreditCardFraudDetection.csproj.FileListAbsolute.txt │ │ ├── CreditCardFraudDetection.csprojAssemblyReference.cache │ │ ├── CreditCardFraudDetection.dll │ │ └── CreditCardFraudDetection.pdb │ └── project.assets.json ├── Introduction to Web Scraping with BeautifulSoup └── WebScraping_Introduction.ipynb ├── Keras-Tutorials ├── 1-3. CNN MNIST │ ├── README.md │ ├── classify.png │ ├── keras_cnn.py │ ├── mnist_model.h5 │ ├── model.json │ └── paintApp.py ├── 4. LSTM Text Generation │ ├── Keras LSTM Text Generation.ipynb │ ├── README.md │ ├── sherlock_homes.txt │ └── weights.hdf5 ├── 5. Stock Price Prediction using a Recurrent Neural Network │ ├── AAPL_data.csv │ ├── README.md │ ├── Stock Price Prediction.ipynb │ └── stock_prediction.h5 ├── 6. Sentiment Analysis │ ├── README.md │ ├── Sentiment Analysis.ipynb │ ├── Tweets.csv │ └── sentiment_analysis.h5 ├── 7. Autoencoder │ ├── Autoencoder.ipynb │ └── README.md ├── 8. Hyperparameter Tuning using the Scikit Learn Wrapper │ ├── Keras #8 - Keras Hyperparameter Tuning using Scikit Learn Wrapper.ipynb │ └── README.md ├── 9. Using pretrained models │ ├── Keras #9 Using the pretrained Models.ipynb │ └── elephant.jpg ├── README.md └── requirements.txt ├── LICENSE ├── README.md ├── Recommendation System ├── Recommendation System.ipynb ├── book_tags.csv ├── books.csv ├── ratings.csv ├── regression_model.h5 ├── regression_model2.h5 ├── sample_book.xml ├── tags.csv └── to_read.csv ├── Reddit Webscraping using PRAW ├── Reddit API.ipynb └── top_ml_subreddit_posts.csv ├── Scikit-Learn-Tutorial ├── 1. Introduction.ipynb ├── 10. Cross Validation and Model Selection.ipynb ├── 11. Hyperparameter Tuning.ipynb ├── 12. Preprocessing.ipynb ├── 13. Feature extraction.ipynb ├── 14. Machine Learning Process.ipynb ├── 2. Setup and Benefits of Scikit Learn.ipynb ├── 3. Training a model on the Iris Dataset.ipynb ├── 4. Training, Testing Datasets.ipynb ├── 5. Classification Algorithms.ipynb ├── 6. Regression Algorithms.ipynb ├── 7. Clustering Algorithms.ipynb ├── 8. Dimensionality Reduction.ipynb ├── 9. Dimensionality Reduction 2.ipynb └── README.md ├── Streamlit ├── Deploy-Application-with-Heroku │ ├── Procfile │ ├── README.md │ ├── app.py │ ├── requirements.txt │ └── setup.sh ├── Examples │ └── Stock-Price-Dashboard │ │ ├── AAPL_data.csv │ │ ├── app.py │ │ └── requirements.txt ├── README.md └── doc │ ├── deployment_example_application.png │ ├── stock-price-dashboard.PNG │ └── streamlit_example.gif ├── Tensorflow Object Detection ├── README.md ├── detect_object_in_webcam_video.ipynb ├── object_detection_tutorial.ipynb ├── object_detection_with_own_model.ipynb ├── requirements.txt ├── surveillance.py └── surveillance_with_imutils.py ├── Uber Ludwig Examples ├── Amazonas Satellit Dataset │ └── model_definition.yaml ├── French-English Bilingual Pairs │ ├── model_definition.yaml │ └── txt_to_csv.py ├── Heart Disease Dataset │ ├── heart.csv │ └── model_definition.yaml ├── Malaria Cell Images Dataset │ ├── create_csv.py │ ├── model_definition_custom.yaml │ └── model_definition_simple.yaml 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