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Machine Learning & Data Science

Series of notebooks that walks through the fundamentals of machine learning and data science.

Important Algorithms for a beginner to learn and implement Machine Learning:

Linear Regression Logistic Regression K-Nearest Neighbours K-Means Clustering Naive Bayes SVMs Decision Trees Random Forest Dimensionality Reduction Algorithms Gradient Boosting algorithms- XGBoost, GBM, LightGBM, CatBoost

Topics Covered:

  • naive-bayes
  • Basic-Computer Vision
  • Basic_ANN_using_Keras_and_Tensorflow
  • Breast_Cancer_Prediction
  • Callbacks
  • Classification model
  • Credit Card Fraud Detection
  • Detecting_Masks
  • Diabetes_dataset_with_Neural_Networks_and_validation_split
  • Fashion_MNIST_dataset_with_Neural_Networks
  • Heart_disease_prediction
  • Linear regression
  • Logistic Regression
  • Mask Detection
  • OpenCV_Webcam
  • Polynomial Regression
  • RandomForestClassifier
  • Sentiment_analysis
  • Stock Price Prediction
  • Summarizer
  • Training_the_CNN
  • Vanilla-Autoencoder
  • spam identifier
  • topicExtraction
  • SVM Classifier
  • MLP Classifier (Multi-Layer Perceptron)

Issue

Ensure the bug was not already reported by searching on GitHub under issues. If you're unable to find an open issue addressing the bug, open a new issue.

Write detailed information. Detailed information is very helpful to understand an issue.

Pull Requests

Pull Requests are always welcome.

License

MIT © SRM-ACM-Women

This project is licensed under the MIT License License