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
- 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)
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 are always welcome.
MIT © SRM-ACM-Women
This project is licensed under the MIT License
