Example codes and experiments around machine learning algorithms (by language) that are widely used within the data science community.

Python:

• Genetic Algorithm implementation in Python (Solving Mathematical Equality Problem)
• PySpark Classification implementation in Python [using PipeLine] (RandomForestClassifier)
• Scipy Beta distribution implementation in Python
• Sklearn Classification implementation in Python (classifiers comparison)
• Sklearn Gaussian Mixture Model implementation in Python (new sample generator from the same distribution)
• Sklearn Kernel Density Estimation implementation in Python (new sample generator from the same distribution)
• Sklearn Regression implementation in Python [using PipeLine] (LinearRegression vs RandomForestRegressor)
• TensorFlow Classification implementation in Python [using TensorFlow v1.x] (classifiers comparison)
• TensorFlow DNN Classification implementation in Python [using TensorFlow v1.x] (Deep Neural Network)
• TensorFlow DNN Regression implementation in Python [using TensorFlow v1.x] (Deep Neural Network)
• TensorFlow Generative Adversarial Network for New Sample Generating implementation in Python [using TensorFlow 1.x]
• TensorFlow Linear Classification implementation in Python [using TensorFlow v1.x]
• TensorFlow MLP Classification implementation in Python [using TensorFlow v1.x] (Deep Neural Network)
• TensorFlow MNIST Classification implementation in Python [using TensorFlow v1.x] (Deep Learning)
• TensorFlow.Keras DNN Binary Classification implementation in Python [using TensorFlow v2.x]
• TensorFlow.Keras DNN Binary Classification Imbalanced Data implementation in Python [using TensorFlow v2.x]
• TensorFlow.Keras DNN Regression implementation in Python [using TensorFlow v2.x]
• To be continued...

R:

• Kmeans Clustering implementation in R
• To be continued...