applied_machine_learning_python

Python scripts for machine learning algorithms (supervised and unsupervised). Mostly applications based on sklearn toolbox.

Most of these scripts are based on the 'Applied Machine Learning in Python' course in Coursera.

Supervised learning

• Linear regression: https://github.com/alzaia/applied_machine_learning_python/tree/master/linear_regression
• Logistic regression (classification): https://github.com/alzaia/applied_machine_learning_python/tree/master/logistic_regression
• SVMs (Support Vector Machines): https://github.com/alzaia/applied_machine_learning_python/tree/master/svm
• K-nearest-neighbours: https://github.com/alzaia/applied_machine_learning_python/tree/master/knn
• Decision trees: https://github.com/alzaia/applied_machine_learning_python/tree/master/decision_trees
• Random forests: https://github.com/alzaia/applied_machine_learning_python/tree/master/random_forests
• Gradient boosting techniques: https://github.com/alzaia/applied_machine_learning_python/tree/master/gradient_boosting
• Naive Bayes: https://github.com/alzaia/applied_machine_learning_python/tree/master/naive_bayes
• Multilayer perceptrons (neural nets): https://github.com/alzaia/applied_machine_learning_python/tree/master/neural_nets

Unsupervised learning

• K-means clustering: https://github.com/alzaia/applied_machine_learning_python/tree/master/kmeans
• PCA (principal component analysis): https://github.com/alzaia/applied_machine_learning_python/tree/master/pca

Other

• Evaluation metrics when doing machine learning: https://github.com/alzaia/applied_machine_learning_python/tree/master/evaluation_metrics