machine_learning_basics
# Machine learning basics This repository contains implementations of basic machine learning algorithms in plain Python (Python Version 3.6+). All algorithms are implemented from scratch without using additional machine learning libraries. The intention of these notebooks is to provide a basic understanding of the algorithms and their underlying structure, *not* to provide the most efficient implementations. - [Bayesian Linear Regression](bayesian_linear_regression.ipynb) - [Decision tree for classification](decision_tree_classification.ipynb) - [Decision tree for regression](decision_tree_regression.ipynb) - [k-nearest-neighbor](k_nearest_neighbour.ipynb) - [k-Means clustering](kmeans.ipynb) - [Linear Regression](linear_regression.ipynb) - [Logistic Regression](logistic_regression.ipynb) - [Multinomial Logistic Regression](softmax_regression.ipynb) - [Perceptron](perceptron.ipynb) - [Principal Component Analysis]([principal_component_analysis.ipynb) - [Simple neural network with one hidden layer](simple_neural_net.ipynb) - [Softmax regression](softmax_regression.ipynb) - [Support vector machines](support_vector_machines.ipynb)  ## Data preprocessing After several requests I started preparing notebooks on how to preprocess datasets for machine learning. Within the next months I will add one notebook for each kind of dataset (text, images, ...). As before, the intention of these notebooks is to provide a basic understanding of the preprocessing steps, *not* to provide the most efficient implementations. - [Image preprocessing](image_preprocessing.ipynb) - [Preprocessing a numerical/categorical dataset](data_preprocessing.ipynb)  ## Live demo Run the notebooks online without having to clone the repository or install jupyter: [](https://mybinder.org/v2/gh/zotroneneis/machine_learning_basics/HEAD). Note: this does not work for the `data_preprocessing.ipynb` and `image_preprocessing.ipynb` notebooks because they require downloading a dataset first. ## Feedback If you have a favorite algorithm that should be included or spot a mistake in one of the notebooks, please let me know by creating a new issue. ## License See the LICENSE file for license rights and limitations (MIT).