Machine and Deep Learning models from scratch

This repository is for machine and deep learning models created by hand, i.e. without specific libraries.

Machine Learning models:

1. Linear Regression (one variable + multiple variables cases)
2. Logistic Regression (logistic regression + feature mapping + regularization)
3. K-Means (clustering with sklearn.datasets.make_blobs + elbow method)
4. Naive Bayes classifier
5. PCA
6. Tree
7. Random Forest

Deep Learning models:

1. Simple Neural Network

Other models:

1. Word2vec (word2vec based on Skip-Gram. Only theory by now) - NLP

Sources:

• Machine Learning course by Stanford University on Coursera (files with data are taken from here as well)
• Machine Learning and Data analysis Specialization by MIPT and Yandex on Coursera
• NLP Stanford Course - cs224