⚡机器学习实战（Python3）：kNN、决策树、贝叶斯、逻辑回归、SVM、线性回归、树回归

Objective of the repository is to learn and build machine learning models using Pytorch. 30DaysofML Using Pytorch

Image classification using SVM, KNN, Bayes, Adaboost, Random Forest and CNN.Extracting features and reducting feature dimension using T-SNE, PCA, LDA.

In This Repository you can find The Explanation and The Implementation of the Most Famous Machine Learning Algorithms

Dtreehub is a lightweight decision tree framework for Python with categorical feature support. It covers regular decision tree algorithms: ID3, C4.5, CART, CHAID and regression tree, random forest and adaboost.

Built 5 ML models to identify customers likely to purchase loans in Makati, Philippines for Real Estate Purposes

Implementation of various machine learning algorithms from scratch.

Perceptron Implementation - implementing Empirical Risk Minimization (ERM) and k-folds cross-validation

Solution to the (MBTI) Myers-Briggs Personality Type Dataset on Kaggle

Decision tree and Ada-boosted stumps based algorithm to tag sentences with their language.

Using Various Regression Algorithms to Predict House Sales

Implementation of common ML Algorithms from scratch in Python3

Software prediction has been used. Where the machine learning and the deep learning algorithms have been compared.

Using Decision Tree and AdaBoost to classify languages(English/Dutch)

Logistic Regression and AdaBoost for Classification