#Logistic-regression
Showing 60 of 85 repositories tagged #logistic-regression, ranked by stars
100 Days of ML Coding
The "Python Machine Learning (1st edition)" book code repository and info resource
Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG
It is my belief that you, the postgraduate students and job-seekers for whom the book is primarily meant will benefit from reading it; however, it is my hope that even the most experienced researchers will find it fascinating as well.
Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.
Plain python implementations of basic machine learning algorithms
Text Classification Algorithms: A Survey
A curated list of data mining papers about fraud detection.
General Assembly's 2015 Data Science course in Washington, DC
Python programming assignments for Machine Learning by Prof. Andrew Ng in Coursera
This repository contains my full work and notes on Coursera's NLP Specialization (Natural Language Processing) taught by the instructor Younes Bensouda Mourri and Łukasz Kaiser offered by deeplearning.ai
Fast Best-Subset Selection Library
Pytorch、Scikit-learn实现多种分类方法，包括逻辑回归（Logistic Regression）、多层感知机（MLP）、支持向量机（SVM）、K近邻（KNN）、CNN、RNN，极简代码适合新手小白入门，附英文实验报告（ACM模板）
The full collection of Jupyter Notebook labs from Andrew Ng's Machine Learning Specialization.
Decision Trees, Random Forest, Dynamic Time Warping, Naive Bayes, KNN, Linear Regression, Logistic Regression, Mixture Of Gaussian, Neural Network, PCA, SVD, Gaussian Naive Bayes, Fitting Data to Gaussian, K-Means
Tool that predicts the outcome of a Dota 2 game using Machine Learning
Gender recognition by voice and speech analysis
Simple machine learning library / 簡單易用的機器學習套件
A New, Interactive Approach to Learning Python
Estudo e implementação dos principais algoritmos de Machine Learning em Jupyter Notebooks.
Collection of stats, modeling, and data science tools in Python and R.
Network Intrusion Detection based on various machine learning and deep learning algorithms using UNSW-NB15 Dataset
텐서플로우와 머신러닝으로 시작하는 자연어처리(로지스틱회귀부터 트랜스포머 챗봇까지)
Machine learning algorithms in Dart programming language
Data Science & Machine Learning projects and tutorials in python from beginner to advanced level.
This repository contains implementations of all Machine Learning Algorithms from scratch in Python. Mathematics required for ML and many projects have also been included.
Anomaly based Malware Detection using Machine Learning (PE and URL)
Projects I completed as a part of Great Learning's PGP - Artificial Intelligence and Machine Learning
This repository explores the variety of techniques and algorithms commonly used in deep learning and the implementation in MATLAB and PYTHON
A professional TF-IDF + Logistic Regression style-risk classifier for educational fake-news detection, with a Streamlit dashboard, honest evaluation, uncertainty handling, and leakage analysis.
Source files for "Fun Q: A Functional Introduction to Machine Learning in Q"
Predicts Daily NBA Games Using a Logistic Regression Model
Forecast stock prices using machine learning approach. A time series analysis. Employ the Use of Predictive Modeling in Machine Learning to Forecast Stock Return. Approach Used by Hedge Funds to Select Tradeable Stocks
Loan Application Data Analysis
Football Match prediction using machine learning algorithms in jupyter notebook
A repository for recording the machine learning code
Includes top ten must know machine learning methods with R.
A New Interactive Approach to Learning Data Analysis
Collection of various implementations and Codes in Machine Learning, Deep Learning and Computer Vision ✨💥
By-hand code for models and algorithms. An update to the 'Miscellaneous-R-Code' repo.
My solution to TUM's Machine Learning MNIST challenge 2016-2017 [winner]
Data Science, Machine Learning, Deep Learning, NLP, Python & Library's cheat Sheet - Interview Questions & Notes
Pipeline for fast building text classification TF-IDF + LogReg baselines.
I will update this repository to learn Machine learning with python with statistics content and materials
This project employs emotion detection in textual data, specifically trained on Twitter data comprising tweets labeled with corresponding emotions. It seamlessly takes text inputs and provides the most fitting emotion assigned to it.
🤖 Python implementations of some of the fundamental Machine Learning models and algorithms from scratch with interactive Jupyter demos and math being explained.
An introductory statistics course for social scientists, using Stata
Machine Learning model(specifically log-regression with stochastic gradient descent) for tennis matches prediction. Achieves accuracy of 66% on approx. 125000 matches
Course Material for Artificial Intelligence and Machine Learning - Unit 2 @ Computer Science Dept, Sapienza
Contains Optional Labs and Solutions of Programming Assignment for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2023) by Prof. Andrew NG
implement the machine learning algorithms by python for studying
📊 30 Days of Data Science is a daily challenge to guide you through Data Science essentials. From basics to advanced, this repo offers clear examples, practical exercises, and resources to help you master Data Science, one day at a time. Whether you're new or refining your skills, this challenge has something for you. Join the journey now! 🚀
iris数据集的基本数据分析方法，包括KNN,LG,NB,SVM算法。
Forecasting weather Using Multinomial Logistic Regression, Decision Tree, Naïve Bayes Multinomial, and Support Vector Machine
Interpretable and efficient predictors using pre-trained language models. Scikit-learn compatible.
TensorFlow-based implementation of (Gaussian) Mixture Model and some other examples.
This project aims to predict the type 2 diabetes, based on the dataset. It uses machine learning model,which is trained to predict the diabetes mellitus before it hits.
Alzheimer Disease Detection Model for Real Time Hospital Usage
Signal Processing Toolkit, including ML models with visualization