scikit-learn-python
There are 102 repositories under scikit-learn-python topic.
microsoft/ML-For-Beginners
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
dataprofessor/code
Compilation of R and Python programming codes on the Data Professor YouTube channel.
mikekeith52/scalecast
The practitioner's forecasting library
SuperKogito/Voice-based-gender-recognition
:sound: :boy: :girl:Voice based gender recognition using Mel-frequency cepstrum coefficients (MFCC) and Gaussian mixture models (GMM)
flo7up/relataly-public-python-tutorials
Beginner-friendly collection of Python notebooks for various use cases of machine learning, deep learning, and analytics. For each notebook there is a separate tutorial on the relataly.com blog.
dimitrismistriotis/alt-profanity-check
A fast, robust library to check for offensive language in strings, dropdown replacement of "profanity-check".
reddyprasade/Machine-Learning-with-Scikit-Learn-Python-3.x
In general, a learning problem considers a set of n samples of data and then tries to predict properties of unknown data. If each sample is more than a single number and, for instance, a multi-dimensional entry (aka multivariate data), it is said to have several attributes or features. Learning problems fall into a few categories: supervised learning, in which the data comes with additional attributes that we want to predict (Click here to go to the scikit-learn supervised learning page).This problem can be either: classification: samples belong to two or more classes and we want to learn from already labeled data how to predict the class of unlabeled data. An example of a classification problem would be handwritten digit recognition, in which the aim is to assign each input vector to one of a finite number of discrete categories. Another way to think of classification is as a discrete (as opposed to continuous) form of supervised learning where one has a limited number of categories and for each of the n samples provided, one is to try to label them with the correct category or class. regression: if the desired output consists of one or more continuous variables, then the task is called regression. An example of a regression problem would be the prediction of the length of a salmon as a function of its age and weight. unsupervised learning, in which the training data consists of a set of input vectors x without any corresponding target values. The goal in such problems may be to discover groups of similar examples within the data, where it is called clustering, or to determine the distribution of data within the input space, known as density estimation, or to project the data from a high-dimensional space down to two or three dimensions for the purpose of visualization (Click here to go to the Scikit-Learn unsupervised learning page).
SuperKogito/Voice-based-speaker-identification
:sound: :boy: :girl: :woman: :man: Speaker identification using voice MFCCs and GMM
BaseMax/ImageRecognitionAI
Recognition of the images with artificial intelligence includes train and tests based on Python.
niitsuma/delayedsparse
Efficient sparse matrix implementation for various "Principal Component Analysis"
Lawrence-Krukrubo/Machine_Learning
Machine learning is the sub-field of Computer Science, that gives Computers the ability to learn without being explicitly programmed (Arthur samuel, American pioneer in the field of Computer gaming and AI , coined the term Machine Learning in 1959, while at IBM )
UtsavMurarka/MXene-machine-learning
Classification of MXenes into metals and non-metals based on physical properties
rifatSDAS/satellite_machine_learning
Unsupervised and supervised learning for satellite image classification
chandakakshat/Digital-Kisaan-Portal
A web application designed to support farmer-community with Intelligent Machine Learning technologies, providing live crop recommendation and prediction system, facilitating farmers with online community support and chat bot based on Artificial Intelligence. It also Integrates an on-demand news feed page aiding for socializing within the farmer community.
dpscience/DMLLTDetectorPulseDiscriminator
DMLLTDetectorPulseDiscriminator - A supervised machine learning approach for shape-sensitive detector pulse discrimination in lifetime spectroscopy applications
souvikmajumder26/IEEE-ECG-Ensemble-XGBoost
👨💻 Developed AI Models - Ensemble of Random Forest & SVM and XGBoost classifiers to classify five types of Arrhythmic Heartbeats from ECG signals - published by IEEE.
tanvirnwu/ML-Algorithms--Scikit-learn--Python
This folder contains the basic algorithms of ML implemented with Python.
AjayZinngg/simple-nltk-chatbot
A Q&A based chatbot which queries the database to find responses for similar questions asked by the users
AMPA-ML-Team/PD-Classification
Codes for "Parkinson’s Disease Diagnosis: Effect of Autoencoders to Extract Features from Vocal Characteristics"
AshishPandey88/Nanda-Devi-Glacier-Loss
Feburary 7,2021 Ecological Disaster (Nanda Devi Glacier, IND: 7,108 m above sea level). Satellite image analysis using the methodology of image segmentation shows that the Glacier cover in Nanda Devi has substantially decreased over the last 4 decades. It has gone down from 43% in Year 1984 to 20% in Year 2022 (in relation to the captured area in image)
dblabs-mcgill-mila/machine_learning_with_sklearn
Explore and understand the Machine Learning concepts through the prism of sklearn, one notebook at a time.
Go-MinSeong/Predicting-whether-your-mail-will-be-read
predicting whether you read mail
RafeyIqbalRahman/Data-Imputation-Techniques
This repository demonstrates data imputation using Scikit-Learn's SimpleImputer, KNNImputer, and IterativeImputer.
thamizhannal/INSOFE
Works done at International School of Engineering
AbhishekRS4/ML_water_potability
ML model deployment using docker, kubernetes; API deployment with FastAPI; and MLOps using MLFlow for water potability dataset
akimcs/fish-weight-estimation
a python project that uses machine learning to estimate the weight of a fish.
AM-mirzanejad/Heart-Failure-Prediction
The Heart Disease Predictor is a Python project developed to classify whether an individual has heart disease based on specific input parameters. It utilizes the scikit-learn and NumPy libraries for implementation.
Aroojzahra908/Artifical-Intelligence-and-Machine-Learning-
Explore the basics of linear regression, gradient descent, and AI using Python. Get hands-on with NumPy, pandas, Matplotlib, and scikit-learn for practical learning.
benjaminweymouth/machine_learning_Risky_Business
Machine Learning Examples: this repo is to show proficiency in building and evaluating several machine learning models to predict credit risk
DNAMAY/ML-Diabetes
Early stage detection of Diabetes risk
isaccanedo/Machine-Learning-For-Beginners
:star2: 12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
Jeyjocar/Redes-Neuronales
24/01/2024 Jeyfrey J. Calero R. Aplicación de Redes Neuronales con scikit-learn streamlit, pandas, seaborn y matplolib
nafisalawalidris/Machine-Learning-with-Python
Machine Learning with Python final project: Apply ML algorithms to solve real-world problem. Hands-on experience in data preprocessing, model selection, evaluation. Showcase ML proficiency in Python.
NajiAboo/TextClassification
Build custom vacab, Ham /Spam using tfidf , Movie review classification using TFIDF
Shakthi011001/Sentimental-Analysis-using-Scikit-Learn
Sentiment analysis refers to analyzing an opinion or feelings about something using data like text or images, regarding almost anything. Sentiment analysis helps companies in their decision-making process. For instance, if public sentiment towards a product is not so good, a company may try to modify the product or stop the production. Although there are several known tasks related to sentiment analysis, in this project we will focus on the common binary problem of identifying the positive / negative sentiment that is expressed by a given text toward a specific topic
SubhangiSati/Machine-Learning
This consists of various machine learning algorithms like Linear regression, logistic regression, SVM, Decision tree, kNN etc. This will provide you basic knowledge of Machine learning algorithms using python. You'll learn PyTorch, pandas, numpy, matplotlib, seaborn, and various libraries.