Machine Learning Algorithms implemented using Numpy and Scipy
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Updated
Feb 6, 2020 - Python
Machine Learning Algorithms implemented using Numpy and Scipy
The objective of this repository is to provide a learning and experimentation environment to better understand the details and fundamental concepts of neural networks by building neural networks from scratch.
Analysis of the Restaurant reviews by using the Naive Bayes & the Random Forests Algorithms
Numerical Methods: "Handwritten Digit Recognition" Group Project - 2nd Semester 2021 - Computer Science, UBA
Insights into how the error varies with change in K while performing k-fold Cross Validation
Experimenting ML Algorithms, feature selection, cross validation and feature transformation on self-annotated custom Eskişehir real estate dataset. - 2021 - Yildiz Technical University
Predicts gender of an author based on syntactic constructions of their tweets
Implementation of Naive Bayes, Gaussian Naive Bayes, and 5-fold cross-validation Nearest neighbor with pure python
This repository consists of 6 sections, detailing hands on Machine Learning Models: Regression, Classification, Clustering, AssocaitionRuleLearning, Deep Learning and Natural Language Processing Techniques
Data sampling library
Machine Learning Task implemented in PySpark to parallelise K-Fold Cross Validation
Code for Intro to ML Midterm 2
Does Work-Life Balance Matter? (DSI Capstone I Project)
Progetto di Intelligenza artificiale sugli alberi di decisione con valori mancanti.
Used Python Scikit-Learn to analyze Austin car crash data from 2018 to 2020 and created an interactive dashboard using a Random Forest Classifier algorithm to calculate a driver score from user features.
My very first hands on experiment with CV
GroupSplit is a module to help split datasets into train and test sets for data science and machine learning projects.
Image classification with DeiT model, including data preprocessing, k-fold CV, early stopping and model saving.
These are all of my machine learning codes.You can find every code about machine learning.
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