Implementation of a series of Neural Network architectures in TensorFow 2.0

Bird Classifier developped in tensorflow using pre-trained model from Tensorflow Hub and running on Google Colab

Colab Compatible FastAI notebooks for NLP and Computer Vision Datasets

A classification approach to the machine learning Titanic survival challenge on Kaggle.Data visualisation, data preprocessing and different algorithms are tested and explained in form of Jupyter Notebooks

A series of documented Jupyter notebooks implementing SVM and SVC's.

Interactive Jupyter notebooks running Brainome to measure your data and create ML classifiers.

This repository contains a compilation of my Python projects and notebooks focusing on Machine Learning and Deep Learning.

Classifier that identifies Greek text as Cypriot Greek or Standard Modern Greek

A click bait classifier notebook developed using LSTM. The notebook showcases the analysis on Click bait heading data and a neural network to classify Heading as click bait. The model accuracy is 96%+.

Data tools in Jupyter notebooks served from a container. Includes examples of cleaning, classification, clustering, graph drawing, and principal component analysis.

A collection of classification algorithms for different purposes

Mon Notebook sur les fondamentaux du Machine Learning

Jupyter notebooks of SAKI ML course at FAU (for students)

I have created a classifier to classify website, whether it is benign or Malicious with using the CRISP DM concepts in it. Additionally I wrote blog on it https://medium.com/@prashantjadiya/process-of-classifying-malicious-and-benign-websites-815cc2b42435.

A handwritten digits classifier of the MNIST dataset using PyTorch neural networks and Jupyter Notebook.

My attempt at the introduction to machine learning Kaggle competition: "Titanic: Machine Learning from Disaster"

A Bachelor's Thesis project analyzing and comparing classifiers for breast cancer detection using fine needle aspiration biopsies. Includes Jupyter Notebooks for model training and evaluation, and a LaTeX document detailing the methodology and results. Features SHAP for explainable AI analysis.

This is IPython notebook in which i have used neural network to classify the mnist fashion dataset into 10 different categories based on their features

MLP classification model