A notebook about commonly used machine learning algorithms.

A series of Jupyter notebooks, to know about Machine Learning, its implementation, and identifying its best practices.

Notebooks completed to learn various Deep Learning topics during Inspirit AI's Deep Dives: Designing Deep Learning Systems program(500+ lines)

This notebook consists of the notebook file that consists of a supervised learning model built to classify the nature of the breast cancer cells based on the features.

This notebook demonstrates timeseries classification for crop identification on a subset of the MiniTimeMatch dataset by training an LSTM model.

This repository contains a Jupyter Notebook demonstrating a practical example of data science and machine learning for heart disease classification.

Notebooks in this repository focus on code related to machine learning topics

The notebook shows how deep learning tools (TensorFlow/Keras and PyTorch ) work in practice.

This Python notebook demonstrates the application of Support Vector Machines (SVM) for classification tasks on the MNIST dataset. The notebook covers data preprocessing, hyperparameter tuning, and dimensionality reduction using PCA.

This repo consists of notebooks I created to work upon the skills I learnt through several courses. All notebook has EDA, Model Building, Hyperparameter Tuning, Ensemble Models and Sampling Techniques.

This Jupyter notebook demonstrates tuning hyperparameters of machine learning models with total profit as a scoring metric to gain maximum total profit.

This repository consists the Jupyter Notebook files containing code of Artificial Neural Network with different tuning parameters for a similar scenario.

Bayesian Optimization for hyperparameter tuning in machine learning using a Jupyter Notebook. This repository demonstrates optimizing a Gradient Boosting Classifier with practical examples and clear explanations.

"oxayavongsa/projects" is a public GitHub repository serving as a diverse AI/ML Project Portfolio. Using Python coding and Juptyer notebook for multiple methodologies to model statistical algorithms.

This repository houses a diverse collection of projects developed using Jupyter Notebooks, focusing on testing various machine learning pipelines, neural network models, and statistical machine learning approaches. Through exploration of different datasets, the projects delve into predictive modeling, classification tasks, and in-depth analyses.

Interactive exploration of hyperparameter tuning results with ipywidget and plotly in jupyter notebook.

This repository contains code and associated files for deploying ML models using AWS SageMaker. This repository consists of a number of tutorial notebooks for various coding exercises, mini-projects, and project files that will be used to supplement the lessons of the Nanodegree.

The jupyter notebooks of the deep learning specialization by deeplearning.ai

5 courses of Specialization in Deep Learning taught by Prof.Andrew Ng on Coursera

This repository consists of a Jupiter notebook showing the experiments conducted to create an RNN LSTM Model. Also, it shows the prediction done on the collected data from Twitter.