Python Scripts and Jupyter Notebooks

This contains the Jupyter Notebook and the Dataset for the mentioned Classification Predictive Modeling Project

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

A notebook about commonly used machine learning algorithms.

In this Amazon SageMaker tutorial, you'll find labs for setting up a notebook instance, feature engineering with XGBoost, regression modeling, hyperparameter tuning, bring your custom model etc.

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

An easy way to enhance DeepRacer model training using DRfC functionalities through Jupyter Notebooks. Reproducing some core functionalities provided by AWS SageMaker Notebook

Jupyter notebooks on machine learning fundamentals, including classification and regression methods with practical implementations. Created as part of the Artificial Intelligence 2024 course at UIB.

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 programming assignments for the Deep Learning Specialization by deeplearning.AI. It includes Jupyter Notebooks for exercises in neural networks, hyperparameter tuning, convolutional networks, and sequence models.

A well-organized collection of Jupyter notebooks covering the full machine learning journey—from data preprocessing and classic algorithms to deep learning, NLP, and reinforcement learning. Ideal for learners and professionals to explore, experiment, and master ML with real code.

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

A comprehensive analysis of the Fashion MNIST dataset using PyTorch. Covers data preparation, EDA, baseline modeling, and fine-tuning CNNs like ResNet. Includes modular folders for data, notebooks, and results. Features CSV exports, visualizations, metrics comparison, and a requirements.txt for easy setup. Ideal for ML workflow exploration.

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.

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

This notebook can be used to quickly revise the KNN algorithm.

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

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.