Python Scripts and Jupyter Notebooks
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Updated
Apr 17, 2024 - Jupyter Notebook
Python Scripts and Jupyter Notebooks
This contains the Jupyter Notebook and the Dataset for the mentioned Classification Predictive Modeling Project
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.
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.
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.
The jupyter notebooks of the deep learning specialization by deeplearning.ai
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.
The notebook shows how deep learning tools (TensorFlow/Keras and PyTorch ) work in practice.
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 notebook demonstrates timeseries classification for crop identification on a subset of the MiniTimeMatch dataset by training an LSTM model.
This repository consists the Jupyter Notebook files containing code of Artificial Neural Network with different tuning parameters for a similar scenario.
This Jupyter notebook demonstrates tuning hyperparameters of machine learning models with total profit as a scoring metric to gain maximum total profit.
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.
5 courses of Specialization in Deep Learning taught by Prof.Andrew Ng on Coursera
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