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This project is a Python machine learning library and data science toolkit designed for building predictive models and analyzing complex datasets. It provides a collection of implementations for common supervised and unsupervised algorithms using the Scikit-Learn framework.
The main features of susanli2016/machine-learning-with-python are: Python Machine Learning Libraries, Scikit-Learn Implementations, Clustering Algorithms, Predictive Machine Learning Analytics, Predictive Model Development, Predictive Modeling, Data Science Toolkits, Tabular Data Manipulations.
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cuml is a GPU-accelerated machine learning library and framework that uses CUDA to accelerate tabular data preprocessing and model execution. It provides a suite of tools for training and deploying classification, regression, and clustering models on NVIDIA GPUs and GPU clusters. The library is designed for scalability, offering a distributed GPU machine learning environment that can spread computation and data across multiple hardware accelerators and nodes to handle datasets exceeding single-device memory. It mirrors standard estimator interfaces to allow the replacement of CPU-based models
This project is a machine learning educational curriculum and learning platform delivered through interactive Jupyter Notebooks. It serves as a comprehensive guide for mastering the Python data science toolkit, providing structured tutorials for numerical computing, tabular data manipulation, and statistical visualization. The curriculum includes specific implementation guides for Scikit-Learn and a practical course on TensorFlow for constructing, training, and deploying neural networks and computer vision models. It covers the end-to-end process of building predictive models, from initial pr
This repository serves as an educational collection of Python implementations for fundamental machine learning algorithms and statistical models. It provides a structured environment for learning core concepts through interactive computational documents that combine live code, narrative text, and data visualizations. The codebase focuses on predictive modeling development, offering instructional examples for building and evaluating regression, classification, and neural network models. It utilizes standardized data science library interfaces to demonstrate how to implement and execute these a
This project is a machine learning education resource consisting of Python implementations of statistical learning models and data analysis examples from a core textbook. It serves as a statistical modeling library that provides the code necessary to implement linear regression, classification, and unsupervised learning techniques for academic data analysis. The repository is structured as a reference-driven implementation, with a directory layout that mirrors the chapter and section hierarchy of the associated academic publication. It includes a set of scripts and notebooks designed to gener