justmarkham/scikit-learn-videos
3,795 स्टार्स·2,532 फोर्क्स·Jupyter Notebook·2 व्यूज़courses.dataschool.io/introduction-to-machine-learning-with-scikit-learn↗
Scikit Learn Videos
This project is a collection of interactive Jupyter notebooks and a structured machine learning tutorial series. It serves as an educational resource for studying predictive modeling and statistical analysis through a curriculum of executable code examples.
The notebooks are specifically designed to accompany video tutorials, integrating external video assets with live code to synchronize visual instruction with hands-on experimentation. This approach allows users to follow sequential lessons while executing and modifying machine learning workflows directly in a browser.
The content covers the end-to-end machine learning development lifecycle, including data preprocessing, model training, and performance optimization. It provides guidance on constructing data science pipelines, processing text-based data, and evaluating model accuracy using metrics such as confusion matrices and ROC curves.
Features
- Jupyter Notebook Curricula - Delivers structured machine learning learning paths as interactive Jupyter notebooks with embedded exercises.
- Machine Learning Workflow Libraries - Teaches standardized pipelines for model training, evaluation, and estimation through structured lessons.
- Predictive Modeling Workflows - Guides users through end-to-end processes for implementing and evaluating predictive machine learning algorithms.
- Sequential Learning Paths - Organizes educational materials into a linear progression that guides users through the machine learning lifecycle.
- Data Science Learning Materials - Offers educational resources and executable code examples for learning predictive modeling and statistical analysis.
- Data Science Notebooks - Provides interactive Jupyter notebooks for exploring and learning data science and statistical analysis workflows.
- Data Science Tutorials - Provides a comprehensive collection of tutorials and guides for mastering data science libraries and tools.
- Technical Skill Acquisition - Offers project-based learning paths and interactive notebooks to help users master predictive modeling.
- Interactive Notebook Learning Resources - Delivers educational resources as Jupyter notebooks where each cell covers a discrete learning step.
- Literate Programming Notebooks - Provides interactive documents that combine executable code with narrative text for reproducible machine learning education.
- Machine Learning Tutorials - Provides guided exercises and structured content for learning foundational machine learning algorithms.
- Scikit-Learn Examples - Includes practical code demonstrations using the Scikit-Learn library for standard machine learning tasks.
- Machine Learning Fundamentals - Delivers foundational content covering machine learning workflows, data preprocessing, and model training.
- Workflow Tutorials - Guides users through the practical steps of data preparation, model training, and performance optimization.
- Educational Model Training - Provides training of small-scale models designed to demonstrate core machine learning concepts.
- Model Training Optimizers - Covers hyperparameter optimization and search strategies to improve model training convergence and results.
- Data Preprocessing Pipelines - Teaches how to build pipelines for cleaning and formatting raw data for machine learning ingestion.
- Text Processing Tutorials - Provides educational resources on converting text into numerical features through vectorization for machine learning.
- Rich Media Integration - Integrates external video assets and multimedia formats directly into the learning documents.
- Model Accuracy Evaluators - Demonstrates how to measure model performance using confusion matrices, ROC curves, and AUC metrics.
- Rich Media Document Handling - Handles the integration of non-editable video blocks within the document to synchronize visual instruction with code.
- Artificial Intelligence - Practical video tutorials for machine learning using the scikit-learn library.
- Machine Learning and AI - Video tutorials for learning the scikit-learn machine learning library.
- Video Tutorials - Video tutorials for machine learning with scikit-learn.
