DataTalksClub/machine-learning-zoomcamp
Machine Learning Zoomcamp
This project is a structured educational program and machine learning engineering course. It provides a comprehensive curriculum and learning path focused on data science, the development of predictive models, and the operational aspects of MLOps.
The instructional material covers the full machine learning lifecycle, moving from basic data engineering to production deployment. This includes guides on wrapping models in APIs, utilizing container-based packaging, and implementing serverless architectures to host models in cloud environments.
The program encompasses technical training in predictive model development for regression and classification, feature engineering workflows, and model performance evaluation using precision and recall. It further addresses the transition of models into production through scalable serving layers and web-based interfaces.
Features
- Machine Learning Engineering Curricula - Offers a structured educational program covering the full lifecycle of developing and deploying machine learning models.
- Lifecycle Workflows - Guides learners through the complete machine learning lifecycle from exploratory data analysis to cloud hosting.
- Data Science Training Programs - Ships a sequence of lessons covering feature engineering, model evaluation, and predictive model building.
- Feature Engineering - Provides a workflow for transforming raw data into optimized numerical inputs using encoding and selection techniques.
- Machine Learning Training - Provides technical training on training regression, classification, and deep learning models using mathematical libraries.
- Model Evaluation Metrics - Teaches the use of precision, recall, and scoring curves to evaluate and validate trained machine learning models.
- Model Evaluation Metrics - Teaches how to measure model quality using precision-recall curves to handle data class imbalances.
- Model Serving & Deployment - Covers the transition of trained models into production environments using specialized serving tools.
- Predictive Model Development - Instructs on building regression and classification models to predict outcomes from structured data.
- AI Model Production Deployment - Provides patterns and workflows for transitioning trained models into scalable production environments via APIs.
- Sequential Learning Paths - Organizes technical machine learning instruction into sequential modules moving from data engineering to production.
- Machine Learning Courses - Provides a structured training program teaching the theory and practical application of machine learning models for production.
- MLOps Guides - Offers instructional materials on the operational side of machine learning, including serverless and containerized deployment.
- Exploratory Data Analysis Workflows - Includes an iterative workflow for performing exploratory data analysis and variable encoding to improve model accuracy.
- Model Inference APIs - Provides instruction on creating web endpoints that expose trained model predictions for external application requests.
- Serverless Serving - Teaches how to host machine learning models as ephemeral functions that scale automatically based on request volume.
- Container Image Packaging - Teaches how to wrap models and dependencies into isolated container images for consistent cloud execution.
- Cloud Deployment Guides - Provides practical documentation for wrapping models in APIs and hosting them in cloud environments.
- ML Model Hosting - Provides guidance on wrapping trained models in APIs and containerization for consistent web service behavior.
- Machine Learning - Course focused on building and deploying models.
- Educational Courses - Practical machine learning bootcamp for developers.
Star history
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