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Serve Machine Learning Models via REST APIs in Under 10 Minutes

KDnuggets

By Kanwal Mehreen , KDnuggets Technical Editor & Content Specialist on July 4, 2025 in Machine Learning Image by Author | Canva If you like building machine learning models and experimenting with new stuff, that’s really cool — but to be honest, it only becomes useful to others once you make it available to them.

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10 GitHub Awesome Lists for Data Science

KDnuggets

This is a must-have bookmark for any data scientist working with Python, encompassing everything from data analysis and machine learning to web development and automation. It is ideal for data science projects, machine learning experiments, and anyone who wants to work with real-world data.

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7 Python Statistics Tools That Data Scientists Actually Use in 2025 - KDnuggets

KDnuggets

It is the most widely used package, and most machine learning and data analytics Python packages depend on it. Learn more: [link] 6. Abid Ali Awan ( @1abidaliawan ) is a certified data scientist professional who loves building machine learning models. import statistics as stats 2.

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How to Learn Math for Data Science: A Roadmap for Beginners

KDnuggets

Part 2: Linear Algebra Every machine learning algorithm youll use relies on linear algebra. Part 3: Calculus When you train a machine learning model, it learns the optimal values for parameters by optimization. You dont need to master calculus before starting machine learning – learn it as you need it.

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How EUROGATE established a data mesh architecture using Amazon DataZone

AWS Big Data

Improve accuracy and resiliency of analytics and machine learning by fostering data standards and high-quality data products. In addition to real-time analytics and visualization, the data needs to be shared for long-term data analytics and machine learning applications. This process is shown in the following figure.

IoT 109
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The Lifecycle of Feature Engineering: From Raw Data to Model-Ready Inputs

KDnuggets

By Jayita Gulati on July 16, 2025 in Machine Learning Image by Editor In data science and machine learning, raw data is rarely suitable for direct consumption by algorithms. This process removes errors and prepares the data so that a machine learning model can use it.

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How to Combine Streamlit, Pandas, and Plotly for Interactive Data Apps

KDnuggets

Born in India and raised in Japan, Vinod brings a global perspective to data science and machine learning education. Vinod focuses on creating accessible learning pathways for complex topics like agentic AI, performance optimization, and AI engineering.