Publications on Data Science, Machine Learning, AI & Analytics
- Python for Machine Learning: Learn Python from Machine Learning Projects from Machine Learning Mastery - Jun 12, 2022
We noticed that when people ask about issues in their machine learning project, very often it is not specifically a problem in machine learning but a problem in the programming language they use. It is sad to see someone distracted by the language, such as misunderstanding the error message that the Python interpreter gave. If we know more about working in the Python ecosystem, we can be much more efficient and focused on the machine learning problem itself.
- The Machine Learning Mastery EBook Catalog from Machine Learning Mastery - Jun 12, 2022
Frustrated with one-off articles and too much math? Take the Next Step and Get Tutorial-Based Playbooks that will Guide You to a Specific Result. Welcome to: the Machine Learning Mastery EBook Catalog.
- Practical Machine Learning for Computer Vision: Chapter 3 from O'Reilly Media - Jun 29, 2022
Using machine learning models to extract information from images is one of the trickiest ML tasks—but it often yields invaluable insights. What’s more, image classification is the “Hello World” of deep learning: It’s a stepping stone to other deep learning domains, such as natural language processing.
- Creating a Production Launch Plan from O'Reilly Media - Jun 22, 2022
This practical report demonstrates how Google devised its production launch plan and provides actionable advice to help your company develop its own.
- Intel and Aible Performance Benchmark and Case Studies Report from Aible - Jun 21, 2022
Download the report to see details with more case studies and the initial results from the performance benchmark study.
- 5 Critical Considerations for Building an Agile Data Pipeline from Incorta - Jun 12, 2022
Traditional data collection, curation, and analysis methods are anything but “agile.” Here’s what to do instead.
- Building Effective Machine Learning Teams from Comet - Jun 12, 2022
eBook: Why Visibility, Reproducibility, and Collaboration are Required for ML & AI Success.