Most engineers learn distributed systems the hard way — by watching something break in production at 2 a.m. Designing Data-Intensive Applications exists so you don’t have to learn everything that way. Written by Martin Kleppmann, the book has become one…
Machine learning models are useful only if they are effective in solving real world problems. Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications extends the machine learning algorithm development to the design, deployment, and productionization of scalable machine…
Practical Statistics for Data Scientists In the rapidly evolving world of data science, statistics is the foundation of meaningful data analysis and machine learning. Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python is one of the…
If you’re planning to build a career in data science, machine learning, or artificial intelligence, having a strong mathematical foundation is essential. Essential Math for Data Science: Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics by…
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