Develop and Debug high-performance, low-bias, and explainable Machine Learning and Deep Learning Models with Python : http://
amzn.to/3u2JiIB by @AliMLearning via @PacktDataML —————
#DataScientist #DataScience #AI #ML
DATA
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Developing and Debugging Machine Learning and Deep Learning Models
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Python Feature Engineering Cookbook for Machine Learning Models
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3rd Edition! "Python Feature Engineering Cookbook", complete guidebook with recipes for crafting powerful features for #MachineLearning models: http://
amzn.to/4rDWUT9 by @Soledad_Galli —————
#AI #ML #DataLiteracy #DataScience #DataScientist -
Guide to Deploying and Operationalizing AI Applications in Production
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Moving data and AI apps from prototype to production should not take months of custom infrastructure work. This hands-on guide explores how to build → deploy → and operate production-ready apps on Databricks using transactional data layers, built-in governance, and repeatable
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AutoML’s Role in Democratizing Model Development and Governance
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AutoML is democratizing model development across business teams, shifting responsibility toward data quality and governance. Pressure grows on organizations to align tools, skills, and decisions so outputs remain reliable in daily operations. Microblog by @antgrasso #AI
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Energy, not compute, may be AI bottleneck; Stratos data center uses 9 GW
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Energy, not compute, may become the real bottleneck for AI. The proposed Stratos data center in Utah could consume up to 9 GW of power at full buildout, making it one of the largest data center projects in the world. That is roughly comparable to New York City’s average
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Computer Age Statistical Inference: Foundations for Data Science and AI
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Computer Age Statistical Inference — Algorithms, Evidence, and Data Science: http://
amzn.to/47zAhar -
Educational resources for unsupervised machine learning and data analysis
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Data Without Labels — Models and Algorithms for Practical Unsupervised #MachineLearning: http://
amzn.to/4q5bbYz 𝓦𝓱𝓪𝓽 𝓨𝓸𝓾 𝓦𝓲𝓵𝓵 𝓛𝓮𝓪𝓻𝓷: Fundamental building blocks and concepts of machine learning and unsupervised learning
Data cleaning for structured and -
Practical Guide to Applied Machine Learning and Model Implementation
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Applied Machine Learning: A Practical Guide to Preparing Data, Selecting Algorithms, and Implementing Machine Learning Models in the Real World Available at http://
amzn.to/4uTZjLn -
Machine Learning for Algorithmic Trading and Time-Series Prediction
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#MachineLearning for #AlgorithmicTrading — Predictive models to extract signals from market and alternative data for systematic trading strategies with #Python and for *other* #TimeSeries prediction applications Explore http://
amzn.to/47Vd6s8 by @ml4trading via -
Moving Beyond Traditional Statistics in the Age of AI
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If You’re Still Worshipping Pearson Correlation, You’re Not a Data Scientist — You’re Driving a Horse Cart in the Age of AI: https://
valeman.medium.com/if-youre-still
-worshipping-pearson-correlation-you-re-not-a-data-scientist-you-re-driving-a-831dc0590de6
… My summary: Don’t just plug your data into some formula to find the answers to the questions you started with. Real Data