30 Agents Every AI Engineer Must Build — Build production-ready agent systems using proven architectures and patterns: http://
amzn.to/41ckg6z v/ @PacktDataML —
What you will learn:
Deploy production-ready agent systems that scale securely and reliably
Use LangChain and
@kirkdborne
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Agent Engineering Guide Covers Secure and Reliable Production Deployment
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Free Large Language Model Foundations Textbook Available as PDF
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Foundations of Large Language Models: http://
arxiv.org/abs/2501.09223 [277-page PDF]
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#LLMs #AI #GenAI #GenerativeAI #ML #MachineLearning #DataScience -
Open Textbook Explains Engineering and Implementation of Machine Learning Systems
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Machine Learning Systems — The Principles & Practices of Engineering Artificially Intelligent Systems: http://
mlsysbook.ai by @profvjreddi [Updated 2000+ pages PDF] Open-source textbook on how to design and implement AI systems effectively.
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#ML #MLOps #DataScience -
Open Causal Inference Ebook Covers Applications of Machine Learning
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[Download 496-page PDF eBook] Applied Causal Inference Powered by Machine Learning and AI: http://
arxiv.org/abs/2403.02467
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#ML #DataScience #Algorithms #Statistics -
Free Machine Learning Textbook Targets Scientists and Engineers
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Download 348-page PDF eBook >> Machine Learning for Scientists and Engineers: https://
smlbook.org/book/sml-book-
draft-latest.pdf
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JPMorgan Guide Surveys Machine Learning and Alternative Data for Investors
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This classic FREE 280-page PDF report from @JPMorgan provides an excellent framework for Machine Learning, AI, and Data Science investors, including an overview of types of alternative data and a brilliant tutorial on ML methods to analyze the data: https://
cpb-us-e2.wpmucdn.com/faculty.sites.
uci.edu/dist/2/51/files/2018/05/JPM-2017-MachineLearningInvestments.pdf
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A Primer Explains Hidden Markov Models
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17-page PDF explains Hidden Markov Models (HMM): https://
web.stanford.edu/~jurafsky/slp3
/A.pdf
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Scikit-Learn Cookbook Covers Preprocessing and Machine Learning Workflows
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Scikit-learn Cookbook — 80+ recipes for Machine Learning in Python with scikit-learn [3rd Edition]: http://
amzn.to/4oDGOq7 v/ @PacktDataML 𝓒𝓸𝓷𝓽𝓮𝓷𝓽𝓼:
Common Conventions & API Elements of Scikit-Learn
Pre-Model Workflow and Data Preprocessing
Dimensionality -
Unsupervised Learning Guide Covers Algorithms and Data Preparation
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Data Without Labels — Models and Algorithms for Practical Unsupervised Machine Learning: http://
amzn.to/4q5bbYz 𝓦𝓱𝓪𝓽 𝓨𝓸𝓾 𝓦𝓲𝓵𝓵 𝓛𝓮𝓪𝓻𝓷: Fundamental building blocks and concepts of machine learning and unsupervised learning
Data cleaning for structured and -
Supervised Learning Guide Covers Capacity, Generalization and Optimization
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"Machine Learning Foundations, Volume 1: Supervised Learning" – available at http://
amzn.to/4syhPal 𝓑𝓮𝓷𝓮𝓯𝓲𝓽𝓼: Master the key concepts of supervised machine learning, including model capacity, the bias-variance tradeoff, generalization, and optimization techniques