Learn Probability Through Problems (exercises, hints, and solutions provided): https://
amzn.to/3vZed8W
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#Statistics #DataScience #Mathematics #DataScientist
@kirkdborne
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Learn Probability Through Problems: Exercises and Solutions
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Mathematical Methods in Data Science: Theory and Python Applications
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Mathematical Methods in Data Science — Bridging Theory and Applications with Python: https://
amzn.to/4b7ZYQ4
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#ML #MachineLearning #DataScientist #DataScience #Mathematics #AI #Algorithms -
Mathematical Optimization with Python: Practical Guide
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Hands-On Mathematical Optimization with Python: https://
amzn.to/4b3VADe “…presents the key ingredients of an optimization problem and the choices one needs to make when modeling a real-life problem mathematically. Topics covered range from linear and network optimization to -
Time Series Analysis Python Cookbook: Data Engineering Forecasting
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New release from @PacktDataML at https://
amzn.to/4sjCbni "Time Series Analysis with Python Cookbook: Practical recipes for the complete time series workflow, from modern data engineering to advanced forecasting and anomaly detection" [2nd Edition; 812 pages] -
Python Feature Engineering Cookbook 3rd Edition Released
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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 Her course: https://
trainindata.com/p/feature-engi
neering-for-machine-learning
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#AI #ML #DataLiteracy #DataScience #DataScientist -
Complete Machine Learning Engineer Cookbook: Python AI Developer Path
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AI Mastery >> The Complete Machine Learning Engineer Cookbook for Everyone — Become an AI Developer with Python: https://
amzn.to/3XZTzQS Complete 5-part Learning Path:
1 – Build Your Foundation
2 – Assemble Your ML Toolkit
3 – Discover Deep Learning & Generative AI
4 – Master -
Scikit-learn Cookbook: 80+ Machine Learning Recipes Guide
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Scikit-learn Cookbook — 80+ recipes for #MachineLearning 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 -
Threat Detection: Reducing Breach Dwell Time with Advanced Scanning
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𝙎𝙘𝙖𝙧𝙮 𝙨𝙩𝙖𝙩: The median dwell time for a non-actor disclosed breach (i.e., only discovered by the breached organization) is 24 days, giving attackers ample opportunity to silently embed malicious code across systems. 𝙎𝙤𝙡𝙪𝙩𝙞𝙤𝙣: @Commvault Expands Threat Scan with
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Enterprise Cyber Event Recovery: Speed and Clean Recovery
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When a cyber "event" happens in your enterprise IT/data infrastructure, the stakes are high and the clock is ticking — you need to recover quickly and cleanly! "Event" is just a euphemism for the high-stakes drama that is unfolding rapidly. Does this sound familiar? @Commvault
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Transfer Learning: Adapting ML Systems to New Situations
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TRANSFER LEARNING book [390 pages]: https://
amzn.to/3R4G0zm Amazon Summary:
"Transfer learning deals with how systems can quickly adapt themselves to new situations, tasks and environments. It gives machine learning systems the ability to leverage auxiliary data and models to