Kirk Borne

Join my X subscriber community for exclusive perks! xitter.twitterapi.xyz/KirkDBorne/creator-sub…

#Sedecordle Daily #1733 21/21 🔥 1 day streak 0️⃣8️⃣⬛0️⃣5️⃣ 0️⃣6️⃣⬛0️⃣7️⃣ 1️⃣1️⃣⬛1️⃣2️⃣ 1️⃣3️⃣⬛1️⃣0️⃣ 1️⃣4️⃣⬛1️⃣5️⃣ 1️⃣6️⃣⬛1️⃣7️⃣ 1️⃣8️⃣⬛1️⃣9️⃣ 2️⃣1️⃣⬛2️⃣0️⃣ sedecordle.com

Introduction to Differential Geometry of Space Curves and Surfaces: link.amazon/B01qaquJb + General Relativity, Simplified: link.amazon/B026YEQUU

I really like this quote from @FCentinello — it makes perfect sense to me… “Cynicism isn’t intelligence, and it doesn’t offer solutions because it's really just mentally tapping out. Curiosity is the muscle that discovers, builds, and explores.” Be curious! That’s how we are built. Just watch any small child — always testing, exploring, curious. That’s a natural-born scientific instinct.

"Machine Learning Foundations, Volume 1: Supervised Learning" — available at amzn.to/4syhPal 𝓑𝓮𝓷𝓮𝓯𝓲𝓽𝓼: 💠Master the key concepts of supervised machine learning, including model capacity, the bias-variance tradeoff, generalization, and optimization techniques 💠Implement the full supervised learning pipeline, from data preprocessing and feature engineering to model selection, training, and evaluation 💠Understand key learning tasks, including classification, regression, multi-label, and multi-output problems 💠Implement foundational algorithms from scratch, including linear and logistic regression, decision trees, gradient boosting, and SVMs 💠Gain hands-on experience with industry-standard tools such as Scikit-Learn, XGBoost, and NLTK 💠Refine and optimize your models using techniques such as hyperparameter tuning, cross-validation, and calibration 💠Work with diverse data types, including tabular data, text, and images 💠Address real-world challenges such as imbalanced datasets, missing data, and high-dimensional inputs

Feature Engineering and Selection — A Practical Approach for Predictive Models (Chapman & Hall/CRC Data Science Series): amzn.to/3UF4DEy

Python Machine Learning By Example: amzn.to/3MczBwk v/ @PacktDataML [4th Edition] + GitHub: github.com/PacktPublishin… 518-pages! What you will learn: 🟣Machine learning best practices throughout data preparation and model development 🟣Build and improve image classifiers using convolutional neural networks (CNNs) and transfer learning 🟣Develop and fine-tune neural networks using TensorFlow and PyTorch 🟣Analyze sequence data and make predictions using recurrent neural networks (RNNs), transformers, and CLIP 🟣Build classifiers using support vector machines (SVMs) and boost performance with PCA 🟣Avoid overfitting using regularization, feature selection, and more

Feature Selection in Machine Learning — 20+ feature selection methods to build simpler, faster, and more reliable ML models: link.amazon/B08emWeJ0 by @Soledad_Galli via @PacktPublishing 🔹 🔹 🔹 🔹 🔹 🔹

XGBoost for Regression, Predictive Modeling, and Time Series Analysis — Learn how to build, evaluate, & deploy predictive models: amzn.to/4l2YcU9 v/ @PacktDataML — My review: 🟠XGBoost is definitely the focal point and central contribution of this book, along with all those other things: Regression Trees, Predictive Modeling, Time Series Analysis, and Forecasting. 🟠This book’s added value comes in its thorough progression from start to finish with comprehensive coverage of all the major steps and issues in data science and machine learning modeling: data cleaning, imbalanced data, feature engineering, feature engineering pipeline, feature importance, categorical data encoding, model interpretability and explainability, ensembles, model training, model evaluation metrics, hyperparameter tuning, model deployment, and more - including a quick-start case study starter project that feeds into next-level projects. 🟠I highly recommend this broadly useful book for all levels of practitioners.

Mastering Exploratory Data Analysis (EDA) with Python: A Complete Guide to Data Visualization, Feature Engineering, and Real-World Insights for Aspiring Data Scientists Get it here: link.amazon/A08eXqhVx #DataScience #DataScientist #DataLiteracy

Artificial Intelligence for Cybersecurity: amzn.to/4gOK3rD v/ @PacktDataML —— #AI #MachineLearning —— 𝒦𝑒𝓎 𝐹𝑒𝒶𝓉𝓊𝓇𝑒𝓈: 🟠Learn AI methods and see how they fit into cybersecurity 🔵Learn how to design solutions in cybersecurity that include AI as a key feature 🔴Acquire practical AI skills using step-by-step exercises and code examples 🟢Purchase of the print or Kindle book includes a free PDF eBook

Feature Engineering for Modern Machine Learning with Scikit-Learn — Advanced Data Science & Practical Applications (Book 2 in Advanced Data Analysis Series): link.amazon/B0fTE3p4F + Book 1 — Data Engineering Foundations: link.amazon/B0aTQssdV

Benford’s Law: link.amazon/B09tTKmEH Fraud detection is just one of its many practical powerful applications! Amazon Summary: “This book provides the first comprehensive treatment of Benford's law, the surprising logarithmic distribution of significant digits discovered in the late nineteenth century. Establishing the mathematical and statistical principles that underpin this intriguing phenomenon, the text combines up-to-date theoretical results with overviews of the law’s colorful history, rapidly growing body of empirical evidence, and wide range of applications. An Introduction to Benford’s Law begins with basic facts about significant digits, Benford functions, sequences, and random variables, including tools from the theory of uniform distribution. After introducing the scale-, base-, and sum-invariance characterizations of the law, the book develops the significant-digit properties of both deterministic and stochastic processes, such as iterations of functions, powers of matrices, differential equations, and products, powers, and mixtures of random variables. Two concluding chapters survey the finitely additive theory and the flourishing applications of Benford’s law. Carefully selected diagrams, tables, and close to 150 examples illuminate the main concepts throughout. The text includes many open problems, in addition to dozens of new basic theorems and all the main references. A distinguishing feature is the emphasis on the surprising ubiquity and robustness of the significant-digit law. This text can serve as both a primary reference and a basis for seminars and courses.”

Prompting Scikit-Learn for Machine Learning — Build Classification, Regression, Clustering, Feature Engineering, and Production ML Pipelines Faster with Scikit-Learn and AI Prompt Engineering: link.amazon/B0eYYlwSP

Introduction to the Probability Theory: link.amazon/B0aXUXQ8e + Winning Ways for Your Mathematical Plays: link.amazon/B06hjpnkB

Notes and Problems in Number Theory Volume 1: link.amazon/B01qeYV4W Volume 2: link.amazon/B0fKac8vD

LOL. 🤣

Harness Engineering — Design Patterns for Securing Long-Horizon Multi-Agent AI Systems: link.amazon/B061bF9ne from @PacktPublishing Key Topics: 🔶 Design a security control plane for long running agents and delegated multi-agent work 🔶 Contain prompt injection and memory poisoning through enforceable trust boundaries 🔶 Use HarnessLab scenarios to connect design patterns with observable control decisions 🔶 Build a secure control plane for agentic AI with design patterns for bounded autonomy, least privilege identity, trusted context, governed memory, and runtime recovery.

#Sedecordle Daily #1732 X/21 (15/16) Lost! 😟 0 day streak 0️⃣5️⃣⬛0️⃣6️⃣ 0️⃣8️⃣⬛0️⃣9️⃣ 1️⃣0️⃣⬛1️⃣7️⃣ 1️⃣1️⃣⬛1️⃣2️⃣ 1️⃣3️⃣⬛1️⃣4️⃣ 1️⃣5️⃣⬛1️⃣6️⃣ 1️⃣8️⃣⬛1️⃣9️⃣ 🟥🟥⬛2️⃣0️⃣ sedecordle.com