#MachineLearning

"Mathematical Pathways to Machine Learning" is a new introduction to the mathematical foundations of machine learning, published in August 2026. It is a collection of lecture notes that develops the mathematics from first principles and covers vectors and matrices, inner and outer products, orthogonality and projections, Gram–Schmidt and QR factorization, eigenvalues and eigenvectors, SVD, PCA, derivatives and gradients, Jacobians, Hessians, Taylor approximations, convexity, least squares, gradient descent, convergence and conditioning, Newton's method, momentum, Lagrange multipliers, KKT conditions, duality, and support vector machines. The mathematical concepts are directly connected to actual machine learning applications, including neural networks and backpropagation, PageRank, recommender systems, dimensionality reduction, regression, optimization, and SVMs. The presentation is accessible and very well structured, with worked examples and exercises throughout, including exam-practice problems and small computational exercises.

"Machine Learning: The Basics" by Alexander Jung A great, compact refresher on the core concepts of machine learning through the data–model–loss framework. It explains: - hypothesis spaces - model selection - empirical risk minimization and regularization - probabilistic models and clustering - federated learning - privacy and explainability

📂 Cheat sheets for data science and machine learning: #DataScience #MachineLearning #CheatSheet

MCP as the interface to Microsoft Fabric #MachineLearning #ChatGPT

Collection of essential cheat sheets for #machinelearning and #deeplearning

After two years of writing + editing, the (proof-read) hard copy is finally here! Thank you to everyone who helped with this book. I'll tweet about a few unique things about this book. To get a PDF copy of this book: #datascience, #MachineLearning

Machine Learning from Scratch 🚀 A 33K-star GitHub repo rebuilds machine learning algorithms from scratch, from linear regression to GANs and deep reinforcement learning. It's called ML-From-Scratch, created by Erik Linder-Noren, and the entire project is designed around understanding the implementations rather than optimizing them for speed. The repository contains readable Python implementations of the algorithms normally hidden behind high-level machine learning libraries. It covers - → Linear and logistic regression → Decision trees, random forests and gradient boosting → XGBoost and support vector machines → K-means, DBSCAN, PCA and Gaussian mixtures → Neural networks, CNNs and RNNs → Generative adversarial networks → Deep Q-learning for CartPole The neural-network code is especially useful. It includes implementations of convolutional layers, pooling, batch normalization, dropout, activation functions, loss functions and optimizers. You can follow how the components fit together to train a convolutional classifier, generate handwritten digits with a GAN, or train a reinforcement-learning agent. Most of the numerical work is built around NumPy, with additional scientific Python dependencies where needed. The implementations prioritize readability over performance. It's an older codebase, so some examples need dependency updates to run with current libraries. For anyone who has studied machine learning theory and wants to examine the actual algorithms, this is an excellent reference to work through. GitHub:

Machine Learning Methods — Free 874-Page Open Access Textbook! Pre-Order, Now! Hang Li’s Springer 2026 book, a follow up 2nd Edition is an enormous reference spanning the four core branches of Machine Learning: • Supervised Learning • Unsupervised Learning • Deep Learning • Reinforcement Learning • 80+ essential algorithms • GBDT + EM Algorithm • Transformers • Diffusion Models • PPO • Mathematical formulations + intuitive explanation • Practical examples+ Machine Learning real-world relevance At 874 pages with 260 illustrations, it is designed for advanced undergraduate/graduate students of ML, educators and AI professionals. Springer explicitly marks the textbook Open Access, free and unlimited access. #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Mathematics #Programming #Books #Coding #100DaysofCode Free Open Access: Amazon:

Machine Learning Engineers, where are you? 👀🔥 I have a remote opportunity for talented ML engineers! 🌍💻 💰 $60–$120/hour ⏰ 15 hours/week 🎓 MSc. or PhD preferred 🐍 Python + ML expertise required 👇 Comment “ML” if you're interested! #MachineLearning #AIJobs #RemoteWork

Day 12/60 — Classification Model Evaluation 📊🐍 Learning Accuracy, Precision, Recall, F1-score, Confusion Matrix, Classification Report, ROC-AUC, and Average Precision using Scikit-learn. #Python #MachineLearning #AI #60DaysOfLearning

🚀 30 AI Algorithms Every Data Scientist Should Know From Machine Learning to Deep Learning, these algorithms are essential for understanding how AI works. Save this cheat sheet for future reference! #AI #MachineLearning #DataScience

Best of #SQL Cheat Sheet #MachineLearning #DataScience #Cybersecurity #BigData #Analytics #AI #IIoT #IoT #5G #Python #RStats #TensorFlow #JavaScript #ReactJS #FutureOfWork #DataScientist #Linux #Programming #100DaysOfCode #NodeJS #golang #NLP #GitHub #MWC23 #SDGs #MLOps

Since it's summer, and more or less internship and tech interview season, I made all 30 chapters of my Machine Learning Q and AI book freely available for the summer: Hope it’s helpful! Happy reading, and good luck if you are interviewing!

LLM Engineer's Handbook Master the art of engineering large language models from concept to production Link - #LLM #MachineLearning #AI #code #Coding #Python #programming

This is free f*cking gold for AI engineers.. 10 repos worth keeping, from Python basics all the way to LLMs, agents and production AI. Forget the random tutorials. 1. Python-100-Days -- 100 days through fundamentals, data analysis and web dev. ↳ 2. Generative AI for Beginners -- LLM basics, prompting, RAG, agents and fine-tuning. ↳ 3. LLMs from Scratch -- build one yourself: tokenization, attention, transformers, training. ↳ 4. ML for Beginners -- 26 lessons of classical ML across 12 weeks. ↳ 5. OpenAI Cookbook -- working examples that take you from reading to building. ↳ 6. Stable Diffusion -- the original implementation and the research code behind it. ↳ 7. AI Agents for Beginners -- tool use, RAG, agent frameworks and multi-agent systems. ↳ 8. AI for Beginners -- 24 lessons over 12 weeks: neural nets, vision, NLP. ↳ 9. LLM App -- RAG pipelines, enterprise search, real-time data and vector search. ↳ 10. Segment Anything -- Meta's foundation model for image segmentation. ↳ Start from wherever you are: > Python -> Python-100-Days. > ML -> ML-For-Beginners. > LLMs -> LLMs-from-scratch. > Agents -> AI-Agents-for-Beginners. > Building -> OpenAI Cookbook, LLM App. > Vision -> Segment Anything. Pick one, build something real with it, then move on to the next.

Am I the only one who imports libraries in alphabetical order? 🥲 #DataScience #MachineLearning #AI

Learning Machine Learning in the era of Al should not be overwhelming, In this video I shared the 3 free resources you need to be a good machine learning engineer. Comment "ML" for these all resources. #ai #softwaredeveloper #machinelearning #maths #tech

Create, run, consume and maintain #machinelearning apps with ease by using algorithms that require no data-science skills. @SAPLeonardo

Implementing Agentic RAG on #Azure using code #MachineLearning #ChatGPT

One year ago, I launched this free textbook Today, the book has been downloaded by >35,000 students from >500 universities. If each book costs $100, we have saved families $3.5M. Boiler up @LifeAtPurdue #DataScience #MachineLearning