#Machinelearning

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:

Artificial Intelligence Terms You Should Know. #artificialintelligence #AIterms #MachineLearning #DataScience

Collection of essential cheat sheets for #machinelearning and #deeplearning

Our preprint on a comprehensive benchmark of #sklearn is out. Some interesting findings! #MachineLearning #dataviz

🚀 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

"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.

McKinsey: Detailed Discussion Paper On Artificial Intelligence #AI #MachineLearning #DeepLearning #BigData #Fintech #Insurtech #Marketing #Datascience #ML #DL #Robotics #HealthTech #martech #tech

Generative Quantum Machine Learning Methods for Cancer Detection using Augmented Datasets #machinelearning #compchem

A Concise Guide to AI & Machine Learning Use Cases @SpirosMargaris @JimMarous @ipfconline1 @drlindekilde @BrettKing #AI #MachineLearning #DeepLearning #BigData #Fintech #Insurtech #Marketing #Datascience #ML #DL #Robotics #HealthTech #IoT #tech

#MachineLearning can connect laser processing conditions with material structure and performance. This review explores its potential for defect prediction, #Microstructure optimization and improved mechanical properties. @NUSingapore #IJEM #OpenAccess:

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.

Mathematics of Machine Learning Master linear algebra, calculus, and probability for machine learning Link - #MachineLearning #AI #ML #Algorithms #code #Coding #DataScience #SoftwareEngineering #programming

"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

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

Introducing Facets, an open source visualization tool to help understand and analyze #MachineLearning datasets

As #AI adoption accelerates, the spotlight is shifting from training models to deploying them efficiently at scale. 📈 Explore the technologies driving the next wave of AI hardware innovation in the October issue of @IEEESpectrum: 🔗 #MachineLearning

Mastering NLP From Foundations to Agents: Building AI Agents through Agentic Automation and RAG Workflows with Python 📕 Get Now: #GenerativeAI ✨ | #MachineLearning 📊 | #AIEngineering ⚙️ | #AutomationTools 🔄 | #TechBooks 📚

Machine Learning with PyTorch and Scikit-Learn Develop machine learning and deep learning models with Python Link - #MachineLearning #ML #AI #DataScience #PyTorch #Python #SoftwareEngineering #programming

Machine Learning: Algorithms, Python and Applications in Reliability and Engineering Analytics (Springer Series in Reliability Engineering) 📕 Get Now: #MachineLearning 🤖 | #PythonProgramming 🐍 | #EngineeringAnalytics 📊 | #ReliabilityEngineering ⚙️ | #MLAlgorithms 💻

📢 Call for Papers: Feature Papers for “Land Innovations—Data and Machine Learning” Section: 4th Edition ✨ Guest Editors: Prof. Dr. Chuanrong Zhang 📅 Deadline: 31 March 2027 🔗 #landuse #machinelearning #landcover