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Free Download Udemy - Old Engine Creation in Blender - Substance Painter Published: 4/2025 Created by: Oleh Boiko MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: Intermediate | Genre: eLearning | Language: English | Duration: 29 Lectures ( 8h 13m ) | Size: 9.34 GB Creating a high-quality low-poly 3D asset for games What you'll learn Proper modeling of complex shapes - Create an optimized 3D game-ready attachment with a built-in light source. UV unwrapping - Efficiently unwrap your model for high-quality texturing. High-poly modeling in ZBrush - Add intricate details for realistic baking. Baking textures in Marmoset Toolbag - Transfer high-poly details to the low-poly model. Creating realistic textures in Substance Painter - Develop high-quality, game-ready materials. Requirements Skill Requirements: This course is designed for intermediate users who are already proficient in Blender and Substance Painter. Prior experience with ZBrush and Marmoset Toolbag is not required, as we will only cover the essential tools needed for this project. Description Welcome to my new course on creating a highly detailed 3D engine model!In this course, I'll guide you through all the key stages of building a complex object - from basic modeling in Blender to final texturing using professional software such as ZBrush, Marmoset Toolbag, and Substance Painter. Together, we'll create a portfolio-worthy model that will help you sharpen your skills and take your 3D art to the next level.This course is designed for those who already have some experience with Blender and are ready to expand their toolkit by learning additional professional programs. 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Free Download Udemy - Oil and Gas Team Management Seminar Published: 4/2025 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Language: English | Duration: 1h 18m | Size: 453 MB An Overview Seminar of Oil and Gas Teams Management Presented by 20 Years Sr. Petroleum Engineering Leader What you'll learn Oil and Gas Reservoir Team Management Oil and Gas Soft Skills Oil and gas Leadership Oil and Gas Business Management Requirements Interest in Oil and Gas Industry Interest in Oil and Gas Business Management Description This Oil and Gas Team Management Seminar addresses critical aspects of leadership and teamwork within the oil and gas industry. The seminar emphasizes the importance of tackling industry challenges through effective communication, collaborative problem-solving, and adaptability. It also focuses on strategic planning, resource allocation, operational efficiency, and risk mitigation.The seminar further delves into leadership development, highlighting the importance of identifying skills, experiential learning, mentorship programs, and assessment. Effective team dynamics are crucial, with a focus on collaboration, open communication, mutual respect, and conflict resolution. Strategic thinking is another key component, addressing industry trends, emerging technologies, sustainability, and regulatory changes.Decision-making is explored, emphasizing data analysis, trend identification, risk mitigation, and stakeholder alignment through transparent communication and expectation management. The seminar concludes with organizational transformation, focusing on change management, employee empowerment, process optimization, and talent development through upskilling, succession planning, and performance management.Chapter Bullet Points:Topic 1: Oil and Gas Industry ChallengesEffective CommunicationCollaborative Problem-SolvingAdaptability and FlexibilityStrategic PlanningOptimize Resource AllocationEnhance Operational EfficiencyMitigate Risk and UncertaintyTopic 2: Leadership DevelopmentIdentify SkillsExperiential LearningMentorship ProgramGroup and Individual AssessmentTopic 3: Team DynamicsLeverage ExpertisePromote Open CommunicationFoster Mutual RespectEffective DialogueConflict ResolutionFeedback MechanismsTopic 4: Strategic ThinkingEmerging TechnologiesSustainability FocusRegulatory ChangesTopic 5: Effective Decision-MakingLeverage DataIdentify TrendsMitigate RisksStakeholder AlignmentIdentify Key StakeholdersFacilitate Open CommunicationManage ExpectationsStreamlined ProcessesCollaborative ApproachContinuous MonitoringTopic 6: Organizational TransformationAdapt to Evolving NeedsEmpower EmployeesOptimize ProcessesUpskilling WorkforceSuccession PlanningPerformance Management Who this course is for Reservoir , Geologist, Petroleum Engineers, Oil and Gas Professionals Petrophysicist, Geoscientist, Cased Hole Logs Analysts and Interpreters Geology and Petroleum Engineering College and University Students MBA and DBA Holders and Students Homepage: https://www.udemy.com/course/oil-and-gas-team-management-seminar/ AusFile https://ausfile.com/q4783o3tubka/netxh.Oil.and.Gas.Team.Management.Seminar.rar.html Rapidgator Links Download https://rg.to/file/08086227a53061cc6b540cdaa48d775e/netxh.Oil.and.Gas.Team.Management.Seminar.rar.html Fikper Links Download https://fikper.com/3dJr077gEp/netxh.Oil.and.Gas.Team.Management.Seminar.rar.html No Password - Links are Interchangeable

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No prior knowledge or experience with Photoshop is required Exercise files are provided Description Welcome to my Non-Scary Photoshop for Beginners!Photoshop is the industry standard for image editing and creation-and now it's even better with powerful AI features.You'll learn Photoshop basics and apply them by creating multi-image compositions, retouching photos, color correcting, using adjustment layers and selections, exploring AI-powered neural filters, and leveraging AI tools for generating and editing images quickly and accurately.This course is packed with beginner-friendly tutorials and step-by-step projects to help you get started confidently. No prior Photoshop or photography experience is needed.Hi, I'm Tim!I'm an Adobe Certified Instructor and Expert, as well as a designer based in London.We'll explore essential tools and techniques together, and you'll complete guided projects to reinforce your learning. 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Free Download Udemy - No Code Retrieval Augmented Generation (RAG) - A Crash Course Published: 4/2025 Created by: Aravind Mohanoor MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: Beginner | Genre: eLearning | Language: English | Duration: 13 Lectures ( 56m ) | Size: 536 MB Build a proof of concept in a few hours without writing any code What you'll learn What is Retrieval Augmented Generation Search The 9 aspects of DIY RAG Search Why Discourse AI is the best option for non-programmers to learn RAG Search How to use Discourse AI to implement a proof of concept in a few hours without writing any code Requirements No programming experience needed Description Recently I saw a post on an online forum which inspired this course:"I personally work with many people who can't really use any AI in their work (ChatGPT, Claude, Perplexity, etc) because of privacy and other concerns. They also don't have and can't afford dedicated AI engineers to build a custom solution, nor are they technical individuals themselves. So I see a very large market gap here for small and medium businesses/organizations, where Discourse AI is the perfect solution."Are you looking for an easy, no-code way to integrate AI into your workflows without breaking the bank or requiring technical expertise? This crash course is designed for professionals, small business owners, and educators who want to harness the power of Retrieval-Augmented Generation (RAG) with minimal effort.Using Discourse AI, this course will guide you through the basics of RAG-retrieving relevant information and generating intelligent summaries-using real-world examples based on YouTube video transcripts. With a hands-on approach, you'll learn how to use Discourse AI for answering questions promptly based on your existing knowledge base. This course focuses exclusively on practical, no-code techniques, ensuring that non-technical users can implement AI-driven workflows without worrying about complex setups or programming knowledge. See how you can unleash the potential of RAG in just a few easy-to-follow lessons!Note: this is intended to be a crash course which you can finish within an hour and I also expect most people will be able to get their RAG search up and running within a few hours. I do not plan to add more material to this course unless it is absolutely essential to explain the topic. If you are looking for in depth material, this is not the course for you. Testimonials from non-programmers for my Dialogflow coursesThese are testimonials for OTHER courses where people have mentioned that my courses are easy to follow for people who don't have a background in computer science or coding."I most definitely would recommend, and have recommended, Aravind's Dialogflow courses to othersI bought several courses to learn about Dialogflow ES. I see the courses as an investment in my learning, as such I am more than willing to pay a reasonable price.As someone who has no background in computer science or coding, but is purely self-taught, I was needing an easy-to-understand course for learning Dialogflow. Aravind's delivery and explanation of how to work with Dialogflow propelled my understanding and speeded up my learning curve.What I appreciate the most about Aravind's courses is how he organizes his lessons. He starts at the beginning and works his way through the lessons that follow a more logical and natural pattern of learning, in this case for beginners.Marisa T""As a non programmer who works with programmers this course is perfectThe course is targeting programmers, but as a non programmer who works with programmers this course is perfect. it provides an Overview, well explained concepts and frameworks, and not too technical to follow. As with all other courses on BotFlo, I find it easy to follow and understand.CAROLINE V" Who this course is for Non programmers who would like to learn about Retrieval Augmented Generation (RAG) using a hands-on approach Homepage: https://www.udemy.com/course/no-code-retrieval-augmented-generation-rag-a-crash-course/ AusFile https://ausfile.com/2j4vxn611h1x/prlbt.No.Code.Retrieval.Augmented.Generation.RAG.A.Crash.Course.rar.html Rapidgator Links Download https://rg.to/file/fe05e85f951e835f0a9e4eb937e37bff/prlbt.No.Code.Retrieval.Augmented.Generation.RAG.A.Crash.Course.rar.html Fikper Links Download https://fikper.com/kiR2H4HQ0r/prlbt.No.Code.Retrieval.Augmented.Generation.RAG.A.Crash.Course.rar.html No Password - Links are Interchangeable

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No experience necessary with AI generation tools like Midjourney. Description Want to create jaw-dropping images in minutes using the power of AI? 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Free Download Udemy - Master Network Automation With Python For Network Engineers Last updated: 3/2025 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 7.91 GB | Duration: 23h 24m Real-life hands-on Python and Ansible automation: SSH, Paramiko, Netmiko, Napalm, Telnet, Ansible, Cisco, Linux etc What you'll learn You will MASTER all the Python 3 key concepts starting from scratch. No prior Python or programming knowledge is required. Learn how to automate the configuration of networking devices with Python 3 in a multi-vendor environment. Learn network programmability with Python, GNS3 and Ansible. Learn and apply advanced Python concepts like Multiprocessing and Threading in Network Automation. See real-world examples of automation scripts with Python for Cisco IOS, Arista EOS or Linux. Understand how to use Telnet and SSH with Python for network automation. 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Only free software will be used: Python 3, PyCharm, GNS3, VirtualBox, Linux, etc. Cisco IOS Images should be acquired by the student (from Cisco's official website - contract required) to run into GNS3 or practice all the concepts on real Cisco devices. Basic Knowledge of GNS3 and how to run Cisco Images, although I'll provide you with the necessary setup information. Description This Network Automation with Python course also covers every major general Python Programming topic and is a perfect match for both beginners and experienced developers!Welcome to this Python hands-on course for learning Network Automation and Programmability with Python in a Cisco or Multivendor Environment, taught by a DevOps Engineer and Professional Trainer.Boost your Python Network Programming Skills by learning one of the hottest topics in the Networking Industry in 2024 and become one of the best Network Engineers!This course is based on Python 3 and doesn't require prior Python Programming knowledge. 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Automate the configuration of Cisco, Arista, and Linux using NetmikoNetmiko best practices and real-life examples for backup configuration of multiple devicesBuilding Concurrent Applications using Async IO (async/await, aiohttp, aiofiles, asyncssh)The NAPALM Library - Network Automation and Programmability Abstraction Layer with Multivendor supportHow to use NAPALM to retrieve information about devices and protocols and manage the devices' configurationWhat is Ansible and what are its components: Modules, Tasks, Plays, Playbooks, etcHow to execute Ad-Hoc commands against networking devices and servers using AnsibleAnsible modules: command, shell, raw, copy, file, apt, service, ios_command, ios_config etcHow to use create and run Ansible Playbooks against Networking Devices (Cisco, Arista, etc) and Linux ServersAnsible VaultAutomation of networking device configuration through a serial connection. 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Free Download Udemy - Machine Learning in Bioinformatics From Theory to Practical Published: 4/2025 Created by: Rafiq Ur Rehman MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: Beginner | Genre: eLearning | Language: English | Duration: 35 Lectures ( 6h 4m ) | Size: 2.25 GB Machine Learning for Bioinformatics: Analyze Genomic Data, Predict Disease, and Apply AI to Life Sciences What you'll learn Understand key machine learning concepts, including supervised and unsupervised learning. Learn the differences between classification, regression, clustering, and deep learning in bioinformatics. Process and analyze different types of biological data, such as genomic sequences, transcriptomics, and proteomics data. Understand feature engineering and data preprocessing techniques specific to bioinformatics datasets. Implement essential machine learning algorithms like Random Forest, SVM, k-means clustering, and neural networks in bioinformatics. Learn dimensionality reduction techniques (e.g., PCA, t-SNE) for high-dimensional biological data. Work with Scikit-learn, TensorFlow, Biopython, and Pandas to apply ML techniques in bioinformatics. Develop and optimize machine learning models for gene expression analysis, protein structure prediction, and variant classification. Apply machine learning to genomic variant classification, drug discovery, personalized medicine, and disease prediction. Build a machine learning pipeline for predicting gene function and protein interactions. Evaluate model performance using cross-validation, confusion matrices, ROC curves, and precision-recall metrics. Fine-tune models using hyperparameter optimization and feature selection. Understand deep learning architectures like CNNs and RNNs for biological sequence analysis. Implement deep learning models for protein structure prediction and genome annotation. Develop machine learning models for bioinformatics research and real-world applications. Learn how to interpret ML results for biological insights and scientific publications. Requirements No Prior Machine Learning Experience Needed! Familiarity with biological concepts such as DNA, RNA, proteins, and gene expression. Basic knowledge of bioinformatics file formats (FASTA, FASTQ, CSV, etc.). Basic understanding of Python syntax, loops, functions, and data structures. Experience with libraries like NumPy, Pandas, or Matplotlib is a plus, but not required. Understanding of basic concepts like mean, median, standard deviation, probability, and correlation. Some familiarity with linear algebra and calculus Description Machine Learning for Bioinformatics: Analyze Genomic Data, Predict Disease, and Apply AI to Life SciencesUnlock the Power of Machine Learning in Bioinformatics & Computational BiologyMachine learning (ML) is transforming the field of bioinformatics, enabling researchers to analyze massive biological datasets, predict gene functions, classify diseases, and accelerate drug discovery. If you're a bioinformatics student, researcher, life scientist, or data scientist looking to apply machine learning techniques to biological data, this course is designed for you!In this comprehensive hands-on course, you will learn how to apply machine learning models to various bioinformatics applications, from analyzing DNA sequences to classifying diseases using genomic data. Whether you are new to machine learning or have some prior experience, this course will take you from the fundamentals to real-world applications step by step.Why Should You Take This Course?No Prior Machine Learning Experience Required - We start from the basics and gradually build up to advanced techniques.Bioinformatics-Focused Curriculum - Unlike general ML courses, this course is tailored for biological and biomedical datasets.Hands-on Python Coding - Learn Scikit-learn, Biopython, NumPy, Pandas, and TensorFlow to implement machine learning models.Real-World Applications - Work on projects involving genomics, transcriptomics, proteomics, and disease prediction. Machine Learning Algorithms Explained Clearly - Understand how models like Random Forest, SVM, Neural Networks, and Deep Learning are applied in bioinformatics. What You Will Learn in This Course?By the end of this course, you will be able to: 1. Introduction to Machine Learning in BioinformaticsWhat is machine learning, and why is it important in bioinformatics?Overview of Supervised vs. Unsupervised LearningKey challenges in biological data analysis and how ML helps 2. Working with Biological DatasetsIntroduction to genomic, transcriptomic, and proteomic datasetsUnderstanding biological file formats: FASTA, FASTQ, CSV, and moreData preprocessing & cleaning: Handling missing values and noisy data 3. Supervised Learning for BioinformaticsUnderstanding classification & regression algorithmsImplementing Logistic Regression, Decision Trees, and Random ForestCase Study: Predicting disease from gene expression data 4. Unsupervised Learning & Clustering in BioinformaticsIntroduction to clustering techniquesApplying K-means and Hierarchical Clustering to gene expression analysisDimensionality Reduction: PCA, t-SNE, and their role in biological data visualization 5. Deep Learning & Neural Networks for BioinformaticsBasics of Deep Learning (DL) and Neural NetworksHow CNNs and RNNs are used for protein structure prediction & genome annotationCase Study: Using deep learning to classify cancer subtypes 6. Hands-on Machine Learning with Python for BioinformaticsSetting up the Python environment for ML applicationsWorking with Scikit-learn, Pandas, Biopython, and TensorFlowStep-by-step implementation of ML models on Synthetic biological data 7. Machine Learning Applications in Bioinformatics & Life SciencesGenomic Variant Classification using MLDrug Discovery & Personalized MedicineDisease Prediction Models for precision medicinePredicting protein-protein interactions (PPIs) using ML 8. Model Evaluation & Optimization in BioinformaticsEvaluating ML models with confusion matrices, ROC curves, and precision-recall analysisHyperparameter tuning for improved performanceAvoiding overfitting and improving model generalization 9. Building and Deploying Bioinformatics ML ModelsCreating end-to-end ML pipelines for bioinformaticsDeploying ML models in biomedical research & clinical settingsEthical considerations in AI-driven bioinformatics research Who Should Take This Course?This course is perfect for: Bioinformatics Students & Researchers - Learn how to integrate ML into your bioinformatics research. Life Science Professionals - Biologists, geneticists, and biotechnologists wanting to explore ML applications in genomics & drug discovery. Data Scientists - Looking to specialize in bioinformatics and apply ML to biological problems. Healthcare & Biomedical Professionals - Interested in AI-driven personalized medicine & disease prediction. Beginners in Machine Learning - No prior experience needed! This course teaches ML from scratch, specifically for bioinformatics applications.Course Requirements & PrerequisitesYou don't need prior experience in machine learning, but the following will be helpful:Basic biology and bioinformatics knowledge (DNA, RNA, proteins, gene expression)Some Python programming experience (loops, functions, data structures)Basic understanding of statistics and probabilityIf you're completely new to programming, we'll guide you step-by-step through the coding exercises! Tools & Technologies CoveredPython for Machine Learning (NumPy, Pandas, Matplotlib)Scikit-learn (for classical ML algorithms)TensorFlow/Keras (for deep learning applications)Biopython (for working with biological datasets)Jupyter Notebooks (for hands-on coding)What Makes This Course Unique?Hands-on Learning: Work with synthetic biological datasets and apply ML techniques step by step.Bioinformatics-Focused Curriculum: Unlike generic ML courses, we focus only on bioinformatics & life sciences applications.Comprehensive Yet Beginner-Friendly: We explain everything from basic ML to advanced deep learning models in an easy-to-understand way. Industry & Research Applications: Learn how ML is used in biotech, healthcare, and drug discovery.Course Projects & Real-World ApplicationsThroughout the course, you'll work on practical projects such as: Gene Expression Analysis Using ML Protein Sequence Classification with Deep Learning Cancer Subtype Prediction Using Genomic Data Building a Bioinformatics ML Pipeline for Variant ClassificationBy the end, you'll have portfolio-ready projects that showcase your ML & bioinformatics skills! Ready to Start Your Machine Learning Journey in Bioinformatics?Join now and take your bioinformatics skills to the next level with machine learning!Let's analyze genomes, predict diseases, and accelerate discoveries using AI! Enroll today and start applying machine learning to real-world biological problems! Who this course is for Undergraduate, postgraduate, and PhD students looking to integrate machine learning into their bioinformatics research. Biologists, geneticists, and biotechnologists who want to learn how machine learning can be applied to genomics, proteomics, and drug discovery. Data scientists looking to specialize in bioinformatics by applying machine learning algorithms to biological datasets. Learners with little or no prior experience in machine learning but with an interest in biology and data analysis. Researchers working on personalized medicine, biomarker discovery, and disease prediction who want to leverage machine learning for data analysis. 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Free Download Udemy - MLOps Real-World Machine Learning Projects for Professional Published: 4/2025 Created by: DS with Bappy MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: All | Genre: eLearning | Language: English | Duration: 20 Lectures ( 2h 54m ) | Size: 2.63 GB Build end-to-end ML pipelines with MLFLow, DVC, Docker, Flask, GitHub Actions, Chrome Plugging , and AWS What you'll learn Build and deploy real-world machine learning models using MLOps Tools Implement a complete Google Chrome Plugging Implement a complete CI/CD pipeline for ML using GitHub Actions and model versioning Track, manage, and compare ML experiments using DVC, MLflow for robust model governance Design modular, reusable MLOps pipelines that follow industry best practices Deploy and scale ML model on AWS cloud platforms with Docker production-ready architecture Requirements Basic knowledge of Python and machine learning concepts is recommended Familiarity with Git and the command line will be helpful, but not mandatory No prior experience with Docker, Kubernetes, or MLOps is required Description Welcome to the most hands-on and practical MLOps course designed for professionals looking to master real-world machine learning deployment.In this course, you won't just learn theory - you'll build and deploy production-grade ML pipelines using a modern stack including MLflow, DVC, Docker, Flask, GitHub Actions, and AWS. You'll even integrate ML models into a Chrome plugin, showcasing end-to-end MLOps in action.Projects You'll Build:- ML Sentiment Analyzer with MLflow & DVC- Reproducible training pipeline with DVC + Git- MLflow tracking dashboard with metrics & artifacts- Dockerized inference service with REST API- End-to-end CI/CD with GitHub Actions- Live deployment on AWS EC2- Chrome Extension that calls your ML API in real timeWhy Take This Course?Get hands-on experience with modern MLOps toolsLearn how to manage datasets, track models, and deploy to productionUnderstand real-world DevOps practices applied to Machine LearningBuild a portfolio of deployable, full-stack ML projectsGain job-ready skills for roles in MLOps, Data Engineering, and ML EngineeringThroughout this course, you'll work on production-grade ML projects that simulate real business use cases, incorporating tools and frameworks of MLOps. Whether you're looking to become an MLOps expert or deploy your first model professionally, this course equips you with the knowledge, code, and system design needed to succeed. Who this course is for Data Scientists and ML Engineers who want to deploy their models in production AI Enthusiasts aiming to learn how ML systems work beyond model training Anyone preparing for real-world ML interviews, startups, or enterprise-level ML deployment Homepage: https://www.udemy.com/course/mlops-real-world-machine-learning-projects-for-professional/ AusFile https://ausfile.com/awy6m20pemvj/zmigm.MLOps.RealWorld.Machine.Learning.Projects.for.Professional.part1.rar.html https://ausfile.com/bufp2tnp5uzx/zmigm.MLOps.RealWorld.Machine.Learning.Projects.for.Professional.part3.rar.html https://ausfile.com/nrpxayt11p0y/zmigm.MLOps.RealWorld.Machine.Learning.Projects.for.Professional.part2.rar.html Rapidgator Links Download https://rg.to/file/853191a669f4a10485ccb347f9d6ab57/zmigm.MLOps.RealWorld.Machine.Learning.Projects.for.Professional.part3.rar.html https://rg.to/file/a5c39a747db9afb0ad25d63b4aaaca03/zmigm.MLOps.RealWorld.Machine.Learning.Projects.for.Professional.part1.rar.html https://rg.to/file/da95042b9ccfc07e851b0b828e8070a7/zmigm.MLOps.RealWorld.Machine.Learning.Projects.for.Professional.part2.rar.html Fikper Links Download https://fikper.com/2I0Y0gLniQ/zmigm.MLOps.RealWorld.Machine.Learning.Projects.for.Professional.part1.rar.html https://fikper.com/8CSFfJNyye/zmigm.MLOps.RealWorld.Machine.Learning.Projects.for.Professional.part3.rar.html https://fikper.com/gwGk95W1rd/zmigm.MLOps.RealWorld.Machine.Learning.Projects.for.Professional.part2.rar.html No Password - Links are Interchangeable