Znajdź zawartość

Free Download Udemy - Testing Aiml Models Published 10/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 849.76 MB | Duration: 1h 59m QA Pros, SDETs, and Functional Testers-Add AI/ML Testing to Your Skill Set ! ! What you'll learn Upon completing this course, QA , SDET, QA Test Automation professionals will be equipped to Effectively Test and Validate ML Models. Implement ML-Specific API Testing and Automation. Monitor and Manage Models for Ongoing Quality Assurance Ensure Responsible and Ethical AI through Rigorous Testing Requirements This course is designed to be accessible to both beginners and experienced QA professionals looking to expand their expertise into AI and ML testing. To get the most out of this course, here are a few helpful (but not mandatory) prerequisites Basic Understanding of Software Testing Principles. Interest in Machine Learning Concepts. No prior experience with ML is necessary, but a curiosity about how machine learning models work will enhance your learning experience. Familiarity with Testing Tools (Preferred but Not Required) A Laptop or Computer for Hands-On Practice. Description Course Highlights:Manage the end-to-end lifecycle of ML models. Gain the skills to perform functional, early-stage, and post-deployment testing of ML models.Catch issues early with proactive, early-stage testing.Define and automate API testing for ML models. Learn how to design, and automate API testing for machine learning models.Master functional testing to validate model accuracy and behaviour.Uphold responsible AI through dedicated testing methods. Apply ethical testing techniques to identify biases, ensure model transparency, and uphold fairness in ML applications.Set up continuous quality assurance to monitor live ML models.Why This Course?ML Testing Skills Are in High Demand - Be among the few QA specialists with this expertiseStay Relevant in the Evolving Tech Landscape - AI/ML testing isn't just an edge; it's essentialTransform Your Career Prospects - Add powerful, sought-after skills to your profile!Did you know? As AI and ML continue to transform industries, the need for QA professionals who understand how to test ML models is skyrocketing. Traditional QA methods don't cover ML's complexities like data drift, model accuracy, and ethical concerns, making ML testing a must-have skill for any QA professional looking to stay relevant and impactful.If you're looking to add one of the most in-demand skills to your QA skillset, then this course is a must! Overview Section 1: Basics of Machine Learning Model Lecture 1 Intro to AI system. Lecture 2 Introduction and agenda of tutorial. Lecture 3 What is Machine Learning and approaches of machine learning. Section 2: Early Testing in ML Model Modelling or Engineering Phase. Lecture 4 ML Model Lifecycle Offline and Online Modes. Lecture 5 Important Terminologies in ML Model Testing. Lecture 6 Demo- Predicting Energy output of power plant(ML Model). Lecture 7 Supervised Learning and Hyperparamters. Section 3: Unsupervised Learning Models Testing in Modelling phase Lecture 8 Unsupervised learning and types. Section 4: Reinforcement Learning in ML models Lecture 9 Reinforcement Learning in ML models with examples Lecture 10 Top Python Libraries for ML Models. Section 5: Functional Testing of AIML model in Evaluation phase. Lecture 11 Temperature Testing to fine tune the AIML model response Lecture 12 Zero shot prompting Testing. Lecture 13 Chain of Thought Prompting Testing. Lecture 14 Repeatability and Context Management Testing. Section 6: API Automation Lecture 15 Download Postman, create Google Gemini Account and setup env for testing. Lecture 16 PostBot Pluggin to generate automation script for Model API Response. Section 7: Responsible AI Testing with examples. Lecture 17 Responsible AI and Fairness and Bias Detection Testing. Lecture 18 Transparency and Ethical Testing. Lecture 19 Data Security and Privacy Testing. Lecture 20 Societal Impact Testing. Section 8: Post Deployment Testing of AIML Models. Lecture 21 Latency and Drift Testing. Lecture 22 Shadow Testing and Canary Testing of AIML Model. Section 9: ThankYou!! Lecture 23 Thank You Note!! This course is tailored for QA professionals, SDETs, Data Analysts, and anyone involved in quality assurance who wants to expand their skills into the exciting field of AI and Machine Learning (ML) testing. The course content is designed to help you bridge the gap between traditional software testing and the specialized needs of ML model validation, making it valuable for:,Quality Assurance (QA) Engineers looking to enhance their testing toolkit with skills specific to AI/ML model reliability, functionality, and fairness.,Software Development Engineers in Test (SDETs) aiming to stay ahead of the curve by learning how to automate and monitor ML model testing processes.,Functional and Automation Testers interested in developing new testing strategies for ML models and ensuring their robust performance across different environments.,Data Analysts and ML Enthusiasts who want to learn the testing practices that can ensure model accuracy and compliance in production settings. Screenshot Homepage https://www.udemy.com/course/testing-aiml-models/ Rapidgator https://rg.to/file/07e93432425c0557ac9abc428f6d64bb/nzhfb.Testing.Aiml.Models.rar.html Fikper Free Download https://fikper.com/aGkihzi80d/nzhfb.Testing.Aiml.Models.rar.html No Password - Links are Interchangeable

Free Download Application of Data Science for Data Scientists - AIML TM Published 9/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Language: English | Duration: 8h 32m | Size: 4.06 GB Mastering Real-World Data Science Applications and Techniques for Advanced Problem Solving What you'll learn Students will learn the fundamentals of Data Science and its applications across various industries. Students will explore key algorithms and perform exploratory data analysis (EDA). Students will learn about the roles, skills, and responsibilities of a Data Scientist. Students will dive into advanced techniques and practical applications used by Data Scientists. Students will learn the stages of the Data Science process, from problem definition to data collection. Students will explore model building, evaluation, deployment, and post-deployment strategies. Students will apply Data Science concepts to solve a real-world case study from start to finish. Students will learn how to ensure data quality and make their models interpretable. Students will explore the ethical considerations and responsibilities involved in Data Science. Students will examine the ethical dilemmas surrounding data collection, privacy, and bias. Students will understand how to manage and execute a Data Science project from planning to reporting. Students will learn techniques for selecting and engineering relevant features to improve model performance. Students will explore how to implement and scale Data Science solutions in real-world applications. Students will master data wrangling and manipulation techniques to efficiently handle large datasets. Requirements Anyone can learn this class it is very simple. Description 1. Introduction to Data ScienceOverview of what Data Science isImportance and applications in various industriesKey components: Data, Algorithms, and InterpretationTools and software commonly used in Data Science (e.g., Python, R)2. Data Science Session Part 2Deeper dive into fundamental conceptsKey algorithms and how they workExploratory Data Analysis (EDA) techniquesPractical exercises: Building first simple models3. Data Science Vs Traditional AnalysisDifferences between traditional statistical analysis and modern Data ScienceAdvantages of using Data Science approachesPractical examples comparing both approaches4. Data Scientist Part 1Role of a Data Scientist: Core skills and responsibilitiesKey techniques a Data Scientist uses (e.g., machine learning, data mining)Introduction to model building and validation5. Data Scientist Part 2Advanced techniques for Data ScientistsWorking with Big Data and cloud computingBuilding predictive models with real-world datasets6. Data Science Process OverviewSteps of the Data Science process: Problem definition, data collection, preprocessingBest practices in the initial phases of a Data Science projectExamples from industry: Setting up successful projects7. Data Science Process Overview Part 2Model building, evaluation, and interpretationDeployment of Data Science models into productionPost-deployment monitoring and iteration8. Data Science in Practice - Case StudyHands-on case study demonstrating the Data Science processProblem-solving with real-world dataStep-by-step guidance from data collection to model interpretation9. Data Science in Practice - Case Study: Data Quality & Model InterpretabilityImportance of data quality and handling missing dataTechniques for ensuring model interpretability (e.g., LIME, SHAP)How to address biases in your model10. Introduction to Data Science EthicsImportance of ethics in Data ScienceHistorical examples of unethical Data Science practicesGuidelines and frameworks for ethical decision-making in Data Science11. Ethical Challenges in Data Collection and CurationChallenges in ensuring ethical data collection (privacy concerns, data ownership)Impact of biased or incomplete dataHow to approach ethical dilemmas in practice12. Data Science Project LifecycleOverview of a complete Data Science project lifecycleManaging each phase: Planning, execution, and reportingTeam collaboration and version control best practices13. Feature Engineering and SelectionTechniques for selecting the most relevant featuresDimensionality reduction techniques (e.g., PCA)Practical examples of feature selection and its impact on model performance14. Application - Working with Data ScienceHow to implement Data Science solutions in real-world applicationsCase studies of successful applications (e.g., fraud detection, recommendation systems)Discussion on the scalability and robustness of models15. Application - Working with Data Science: Data ManipulationTechniques for data wrangling and manipulationWorking with large datasets efficientlyUsing libraries like Pandas, NumPy, and Dask for data manipulationThis framework covers key aspects and ensures a deep understanding of Data Science principles with practical applications. Who this course is for Anyone who wants to learn future skills and become Data Scientist, Ai Scientist, Ai Engineer, Ai Researcher & Ai Expert. Homepage https://www.udemy.com/course/application-of-data-science-for-data-scientists-aiml-tm/ Rapidgator https://rg.to/file/be610bef93f4856c9439f48c913ceb6e/nagxs.Application.of.Data.Science.for.Data.Scientists..AIML.TM.part1.rar.html https://rg.to/file/cb4427e2b48dde8a9b4a3fdad7770e69/nagxs.Application.of.Data.Science.for.Data.Scientists..AIML.TM.part2.rar.html https://rg.to/file/23b01922b618e1a5a10d4c41506d19da/nagxs.Application.of.Data.Science.for.Data.Scientists..AIML.TM.part3.rar.html https://rg.to/file/f394295908e5bbabda452bfacffc63fa/nagxs.Application.of.Data.Science.for.Data.Scientists..AIML.TM.part4.rar.html https://rg.to/file/50f3dd1e9eae9cff7da917d787b64137/nagxs.Application.of.Data.Science.for.Data.Scientists..AIML.TM.part5.rar.html Fikper Free Download https://fikper.com/OsEw0Rk3NY/nagxs.Application.of.Data.Science.for.Data.Scientists..AIML.TM.part1.rar.html https://fikper.com/ExA7evxFgX/nagxs.Application.of.Data.Science.for.Data.Scientists..AIML.TM.part2.rar.html https://fikper.com/Jy5pnnhT7S/nagxs.Application.of.Data.Science.for.Data.Scientists..AIML.TM.part3.rar.html https://fikper.com/DfJeDWUG0s/nagxs.Application.of.Data.Science.for.Data.Scientists..AIML.TM.part4.rar.html https://fikper.com/HtaBZGzcj2/nagxs.Application.of.Data.Science.for.Data.Scientists..AIML.TM.part5.rar.html No Password - Links are Interchangeable