Znajdź zawartość

Free Download Udemy - IoT Security and Predictive Analytics with Python Published: 4/2025 Created by: eTech School MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: All | Genre: eLearning | Language: English | Duration: 146 Lectures ( 12h 50m ) | Size: 5.9 GB Secure, analyze, and optimize IoT systems with cutting-edge techniques in security and predictive analytics! What you'll learn Fundamentals of IoT security and threat landscapes Secure device provisioning, authentication, and firmware development Network security best practices for IoT environments Secure communication protocols and encryption techniques Sensor data integration, storage, and privacy-preserving methods Hands-on projects for real-time monitoring and data analysis Predictive analytics using Python and advanced machine learning models Requirements Basic understanding of networking and security principles Familiarity with Python programming Knowledge of data analysis concepts is a plus but not mandatory Description Introduction:The Internet of Things (IoT) is revolutionizing industries with smart devices and connected technologies. However, as IoT ecosystems expand, so do the security threats and challenges associated with managing vast networks of devices and data. This course, Mastering IoT Security and Predictive Analytics with Python, is designed to equip you with the essential skills to secure IoT infrastructures, perform advanced data analytics, and build predictive models. Through a comprehensive curriculum covering IoT security fundamentals, device and network security, secure firmware development, and predictive analysis techniques using Python, you'll gain the expertise to handle real-world IoT security and data analysis challenges.Module 1: Introduction to IoT Security FundamentalsThis module sets the stage by introducing the core concepts of IoT security. You'll explore the unique security challenges in IoT environments, understand the threat landscape, and learn about common vulnerabilities in IoT systems. The section also covers security standards and frameworks that help in building secure IoT architectures, providing a solid foundation for the rest of the course.Module 2: Securing IoT DevicesSecuring individual IoT devices is crucial for protecting the entire ecosystem. This module focuses on secure device provisioning, authentication mechanisms, and strategies for robust communication channels. You'll learn best practices for securing firmware, protecting data on IoT devices, and ensuring device integrity through secure storage and management techniques.Module 3: Network Security for IoTNetwork security is a backbone of IoT infrastructure. Here, you'll dive into secure network architectures, wireless communication security, and network segmentation strategies. The module also addresses securing IoT gateways, implementing strong access controls, and ensuring safe data transmission across connected devices.Module 4: Application Security in IoTApplications are the interface between users and IoT systems, making them prime targets for attacks. This module covers secure design principles for IoT applications, data integrity, privacy measures, and secure API integrations. You'll also explore security testing methodologies and vulnerability assessments to strengthen IoT applications against potential threats.Module 5: Secure Hardware Design for IoT DevicesHardware security ensures the physical and logical protection of IoT devices. This module explores hardware security modules (HSMs), secure boot processes, and techniques to counter side-channel attacks. You'll also learn about tamper resistance strategies and the importance of physical security in IoT device design.Module 6: Secure Firmware DevelopmentFirmware is the software embedded in IoT devices, and securing it is vital. This section focuses on secure coding practices, memory protection, and secure bootloaders. You'll also learn about firmware signing, integrity verification, and strategies to protect against reverse engineering and tampering.Module 7: Secure Communication in IoT DevicesSecure communication protocols are key to maintaining data confidentiality and integrity. This module covers cryptographic techniques, secure protocols, key exchange mechanisms, and best practices for over-the-air (OTA) updates. You'll also learn how to manage device identities and certificates securely.Module 8: Secure Sensor Integration and Data ProcessingSensors are the data sources in IoT systems. This module focuses on secure data acquisition, anonymization techniques, and secure storage practices. You'll explore privacy-preserving data processing methods to ensure that sensitive information remains protected throughout its lifecycle.Module 9: Securing IoT Cloud Services and PlatformsWith the rise of cloud-based IoT solutions, securing cloud services is critical. This module covers security considerations for IoT cloud platforms, access controls, identity management, and secure data storage. You'll also explore auditing and monitoring techniques to maintain robust security in cloud environments.Module 10: Problem-Solving with IoTIn this hands-on module, you'll apply your knowledge to real-world scenarios. You'll learn how to collect, preprocess, and analyze data from IoT devices. This includes time-series analysis, building machine learning models, detecting anomalies, and creating real-time dashboards to monitor IoT systems effectively.Module 11: Predictive Analysis Using IoTPredictive analytics can transform IoT data into actionable insights. This module covers exploratory data analysis, feature engineering, and time-series analysis techniques. You'll implement predictive algorithms, explore reinforcement learning in IoT applications, and optimize predictive maintenance schedules using advanced models.Module 12: Real-World IoT-Based ProgramsThis final module bridges theory with practice through a series of real-world projects. You'll develop programs to monitor environmental conditions, optimize traffic flow, track health parameters, and more. These projects will reinforce your learning and demonstrate the practical applications of IoT security and predictive analytics.Conclusion:By the end of this course, you'll have a comprehensive understanding of IoT security, secure device and network design, and advanced predictive analytics techniques using Python. You'll be equipped to tackle real-world challenges, secure IoT environments, and derive meaningful insights from data. Whether you're an aspiring IoT security professional, data analyst, or IoT developer, this course will empower you to excel in the fast-evolving world of IoT. Who this course is for IoT developers and engineers Cybersecurity professionals looking to specialize in IoT Data analysts interested in predictive analytics with IoT data Students preparing for certifications in IoT security or data analytics Homepage: https://www.udemy.com/course/iot-security-and-predictive-analytics-with-python/ [b]AusFile[/b] https://ausfile.com/5dqt4hgmuutk/ndkpp.IoT.Security.and.Predictive.Analytics.with.Python.part1.rar.html https://ausfile.com/6e2xjjsl4qjz/ndkpp.IoT.Security.and.Predictive.Analytics.with.Python.part3.rar.html https://ausfile.com/97f9kgq1hhv0/ndkpp.IoT.Security.and.Predictive.Analytics.with.Python.part4.rar.html https://ausfile.com/fsyo73svkj9y/ndkpp.IoT.Security.and.Predictive.Analytics.with.Python.part6.rar.html https://ausfile.com/q804ccwondw2/ndkpp.IoT.Security.and.Predictive.Analytics.with.Python.part7.rar.html https://ausfile.com/trt9tgp3o5zv/ndkpp.IoT.Security.and.Predictive.Analytics.with.Python.part5.rar.html https://ausfile.com/yb5x3wo5q6os/ndkpp.IoT.Security.and.Predictive.Analytics.with.Python.part2.rar.html Rapidgator https://rg.to/file/0a7dd097406408d28b1545feaaeb65a4/ndkpp.IoT.Security.and.Predictive.Analytics.with.Python.part3.rar.html https://rg.to/file/39ec31e6cd8fe928461f41f5d8c0c76b/ndkpp.IoT.Security.and.Predictive.Analytics.with.Python.part1.rar.html https://rg.to/file/3a203423cebef8d660325dabdc4383d4/ndkpp.IoT.Security.and.Predictive.Analytics.with.Python.part5.rar.html https://rg.to/file/5d5f4529062b65146a7a7b934ecaa6d1/ndkpp.IoT.Security.and.Predictive.Analytics.with.Python.part4.rar.html https://rg.to/file/6b879d221766e217b78ca3bb89920c4b/ndkpp.IoT.Security.and.Predictive.Analytics.with.Python.part7.rar.html https://rg.to/file/8a364a41921747007c1e683776a67d4c/ndkpp.IoT.Security.and.Predictive.Analytics.with.Python.part2.rar.html https://rg.to/file/c7b9aadc72c39466885235ad42da8843/ndkpp.IoT.Security.and.Predictive.Analytics.with.Python.part6.rar.html Fikper Free Download https://fikper.com/24rYrWBemF/ndkpp.IoT.Security.and.Predictive.Analytics.with.Python.part2.rar.html https://fikper.com/KRkgWbAMuO/ndkpp.IoT.Security.and.Predictive.Analytics.with.Python.part6.rar.html https://fikper.com/PhccVVrLeQ/ndkpp.IoT.Security.and.Predictive.Analytics.with.Python.part4.rar.html https://fikper.com/Z8QrhlxPCM/ndkpp.IoT.Security.and.Predictive.Analytics.with.Python.part1.rar.html https://fikper.com/eujgIr3BCh/ndkpp.IoT.Security.and.Predictive.Analytics.with.Python.part5.rar.html https://fikper.com/iZVp303idi/ndkpp.IoT.Security.and.Predictive.Analytics.with.Python.part3.rar.html https://fikper.com/pLQfv0omI4/ndkpp.IoT.Security.and.Predictive.Analytics.with.Python.part7.rar.html No Password - Links are Interchangeable

Free Download Udemy - GenAI and Predictive AI Architecture Published: 3/2025 Created by: Edcorner Learning MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: Expert | Genre: eLearning | Language: English | Duration: 25 Lectures ( 3h 22m ) | Size: 1 GB Explore The Generative AI & Predictive AI Architectures, Core Components, Layers, Model Types & Use Cases What you'll learn Foundations of Generative AI & Predictive AI Understand the key components and layers within Generative AI and Predictive AI architectures. Learn the differences between traditional AI, Generative AI, and Predictive AI and how to select the right approach. Analyze enterprise AI architecture and its application Explore various types of Generative AI models, including GANs, VAEs, Transformers, and Diffusion models. Discover the best practices for using Generative AI in business applications. Compare Conversational AI vs. Generative AI to understand chatbot and AI assistant implementations. Explore over 40+ real-world use cases of Generative AI across industries. Evaluate the top Generative AI tools and platforms, including OpenAI's GPT models, Google's Bard, Stability AI, and more. Learn about popular Generative AI models and their key differentiators. Understand the differences between Large Language Models (LLMs) and Generative AI and when to use each. Learn the layers of Predictive AI architecture and how data is processed for forecasting and decision-making. Explore various Predictive AI models, such as regression models, decision trees, neural networks, and time series forecasting. Understand how Predictive AI works, including data ingestion, model training, and prediction generation. Gain knowledge on how to implement Predictive AI in an organization Compare Generative AI vs. Predictive AI applications to determine their respective strengths in different scenarios. Explore the Generative AI Monitoring Architecture Learn how Predictive AI Monitoring Architecture works to enhance model accuracy, reduce drift, and optimize decision-making. Requirements Basic Knowledge of AI No advanced programming knowledge is required Description The rapid advancements in artificial intelligence (AI) have led to the rise of two transformative branches: Generative AI and Predictive AI. This comprehensive course explores their architectural foundations, key components, and practical applications in enterprise environments. Designed for AI professionals, data scientists, and business leaders, this course provides a deep dive into how these two AI paradigms work, their unique advantages, and their role in shaping the future of automation and decision-making.The course begins with an in-depth exploration of Generative AI Architecture & Key Components, where learners will understand the essential layers within Generative AI and how various models, such as GANs, VAEs, and diffusion models, generate new content. We will examine Types of Generative AI Models and their outputs, followed by discussions on best practices for leveraging Generative AI effectively in different domains. A comparative analysis of Traditional AI vs. Generative AI and Conversational AI vs. Generative AI will provide clarity on when to adopt these technologies. Enterprise implementation strategies will be covered in Enterprise Generative AI Architecture Layers & Components, along with real-world examples of Top 40+ Generative AI Use Cases and the Top 7 Most Popular Generative AI Tools and Platforms.Moving to Predictive AI, the course explores Predictive AI Architecture, including its layers and models, and delves into how Predictive AI works in real-world applications. We will discuss differences in architecture, purpose, and implementation compared to Generative AI, helping professionals make informed decisions when deploying AI solutions. Practical sessions on implementing Predictive AI in organizations will guide learners through real-world case studies.Finally, the course examines AI monitoring frameworks, focusing on Generative AI Monitoring Architecture and Predictive AI Monitoring Architecture to ensure AI systems remain efficient, ethical, and reliable. By the end of this course, parti[beeep]nts will have a robust understanding of how to choose between Large Language Models (LLMs) and Generative AI, as well as the fundamental distinctions between Generative AI and Predictive AI applications. Who this course is for AI & Machine Learning Professionals Data Scientists & Analysts AI engineers, data scientists, and ML practitioners AI developers Data analysts C-level executives, AI strategists, and product managers Innovation leaders seeking to integrate Generative AI and Predictive AI into business models Professionals responsible for AI adoption and governance within enterprises. Software Engineers & AI Developers Academic Researchers & AI Enthusiasts Homepage: https://www.udemy.com/course/genai-and-predictive-ai-architecture/ [b]AusFile[/b] https://ausfile.com/7azke1x39y7v/bksfe.GenAI.and.Predictive.AI.Architecture.part1.rar.html https://ausfile.com/k7pom47ob02r/bksfe.GenAI.and.Predictive.AI.Architecture.part2.rar.html Rapidgator https://rg.to/file/5cd1af9c2d12c75cc9852a5b0f7bcde5/bksfe.GenAI.and.Predictive.AI.Architecture.part1.rar.html https://rg.to/file/694c1673ac661fc3c3d1ee3a96ca1d90/bksfe.GenAI.and.Predictive.AI.Architecture.part2.rar.html Fikper Free Download https://fikper.com/2APFoReZSM/bksfe.GenAI.and.Predictive.AI.Architecture.part1.rar.html https://fikper.com/cDpmIGPqeM/bksfe.GenAI.and.Predictive.AI.Architecture.part2.rar.html No Password - Links are Interchangeable

Released: 02/2025 Free Download Duration: 1h 47m | .MP4 1280x720, 30 fps(r) | AAC, 48000 Hz, 2ch | 260 MB Level: Intermediate | Genre: eLearning | Language: English Organizations in nearly every industry are seeking and hiring data scientists, but even though data analytics skills are highly valued, individuals with this skill set can't make an impact unless middle and senior management know how to leverage analytics for the long-term benefit of their organization. The challenge is that most of the people overseeing advanced analytics don't have backgrounds in data science themselves. In this course, Keith McCormick shows executives who aren't fluent in data analytics how to hire data science professionals, manage data science teams, and transform their business with effectively deployed advanced analytics. Learn how to actively parti[beeep]te in a discussion about which type of analytics may address your business problem, have a better appreciation of problem-solving from a data scientist's point of view, think strategically about hiring and technology for advanced analytics, and consider various options for organizational structure and the enterprise-wide management of analytics. Homepage: https://www.linkedin.com/learning/predictive-analytics-essential-training-for-executives-25301424 Rapidgator https://rg.to/file/901dc69a1e5d7340dcd561f2b2633bc5/hbcul.Predictive.Analytics.Essential.Training.for.Executives.2025.rar.html Fikper Free Download https://fikper.com/gNKjvzCKlK/hbcul.Predictive.Analytics.Essential.Training.for.Executives.2025.rar.html : No Password - Links are Interchangeable

Free Download Predictive Customer Analytics (2024) Published 10/2024 Created by Start-Tech Academy MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 29 Lectures ( 3h 23m ) | Size: 1.68 GB Build predictive machine learning and forecasting models in Excel to build customer decision and customer behavior What you'll learn Discover how to preprocess customer data for predictive modeling using Excel. Master the application of linear regression in Excel to predict customer behavior. Explore the use of logistic regression for customer churn prediction and retention strategies. Analyze customer data using clustering techniques to segment customer groups. Build sales forecasting models using Excel's Solver and time series analysis. Implement XLSTAT for advanced statistical analysis in customer predictions. Develop and run logistic regression models using Excel Macros for automation. Predict future customer behavior with additive and multiplicative time series models. Interpret the results of regression and clustering models to make actionable business decisions. Evaluate the effectiveness of your predictive models in improving customer retention and business strategies. Requirements A PC/ laptop with good internet connection and MS Excel installed on it Description Are you an aspiring data analyst or business professional looking to make data-driven decisions that impact customer behavior and retention? Do you want to leverage Excel to build predictive models without the complexity of advanced coding? If yes, this course is for you.In today's competitive market, understanding customer behavior is key to business success. Predictive Customer Analytics helps you stay ahead by forecasting customer decisions, improving retention, and driving targeted marketing strategies. This course will empower you to use Excel as a powerful tool for building predictive machine learning models and forecasting techniques, even if you're not an expert in data science.In this course, you will:Develop a solid understanding of linear and logistic regression techniques in Excel to predict customer behavior.Master clustering techniques for customer segmentation, identifying key groups within your customer base.Build sales forecasting models using Excel's Solver and time series methods.Implement real-world solutions with case studies, such as predicting customer churn and segmenting customers for better marketing strategies.Why is Predictive Customer Analytics so important? By using Excel, a tool most professionals are already familiar with, you can unlock deeper insights into customer data, enabling better decision-making without needing advanced technical skills. From forecasting sales trends to retaining key customers, predictive analytics is a game-changer for businesses looking to grow and scale.Throughout the course, you will complete hands-on exercises in Excel, including:Preprocessing customer data for linear and logistic regressionBuilding predictive models using XLSTAT and Excel MacrosClustering customer data for segmentation analysisImplementing time series forecasting to predict salesWhat sets this course apart is its focus on practical, easy-to-implement techniques that don't require programming knowledge. You'll learn how to utilize Excel's advanced features to get accurate, actionable results quickly.Ready to transform your customer insights? Enroll today and start building your own predictive models in Excel! Who this course is for Marketing professionals who want to use data to predict customer behavior and enhance targeted campaigns. Sales managers looking to forecast sales trends and improve customer retention strategies. Data analysts who want to build predictive models in Excel without needing complex coding skills. Small business owners aiming to make data-driven decisions to optimize customer acquisition and retention. Homepage https://www.udemy.com/course/predictive-customer-analytics/ Screenshot Rapidgator https://rg.to/file/22f5ee0eb74995ff51792cdfb7a2082e/hmaze.Predictive.Customer.Analytics.2024.part1.rar.html https://rg.to/file/34e18dce1ed2acff4d9463783b285b44/hmaze.Predictive.Customer.Analytics.2024.part2.rar.html Fikper Free Download https://fikper.com/7QwAECQ5io/hmaze.Predictive.Customer.Analytics.2024.part2.rar.html https://fikper.com/Q16PHN9U1L/hmaze.Predictive.Customer.Analytics.2024.part1.rar.html No Password - Links are Interchangeable

Free Download Excel Predictive Analytics, Automation, and AI - No-Code Published 10/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Language: English | Duration: 3h 57m | Size: 1.82 GB Microsoft Excel Predictive Analytics, Automation, and AI Tools: The Complete No-Code Guide for Every Professional What you'll learn Master Predictive Analytics Using Excel Students will learn how to use Excel's built-in tools and functions, such as LINEST and TREND Automate Repetitive Tasks with Macros and VBA Learners will gain the skills to create and implement Excel macros and basic VBA to automate repetitive tasks Apply Advanced Forecasting Techniques Students will explore and apply advanced forecasting methods, such as exponential smoothing to predict trends Learners will develop dynamic, interactive dashboards using PowerPivot, Power BI, and Excel's advanced charting tools to present insights effectively. Requirements Basic Understanding of Excel Students should have a basic familiarity with Excel, including knowledge of standard functions like SUM, AVERAGE, and simple cell formatting. Access to Excel Software Learners will need access to Microsoft Excel (preferably version 2021 or Excel 365) to follow along with the exercises and practical applications. No Coding Experience Required No prior programming or coding knowledge is needed, as this course focuses on no-code solutions for predictive analytics and automation using Excel. A Desire to Learn Predictive Analytics While no advanced math or analytics background is required, students should be motivated to explore how data-driven decisions can be made using Excel's tools. Description Here's a course description for Excel Predictive Analytics, Automation, and AI : No-Code.Unlock the full potential of Excel with this comprehensive course designed to take you from the basics of predictive analytics to advanced forecasting, automation, and data visualization-all without writing a single line of code.In this course, you'll learn how to use Excel's powerful tools to analyze data, make accurate predictions, and automate repetitive tasks, allowing you to save time and drive smarter business decisions. From forecasting sales trends to building dynamic dashboards, this course is packed with practical, real-world applications that you can start using immediately.What You'll Learn:Master predictive analytics and forecasting using Excel's built-in functions like LINEST, TREND, and Forecast.ETS.Automate repetitive tasks using Excel macros and VBA to boost efficiency and save time.Create dynamic, interactive dashboards with PowerPivot and Power BI, visualizing key business insights with advanced charts and slicers.Leverage AI tools like ChatGPT to enhance your Excel workflows and improve decision-making.This course is perfect for business professionals, analysts, small business owners, and Excel users looking to take their skills to the next level by integrating automation and data-driven decision-making into their work.No coding required-just a desire to improve your Excel skills and become an expert in predictive analytics and automation! Who this course is for Business Professionals Anyone working in business roles such as finance, marketing, sales, or operations who needs to make data-driven decisions and improve their forecasting capabilities without relying on complex coding. Analysts and Data Enthusiasts People in data analysis roles or those interested in learning practical, no-code methods for performing predictive analytics and creating powerful data visualizations using Excel. Small Business Owners and Entrepreneurs Entrepreneurs looking to optimize their business performance by forecasting sales, automating tasks, and gaining insights from their data using easy-to-understand tools in Excel. Excel Users Seeking Advanced Skills Intermediate Excel users who want to elevate their skills by learning advanced Excel features such as macros, Solver, PowerPivot, and data visualization techniques to boost their productivity and analysis capabilities. Homepage https://www.udemy.com/course/excel-predictive-analytics-automation-and-ai-no-code/ Rapidgator https://rg.to/file/c7dee0a270578da0362ff9186c3fabfe/hnpuy.Excel.Predictive.Analytics.Automation.and.AI..NoCode.part2.rar.html https://rg.to/file/f8a89bb8fb3e8dfd7f976bb03bf97676/hnpuy.Excel.Predictive.Analytics.Automation.and.AI..NoCode.part1.rar.html Fikper Free Download https://fikper.com/7qgOFaU5Es/hnpuy.Excel.Predictive.Analytics.Automation.and.AI..NoCode.part1.rar.html https://fikper.com/Tmxd1AsG1a/hnpuy.Excel.Predictive.Analytics.Automation.and.AI..NoCode.part2.rar.html No Password - Links are Interchangeable

Free Download Udemy - Biomarkers 2.0 Predictive Power With Ai Published 10/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English | Size: 857.48 MB | Duration: 0h 0m This 1-2 hour workshop introduces parti[beeep]nts to AI's transformative role in biomarker discovery. What you'll learn Advanced Introduction to AI-Driven Biomarkers AI's Role in Predictive Biomarkers for Complex Diseases Deep Dive into AI Models for Biomarker Identification Data Management, Model Interpretation, and Ethical Considerations Future Directions in AI-Enhanced Biomarker Research Requirements No prior experience in AI or biomarker research is necessary to enroll in this workshop. We welcome parti[beeep]nts from diverse backgrounds, including those new to the field. This workshop is designed to provide a supportive learning environment where foundational concepts will be introduced and built upon throughout the course. Join us to enhance your understanding and skills in this exciting and rapidly evolving area of research! Description AimTo equip PhD scholars and academicians with advanced skills in AI-driven biomarker discovery. This workshop focuses on the role of AI in identifying predictive biomarkers for complex diseases such as cancer, cardiovascular, and neurological disorders, emphasizing emerging research trends, AI models, and ethical implications.Workshop ObjectivesUnderstand the integration of AI in biomarker discovery.Analyze AI models for predicting complex diseases.Learn model validation techniques for AI-driven biomarkers.Address ethical and data challenges in AI biomarker research.Explore AI applications in precision medicine and translational research.Workshop StructureModule 1: Advanced Introduction to AI-Driven BiomarkersIntroduction to Biomarkers: Classical Methods vs. AI IntegrationOverview of traditional biomarker discovery methodsIntroduction to AI's role in transforming biomarker discoveryHistorical perspective of biomarker discoveryThe rise of AI in predictive biomarker developmentModule 2: AI's Role in Predictive Biomarkers for Complex DiseasesTheoretical exploration of machine learning (ML) and deep learning (DL) techniquesCase studies on AI-based biomarkers in complex diseases (Cancer, Cardiovascular, Neurological)Journal reviews on AI-driven biomarkersCase studies from recent research in the fieldModule 3: Deep Dive into AI Models for Biomarker IdentificationAccuracy, precision, and generalizability of AI in biomarker discoveryTheoretical exploration of validation techniquesLarge-scale omics data handlingAI's role in data preprocessing, feature selection, and overcoming challengesModule 4: Data Management, Model Interpretation, and Ethical ConsiderationsAI's role in minimizing overfitting and biasChallenges in interpretability and transparency in biomarker modelsTheoretical frameworks on ethical and legal considerationsResponsible use of AI in healthcare biomarker researchModule 5: Future Directions in AI-Enhanced Biomarker ResearchThe role of AI in the development of precision medicine biomarkersTranslational research and its importance in healthcareAI's contribution to systems biology and biomarker discoveryParti[beeep]nt's EligibilityAI researchers, bioinformaticians, medical researchers, healthcare professionals, and academic scholars.Workshop OutcomesMaster AI techniques for identifying predictive biomarkers.Learn to apply ML and DL models in biomarker research.Handle and preprocess large-scale biological datasets.Explore case studies in cancer, cardiovascular, and neurological research.Address ethical challenges and apply AI models responsibly. Overview Section 1: Introduction Lecture 1 Advanced Introduction to AI-Driven Biomarkers Lecture 2 AI's Role in Predictive Biomarkers for Complex Diseases Lecture 3 Deep Dive into AI Models for Biomarker Identification Lecture 4 Data Management, Model Interpretation, and Ethical Considerations Lecture 5 Future Directions in AI-Enhanced Biomarker Research Lecture 6 Ethical & Legal Challenges Module Lecture 7 Future Directions in AI Module AI researchers, bioinformaticians, medical researchers, healthcare professionals, and academic scholars. Homepage https://www.udemy.com/course/biomarkers-20-predictive-power-with-ai/ Rapidgator https://rg.to/file/58d18ec4754925497ddf58cf10c7e5e1/yevlc.Biomarkers.2.0.Predictive.Power.With.Ai.rar.html Fikper Free Download https://fikper.com/mzhasgRiau/yevlc.Biomarkers.2.0.Predictive.Power.With.Ai.rar No Password - Links are Interchangeable

Free Download Driving Digital Transformation with Generative AI and Predictive AI Technologies Released 9/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Skill Level: Intermediate | Genre: eLearning | Language: English + srt | Duration: 1h 9m | Size: 170 MB Digital transformation has been redefined and reshaped with the adoption of enhanced, modern generative AI and predictive AI technologies. Join LinkedIn Top Voice and best-selling author Thomas Erl as he explores the creative ways in which generative AI and predictive AI features and functions can be incorporated into digital transformations to help organizations better plan, design, deliver and work with contemporary, sophisticated digital business solutions. Learn how incorporating AI can help automate complex tasks, elevate customer experiences, promote product innovation, and more. Also, gain valuable insights into the significant risks and challenges that come with increased AI adoption within digital transformations, and how to avoid them. This course is for anyone interested in understanding how advancements in AI have evolved the practice of digital transformation, how to realize their benefit potential, and how to evade associated pitfalls. Homepage https://www.linkedin.com/learning/driving-digital-transformation-with-generative-ai-and-predictive-ai-technologies TakeFile https://takefile.link/wkng76hsoiq3/kuwjm.Driving.Digital.Transformation.with.Generative.AI.and.Predictive.AI.Technologies.rar.html Rapidgator https://rg.to/file/1560fd4af2374f3c94aecd22ef2a80f2/kuwjm.Driving.Digital.Transformation.with.Generative.AI.and.Predictive.AI.Technologies.rar.html Fikper Free Download https://fikper.com/hHTTgpklj1/kuwjm.Driving.Digital.Transformation.with.Generative.AI.and.Predictive.AI.Technologies.rar.html No Password - Links are Interchangeable

Free Download GenAI and Predictive AI Architecture Foundations Released: 09/2024 Duration: 1h 16m | .MP4 1280x720, 30 fps(r) | AAC, 48000 Hz, 2ch | 157 MB Level: Intermediate | Genre: eLearning | Language: English Get a clear understanding of how AI solutions actually work, starting with their basic architectures and advancing to system components and architectural differences between generative AI and predictive AI systems. Join LinkedIn Top Voice and best-selling author Thomas Erl as he breaks down and explains, in plain English, the foundational building blocks and the primary moving parts behind contemporary AI solution environments. This course provides genuine insight into how AI systems function in the real world, and is essential for professionals already working with application and enterprise architectures, as well as professionals in the AI and data science fields. Homepage https://www.linkedin.com/learning/genai-and-predictive-ai-architecture-foundations?u=121350530 TakeFile https://takefile.link/ng378cbfiiuv/wmlwn.GenAI.and.Predictive.AI.Architecture.Foundations.rar.html Rapidgator https://rg.to/file/96dcdb1f7b91625374811c94b2392617/wmlwn.GenAI.and.Predictive.AI.Architecture.Foundations.rar.html No Password - Links are Interchangeable