Oracle DB Tuning & Troubleshooting - Interpret an AWR Report

Unlock the Power of Kali Linux: Your Comprehensive Guide to Ethical Hacking and Penetration TestingWelcome to Kali Linux Unleashed Volume 1! Dive into the world of ethical hacking and penetration testing with this comprehensive course designed to give you the skills and knowledge needed to master Kali Linux, the most powerful tool for cybersecurity professionals.In this course, you will learn everything you need to know to become proficient in using Kali Linux for ethical hacking purposes. From the basics of Linux command-line usage to advanced penetration testing techniques, we cover it all. Whether you're a beginner or an experienced professional, this course will take your cybersecurity skills to the next level.What You'll Learn:Introduction to Kali Linux: Understand the fundamentals of Kali Linux, its features, and how to set up your environment.Linux Command-Line Essentials: Master the essential commands and utilities of the Linux command line.Ethical Hacking Basics: Learn the principles and ethics of ethical hacking, and understand the legal aspects of penetration testing.Information Gathering: Explore techniques for gathering information about targets and identifying vulnerabilities.Exploitation Techniques: Dive into the world of exploiting vulnerabilities, including buffer overflows, SQL injection, and more.Post-Exploitation: Learn how to maintain access to systems and escalate privileges after exploitation.Web Application Penetration Testing: Discover how to assess and secure web applications using Kali Linux tools.Wireless Network Penetration Testing: Explore techniques for assessing and securing wireless networks.Advanced Tools and Techniques: Master advanced tools and techniques for penetration testing and cybersecurity.Why Take This Course:Hands-O

“This course contains the use of artificial intelligence”We are entering a new era where software doesn’t just assist — it acts. In this transformational program, you will learn how to design, build, and monetize autonomous AI agents that can use tools, make decisions, simulate or process payments, and operate as independent digital economic actors.Welcome to the AI Agent Economy.This 14-day hero-to-zero masterclass is designed for builders, founders, product leaders, and innovators who want to go beyond chatbots and automation. You will architect production-ready agents with multi-step planning, tool integration, persistent memory, payment systems, and risk guardrails — the foundational primitives of modern digital economies.You won’t just learn theory. You will build.Throughout this course, you will:Understand the shift from SaaS to Agent-as-a-Service (AaaS)Implement LLM reasoning engines with structured tool callingBuild vector memory systems (RAG)Design multi-agent orchestration frameworksIntegrate payment APIs and revenue share modelsApply governance, logging, and traceabilityArchitect scalable infrastructure using serverless and event-driven systemsValidate product–market fitLaunch your own autonomous AI businessWe break down the economic primitives that power this ecosystem: identity, tools, memory, payments, governance, and pricing frameworks. You’ll understand how agents can transact, collaborate, earn revenue, and operate within bounded autonomy.By the end of this program, you will build a fully functi

Not all AI mistakes are the same. Knowing the difference can save you time, money, and headaches. This course gives you the skills to identify, debug, and prevent AI hallucinations and errors across different use cases, from natural language generation to coding assistants.We start with the fundamentals:What is an AI hallucination? How to detect fabricated facts, fake citations, and confident falsehoods.What is an AI error? How to spot faulty logic, outdated knowledge, and reproducible mistakes.Quick reality-check techniques to verify AI output before it causes harm.Best prompting strategies to reduce risk and improve accuracy.Then we move into AI code assistant errors:Debugging incorrect AI-generated code.Avoiding subtle logic bugs and broken dependencies.Testing AI-written functions before deployment.Combining human review with AI-generated solutions for reliable output.We’ll also cover real-world case studies where misunderstanding an AI’s mistake led to costly outcomes, and how small changes in workflow could have prevented them. You’ll see how these lessons apply not only to text and coding assistants, but also to AI-driven data analysis, customer service bots, and decision support systems.Finally, you’ll learn a systematic AI output verification framework you can apply to any LLM, whether it’s ChatGPT, Claude, Gemini, or open-source models. This framework ensures you catch misinformation, prevent damaging decisions, and maintain quality in both everyday AI tasks and high-stakes professional work.By the end of this course, you’ll be able to:Tell hallucinations and errors apart instantly.Design prompts that minimize AI mistakes.

The AI Literacy Specialization Program is one-of-a-kind hierarchical cognitive skills based curriculum that teaches artificial intelligence (AI) based on a scientific framework broken down into four levels of cognitive skills.Part 3: Analyze and Evaluate combines the below two cognitive skills -Analyzing (examining model's outputs to identify biases that may have been learned from the training data)Evaluating (assessing the performance, ethics, and overall effectiveness of an AI system)Whether you’re a policymaker, red teamer, LLM safety auditor, risk manager, AI developer, or a certification aspirant (AIGP, RAI, AAIA), this program gives you what theory alone cannot: applied, practice-ready evaluation and mitigation skills.The course is structured into four progressive modules: what to evaluate, how to evaluate, how to mitigate, and real-world case studies:IntroductionWhy Evaluate AI?Who Should Evaluate When?What to Evaluate?1.1) Standard Safety Evaluation1.2) Frontier Capabilities Misuse1.3) Misalignment1.4) Structural Multi-Agent RisksHow to Evaluate?2.1) Overview2.2) Benchmarks DatasetsBenchmark Repository v0.12.3) Evaluation Metrics2.4) Evaluation Techniques2.5) Red Teaming 101Evaluation Toolkit Repository v0.1How to Mitigate?3.1) How to MitigateMitigation Techniques Repository v0.1Case Studies4.1) Case Study 1: Automated Evaluation of LLMs Using Standard Eval Framework4.2) Case Study 2: Measuring Model Performance and Enhancing Explainability of a Credit Risk Model4.3) Case Study 3: Measuring Group Fairness in Housing Price Predi

Updates:2025/09/05: Added embedded web application example.FPGAs are often used to implement digital signal processing applications that require computation acceleration. FPGAs are also used to verify digital circuits before taping them out into silicon chips, called application-specific integrated circuits (ASICs). The Verilog/VHDL hardware description language (HDL) is used to describe the digital circuits, both for FPGA and ASIC targets. This course focuses on the Verilog language.This course teaches the fundamentals of building digital circuits with Verilog. Four topics of fundamental digital circuits are explained: combinational logic, sequential logic, finite state machines (FSM), and finite state machines with data paths (FSMD). Three more topics about integrating the digital circuit on the FPGA into an ARM processor are explained. At the end of the course, a final project on how to build a miniature Google TPU is explained.In the final project, you are going to learn the methodology design of an accelerator for a neural network based on a matrix multiplication core. Matrix multiplication is used in engineering for many applications. Once you get familiar with the methodology, then you should be able to apply the methodology design to any design that you want.After finishing the course, you will receive a certified certificate of completion. A complete Udemy 30-day money-back guarantee if you are not satisfied with this course, allowing you to study with no risk.See you within the course.

Security Information and Event Management (SIEM) is a critical component in modern cybersecurity frameworks. It enables organizations to collect and aggregate log data from various sources, such as servers, network devices, and applications, providing a centralized view of security events. This log collection process is essential for identifying suspicious activities and maintaining a historical record for forensic analysis.Security Information and Event Management also provides real-time monitoring and alerting capabilities. By continuously analyzing incoming logs and events, SIEM systems can detect anomalies or potential threats immediately. These alerts allow security teams to respond quickly to incidents, minimizing potential damage and maintaining organizational security posture.Another core aspect of Security Information and Event Management is threat detection and correlation. SIEM systems use advanced correlation rules, analytics, and sometimes artificial intelligence to connect seemingly unrelated events. This process helps identify complex attack patterns, insider threats, or coordinated attacks that might otherwise go unnoticed.Incident response and forensics form a major part of Security Information and Event Management operations. When a security incident is detected, SIEM tools provide detailed insights into the source, impact, and scope of the attack. This information is vital for investigating incidents, remediating vulnerabilities, and strengthening defenses against future threats.Compliance reporting and auditing is another important function of Security Information and Event Management. Many industries have strict regulatory requirements regarding data security, such as GDPR, HIPAA, or PCI DSS. SIEM systems generate reports and audit trails that demonstrate compliance, helping organizations avoid legal penalties and improve governance.Integration with other security tools and automation enhances the effectiveness of Security Information and Event Management