Napisane przez: charlie - 14-01-2026, 17:45 - Forum: Propozycje
- Brak odpowiedzi
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Mcp For Qa Engineers: Ai Automation With Typescript (2026)
Last updated 1/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 3h 45m | Size: 2.04 GB [/center]
Building AI-Integrated QA Systems with MCP & TypeScript
What you'll learn
Understand Model Context Protocol (MCP) from first principles
Build an MCP server from scratch using the official TypeScript SDK
Connect an MCP server to a real MySQL database for live data interaction
Build MCP Resources to expose read-only contextual data safely to AI models
Apply Zod schemas to validate MCP tool inputs and outputs like a professional
Handle edge cases and error scenarios
Requirements
Basic JavaScript or TypeScript knowledge
Basic understanding of backend concepts such as What APIs or services are etc
Description
This course is designed for QA Engineers, SDETs, and Automation Engineers who want to build AI-integrated, production-ready QA systems using the Model Context Protocol (MCP) and TypeScript.You will learn how to safely connect AI models to real QA systems-such as databases, services, and test utilities-so that AI can assist with validation, analysis, and automated decision-making in enterprise environments.Build production-ready MCP servers using TypeScript to power AI-driven QA automation. Learn MCP fundamentals, create custom tools and resources, integrate real backend systems, and design agentic AI workflows that let LLMs interact with your applications safely and intelligently.What you will be able to do after this courseBuild MCP servers that expose QA-safe actions to AI modelsEnable AI-assisted test data validation and verificationAllow LLMs to interact with read-only QA context securelyDesign controlled AI-driven QA workflows, not autonomous chaosIntegrate AI with existing automation and backend systemsAI models like ChatGPT and Claude are powerful, but on their own they can only generate text. To unlock their real potential in software testing and automation, they need a safe and structured way to interact with real systems. That is exactly what Model Context Protocol (MCP) provides - and this course teaches you how to use it effectively for QA and automation.In this course, you will learn how to build production-ready MCP servers using TypeScript, enabling AI models to read data, execute actions, and reason over real backend systems such as databases.Through hands-on, step-by-step lessons, you'll create custom MCP tools for CRUD operations, expose read-only resources for AI context, and design agentic AI workflows where the model can make decisions, take actions, and request follow-up operations autonomously.Overall, the course teaches how to move beyond traditional automation and build production-ready, AI-integrated QA systems using modern AI protocols and real-world backend integrations.
Who this course is for
QA Engineers wanting to move into AI-driven testing
SDETs & Automation Engineers
Test Leads & QA Architects
Backend developers curious about MCP
Anyone who wants to build AI-powered tools using real backend systems
Napisane przez: charlie - 14-01-2026, 17:41 - Forum: Propozycje
- Brak odpowiedzi
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Machine Learning : Basics To Advanced 2026
Published 1/2026
Created by Vishal Vishal
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 21 Lectures ( 2h 5m ) | Size: 1.66 GB [/center] Learn Machine Learning from scratch using Python - covering , data handling, popular algorithms, and real-world project What you'll learn
Understand the complete Machine Learning workflow, from data collection and preprocessing to model training and evaluation
Implement and clearly understand Regression algorithms including Linear, Multiple Linear, and Polynomial Regression.
Implement and clearly understand Regression algorithms including Linear, Multiple Linear, and Polynomial Regression.
Apply Classification algorithms such as Logistic Regression, KNN, SVM, Naive Bayes, Decision Trees, and Random Forest to solve practical problems.
Work with Unsupervised Learning techniques, including K-Means Clustering for pattern discovery and customer segmentation.
Evaluate and improve model performance using techniques like train-test split, cross-validation, and performance metrics. Requirements
Basic understanding of Python programming (variables, loops, functions).
No prior experience in Machine Learning, Data Science, or AI is required
Basic school-level mathematics is enough (all required math concepts are explained simply)
A computer or laptop with internet access Description
This course contains the use of artificial intelligence.Machine Learning: Basics to Advanced (2026) is a complete, structured, and practical course designed to help you master Machine Learning using Python. This course starts from absolute fundamentals and gradually moves toward advanced algorithms and real-world applications. A basic knowledge of Python is required, but no prior Machine Learning experience is needed.This course is designed in a simple and beginner-friendly way so that even students with no background in Machine Learning can understand concepts clearly and confidently apply them in real projects.Who This Course Is ForStudents who want to start a career in Machine LearningBeginners with basic Python knowledgeAspiring Data Scientists and ML EngineersSoftware developers who want to add ML skillsAnyone preparing for internships, jobs, or interviews in MLMachine Learning Algorithms CoveredYou will learn and implement the following algorithms with hands-on projects:Linear RegressionMultiple Linear RegressionPolynomial RegressionLogistic RegressionK-Nearest Neighbors (KNN)Support Vector Machine (SVM)Naive BayesDecision TreeRandom ForestK-Means ClusteringWhat You Will LearnComplete Machine Learning workflow Data preprocessing, feature engineering, and exploratory data analysis (EDA)Model training, testing, validation, and performance evaluationHow to choose the right algorithm for a given problemPrerequisitesBasic understanding of Python programmingWillingness to learn mathematics behind ML (explained simply)No prior experience in Machine Learning or Data Science required.Career OutcomesAfter completing this course, you will be confident to:Build Machine Learning models from scratchCrack internships and entry-level ML rolesApply ML to real-world business problemsMove forward toward Advanced AI and Deep Learning.Disclosure:This course uses AI-generated images and visual content for better explanation and presentation. The instructor's own voice, knowledge, and teaching methods are used throughout the course. Who this course is for
Students who want to start a career in Machine Learning or Data Science
Software developers who want to add Machine Learning skills to their profile
Anyone interested in learning how machines learn from data
Napisane przez: charlie - 14-01-2026, 17:40 - Forum: Propozycje
- Brak odpowiedzi
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Hashicorp Vault Enterprise 1.21.2 (x64)
File Size: 101.3 MB [/center]
Manage secrets and protect sensitive data. Create and secure access to tokens, passwords, certificates, and encryption keys.
Protect critical systems and customer data
HashiCorp Vault helps organizations reduce the risk of breaches and data exposure with identity-based security automation and encryption-as-a-service.
Increase security across clouds and apps 100+ integrations
To centrally control access to sensitive data and systems across your entire IT estate.
Safely automate dynamic secrets delivery
Generate 10,000+ unique tokens daily
Govern access to secrets, automate application delivery, and consume secrets programmatically.
Reduce risk of a breach
90% less time spent on secrets management
Eliminate static, hard-coded credentials in favor of tightly controlled access based on trusted identities.
![[Obrazek: _c722d32f3d711e8dcf2066406794db8a.jpg]](https://i126.fastpic.org/big/2026/0114/8a/_c722d32f3d711e8dcf2066406794db8a.jpg)
![[Obrazek: f32311c2477f643f4366b9643c509440.jpg]](https://i126.fastpic.org/big/2026/0114/40/f32311c2477f643f4366b9643c509440.jpg)
![[Obrazek: d62afb1f47f511f4269ba8a28a2a6cb9.jpg]](https://i126.fastpic.org/big/2026/0114/b9/d62afb1f47f511f4269ba8a28a2a6cb9.jpg)
