• views: 120
• date: 30 September 2026
• posted by: 0nelove

Agentic Automation — A Pytest Framework with Claude Code
Published 9/2026
Created by Josef Levy
MP4 | Video: h264, 3840x2160 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 49 Lectures ( 3h 6m ) | Size: 3.3 GB

Agentic Automation: Skills, Subagents, Planning, and Test Automation

What you'll learn
⚡ Design and author purpose-built Claude Code subagents — giving each a narrow mandate and exactly the tools it needs
⚡ Separate thinking from doing with plan mode — produce a reviewable plan before writing code
⚡ Orchestrate a multi-agent engineering loop — plan → build → review → test — where a lean orchestrator routes work across agents
⚡ Turn requirements into a running Pytest + Playwright framework — mapping user stories and acceptance criteria into non-flaky automated tests
⚡ Work within enforceable guardrails and improve agents from real results — building inside allow/deny permission policies

Requirements
❗ Comfortable with Python, Pytest, and Git; some Playwright and Claude Code exposure helps

Description

Agentic Automation Course

Build a test-automation framework where the work is done by AI agents, not scripts you run by hand.
Most automation courses teach you to write scripts. This one teaches you to build ateam — a roster of purpose-built Claude Code subagents that plan, write, review, run, and debug tests by reasoning about the work, using their own tools, and handing off to each other in a real engineering loop.

You'll author each specialist from scratch and wire it into a working pipeline

✅plan-architect researches a task and returns a reviewable plan before a line of code is written

✅automation-writer turns user stories and acceptance criteria into non-flaky Pytest + Playwright tests

✅senior-code-reviewer reviews the diff read-only and opens the PR on approval

✅test-suite-planner decomposes a story into prioritised, independent suites as Jira sub-tasks

✅test-runner executes each reviewed suite and posts a sanitized verdict

✅qa-failure-investigator tells a test bug from a product bug — byattempting the fix — and files evidence-backed defects

A leanorchestrator routes work between them and carries context across the hand-offs. Because the heavy lifting stays inside each agent's isolated context window, the orchestrator runs a long, multi-stage workflow without exhausting its own — in one measured build it held at~5%

context used while ~250K tokens of work happened inside the subagents.

What makes it different
✅"Agentic" is literal. Each agent is an autonomous LLM worker with a narrow mandate, its own context, and exactly the tools it needs — toolsare the capability boundary, so a read-only reviewer physically can't mutate the repo.

✅Dogfooded throughout. Every agent is exercised on real work in the course's own repo, driving a live Jira board and a real browser against a running app. Whatever breaks gets fed back into that agent's prompt — so you learn to improve agents the way you'll actually improve them.

✅Guardrails you workwith . Destructive, staging, commit, push, and PR-create commands are denied outright; sanctioned actions are wrapped in allow-listed skills. You learn to build inside enforceable policy, not around it.

The pipelineplan → build → review → test, with a defect fork when a suite catches a real product bug. Progress is visible on the Jira board through a single parent-story status, one owner per transition

To Do → In Planning → Ready for Dev → In Progress → In Review → In Testing → Done

You'll practice
Matching tools to intent · planning before doing · delegating execution to keep an orchestrator's context lean · separating "it compiles" from verified and "review" from security audit · and feeding real findings back into agents so each dogfood failure pre-empts its whole class

of bug next time.

Stack: Python · Pytest · Playwright · pipenv · pytest-html + Allure · custom Claude Code subagents and skills · live Jira Cloud.

Educational course material — designed to be worked through interactively with Claude Code against a sandbox Jira project and a disposable app-under-test.

Who this course is for
⭐ Developers and automation engineers learning to build with Claude Code subagents

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• views: 147
• date: 30 September 2026
• posted by: 0nelove

Hands-On Workshop: Build an AI Document Assistant on AWS
Published 9/2026
Created by Lukasz Kallas
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 14 Lectures ( 3h 42m ) | Size: 3.9 GB

Build a RAG document assistant with AI, Amazon Bedrock Knowledge Bases, S3, Next.js, AWS CDK & CI/CD

What you'll learn
⚡ Build an AI-powered document assistant using Amazon Bedrock and RAG
⚡ Use Amazon Bedrock Knowledge Bases and S3 to answer questions from your own documents
⚡ Define and deploy cloud infrastructure using AWS CDK, Docker, ECS, and Fargate
⚡ Build an automated CI/CD workflow with GitHub Actions while working with an AI coding agent

Requirements
❗ Basic understanding of web development
❗ Basic familiarity with AWS

Description
This hands-on workshop brings together generative AI, cloud, application development, Infrastructure as Code, containers, and CI/CD to build a complete AI document assistant on AWS.

Unlike my regular Hands-On Introduction courses, this workshop isn't focused on teaching one technology from scratch.

Instead, we'll start with an empty folder and use an AI coding agent inside Cursor to build and deploy an application that lets users upload their own documents and ask questions grounded in those documents.

And just like in the other workshops in this series, we're not going to hand-write the application or infrastructure code ourselves.

We'll direct the AI agent, review what it produces, test its decisions, and work through the problems we encounter along the way.

The application we'll build is an AI-powered document assistant.

Users will be able to upload documents through a Next.js application, store them in Amazon S3, and make them available through an Amazon Bedrock Knowledge Base. Once the documents have been processed and indexed, users can ask questions and receive answers grounded in their own content, together with citations pointing back to the source documents.

This gives us a practical implementation ofRetrieval-Augmented Generation, or RAG.

Throughout the workshop, we'll work with

✨ AI-assisted development with Cursor

✨ Retrieval-Augmented Generation (RAG)

✨ Amazon Bedrock

✨ Amazon Bedrock Knowledge Bases

✨ Foundation models

✨ Embeddings and vector search

✨ Amazon S3

✨ Next.js

✨ Docker

✨ AWS CDK with TypeScript

✨ Infrastructure as Code

✨ Amazon ECS

✨ Git and GitHub

✨ GitHub Actions for CI/CD

✨ Automated cloud deployments

We'll start with an empty project and use the AI coding agent to help us determine how the different pieces should fit together.

We'll build a Next.js interface where users can upload documents and interact with them through a question-and-answer experience.

Those documents will be stored in Amazon S3 and connected to an Amazon Bedrock Knowledge Base, giving us a managed RAG workflow for ingesting, indexing, retrieving, and using our own documents as context for foundation models.

On the question-answering side, we'll retrieve relevant information from the knowledge base and use Amazon Bedrock to generate responses grounded in our documents rather than relying solely on the model's existing knowledge.

We'll also work with citations so users can see which source documents contributed to an answer.

But building the AI functionality is only part of the workshop.

We'll useAWS CDK with TypeScript to define our cloud infrastructure as code, containerize the application with Docker, and deploy it using Amazon ECS and AWS Fargate.

We'll then connect everything to GitHub and create a GitHub Actions CI/CD pipeline so changes can automatically move from our repository to the deployed application.

As with every workshop in this series, the AI agent won't necessarily get everything right the first time.

That's intentional.

AI coding agents are non-deterministic. They can choose approaches we didn't expect, create incorrect configurations, misunderstand requirements, or generate code and infrastructure that simply don't work.

Instead of hiding those moments, we'll use them to understand how to direct an agent, inspect what it generates, troubleshoot problems, and decide whether its proposed solution actually makes sense.

That's one of the main differences between this workshop and a traditional step-by-step tutorial.

By the end of the workshop, we'll have connected application development, generative AI, RAG, document storage, vector search, Infrastructure as Code, containers, cloud deployment, and CI/CD into one working system.

More importantly, you'll have seen how all of these individual technologies fit together when building something larger than another isolated AI chatbot demo.

This workshop is a great fit if you are

✨ A developer interested in building practical generative AI applications

✨ An AWS developer or cloud engineer exploring Amazon Bedrock

✨ A developer who wants to understand RAG through a real project

✨ A full-stack developer exploring AI-powered applications

✨ Someone interested in AI-assisted software development with Cursor

✨ Anyone familiar with individual AWS, AI, or DevOps technologies who wants to see how they work together

This workshop assumes some familiarity with AWS, web development, generative AI concepts, and Infrastructure as Code. We won't teach every individual technology from scratch.

If concepts such as Amazon Bedrock, embeddings, vector databases, AWS CDK, or containers are completely new to you, my individual Hands-On Introduction courses cover those topics in more detail.

Here, the goal is different:Put the pieces together and build something.

Who this course is for
⭐ Developers interested in building practical generative AI and RAG applications
⭐ AWS developers exploring Amazon Bedrock and Knowledge Bases
⭐ Full-stack developers who want to add AI capabilities to applications
⭐ Cloud and DevOps engineers interested in AI workloads
⭐ Developers experimenting with Cursor and AI coding agents
⭐ Students familiar with individual technologies who want to see how they work together in a complete project

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• views: 279
• date: 30 September 2026
• posted by: 0nelove

Statistics & Probability from Zero: Real-Life Examples
Published 9/2026
Created by Omar Koryakin
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English + subtitle | Duration: 48 Lectures ( 9h 15m ) | Size: 10.7 GB

Read ratings, salaries, medical tests and polls with confidence. Statistics by hand, one picture at a time, no code.

What you'll learn
⚡ Judge a star rating or review score by reading it together with how many reviews are behind it
⚡ See through an "average salary" or "average house price" by working out the median and spotting outliers
⚡ Calculate the mean, median, mode, range and standard deviation by hand, and know which one answers your question
⚡ Use a z-score to say how unusual a test result, a sales day or a card payment really is
⚡ Work out the real chance of combined events with a dice grid, a Venn diagram and a tree diagram
⚡ Explain what a positive medical test really means when the illness is rare, and why base rates matter
⚡ Read a poll's "plus or minus 3 points" and tell when a race is too close to call
⚡ Build a 95% confidence interval and run a full hypothesis test, with a plain-English conclusion
⚡ Spot a correlation that isn't a cause, fit a simple prediction line, and question a "hot streak"
⚡ Catch misleading charts and headlines, like a cropped bar chart axis or a "most X were Y" story

Requirements
❗ Everyday arithmetic: adding, subtracting, multiplying, dividing and working out a percentage
❗ A basic calculator (the one on your phone is fine) and some paper and a pen for the practice exercises
❗ No previous statistics, algebra or software experience is needed; every word and symbol is explained from scratch
❗ Willingness to pause the video and try each short exercise before watching the answer

Description
This course contains the use of artificial intelligence.

Have you ever looked at a number in a report, a news story or a doctor's letter and felt your mind go blank? This course is for that moment. We start from zero, and by the end you'll be able to look at almost any number someone shows you and ask the three questions that matter: how many values are behind it, how spread out they are, and how likely it is to be luck.

We begin with real life, not formulas. You'll work out why a restaurant with 4.9 stars from 12 reviews is less trustworthy than one with 4.6 stars from 3,100. You'll see why an "average salary" in a job advert can be a number that almost nobody earns, and why the median tells you more. You'll count 1,000 people through a simple tree and find out that a positive result on a "90% accurate" test for a rare illness means only about an 8% chance you're actually ill. And you'll read a poll that says 52%, plus or minus 3 points, and know whether the race is really decided.

The teaching is slow and patient on purpose. Every lesson starts with a situation you'll recognise, explains the reason before the rule, and then walks through a worked example step by step with a picture you can see building on screen. After that it's your turn: you pause, try a short exercise yourself, and then we check the answer together. Each lesson also shows where you'll meet the idea at work or at home, and the classic mistake people make with it, so you don't fall into it.

All you need is everyday arithmetic: adding, subtracting, multiplying, dividing and percentages on a calculator. There's no software to install, no code and no algebra beyond "put the numbers in and work it out". Every new word and symbol is explained the first time it appears, and we reuse the same small set of examples (a restaurant, a class of 20 students, a coffee shop, a small studio's salaries) so you can focus on the idea instead of learning a new story each time.

Here's the path. We start with data, tables and honest bar charts, then the mean, median and mode, then spread and the standard deviation. You'll learn percentiles and z-scores to say how unusual a value is, then probability by counting dice, tree diagrams, conditional probability and base rates. After that comes the normal curve and reading a z-table, then samples, margin of error and your first 95% confidence interval. Next you'll run a full hypothesis test and write a conclusion that doesn't overclaim. We finish with correlation, a line of best fit, and two real-life case studies: is a basketball player really on a hot streak, and are your trading returns as calm as the bell curve suggests?

This course is a good fit if you're switching careers into data, reading dashboards at work, studying for a statistics module, helping your children with school maths, or simply tired of feeling fooled by numbers in the news. If you've tried statistics before and it went too fast, this one won't.

Who this course is for
⭐ Complete beginners who freeze when a report, a news story or a doctor throws numbers at them
⭐ Career switchers heading into data analysis or data science who want solid foundations before learning the tools
⭐ Office staff and managers who read dashboards, sales reports or survey results and want to question them properly
⭐ Students taking an introductory statistics module in school, college or university who need it explained slowly
⭐ Parents who want to understand school reports, percentiles and test scores, and help their children with maths
⭐ Traders, investors and curious readers who want to understand risk, averages, probability and "significant" results

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• views: 248
• date: 30 September 2026
• posted by: 0nelove

Laravel 13 - Build a Video Membership & Subscription Script
Published 9/2026
Created by Md Morshedul Arefin
MP4 | Video: h264, 2560x1440 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 76 Lectures ( 15h 11m ) | Size: 18.5 GB

Making a Complete Video Membership & Subscription Platform using Laravel 13 from Scratch

What you'll learn
⚡ Multi Authentication system from scratch
⚡ Sending email using laravel built in mail system
⚡ Complete CRUD system
⚡ Form validation technique
⚡ Admin login and reset password system
⚡ Admin profile management
⚡ Admin settings for different section of the website
⚡ Photo uploading system
⚡ Toast message notification
⚡ Manage Subscribers and Verify
⚡ Subscriber export as CSV
⚡ Pricing Package Management
⚡ Laravel Advanced Security System
⚡ Standard Single Authentication System
⚡ Standard Multi Authentication System
⚡ PayPal Payment Method Integration
⚡ Stripe Payment Method Integration

Requirements
❗ Basic knowledge in HTML
❗ Basic knowledge in CSS
❗ Basic knowledge in Bootstrap
❗ Basic knowledge in jаvascript
❗ Basic knowledge in Code Editor like VS Code or any other

Description
In this hands-on Laravel course, you will learn how to build a completeVideo Membership & Subscription Platform from scratch.

We will create a real-world platform where an administrator can publish YouTube videos under different categories, while visitors can browse and watch free content. For premium videos, users must create an account and purchase a membership package to access the protected content.

This course is designed to help you understand how a real Laravel application is planned, structured, developed, and turned into a complete working platform.

Admin Panel Features
1. Admin login & authentication

2. Dashboard management

3. Forget and reset password

2. Manage home page information

3. Manage video categories

4. Manage contact information

5. Manage videos

6. Manage memberships and packages

7. Setup videos as free or premium

8. See all payments made by users

9. Manage subscriber list and export as CSV.

10. Manage profile photo, information and password

11. Manage website settings

12. See all tickets and give replies

User Panel Features
1. User registration and verify account

2. User login & authentication

3. Dashboard management

4. Forget and reset password

5. Browse video categories

6. Browse free videos

7. Browse premium videos after purchase

8. Pay with PayPal and Stripe

9. Manage profile information and password

10. Create tickets and give replies

Who this course is for
⭐ Persons who want to learn laravel deeply
⭐ Persons who are looking for a good job in web development sector
⭐ Persons who want to have a remote job in laravel
⭐ Persons who want to start his freelancing career in php and laravel
⭐ Persons who want to learn making a video platform and subscription system

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• views: 179
• date: 30 September 2026
• posted by: 0nelove

Grafana MCP: Production AI Observability on AWS
Last updated 9/2026
Created by Opeyemi Onikute
MP4 | Video: h264, 3840x2160 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 32 Lectures ( 2h 3m ) | Size: 4.5 GB

Deploy Grafana MCP with Docker, Terraform, EKS, and ECS. Query from Cursor, Claude Code, Devin, and OpenCode

What you'll learn
⚡ Understand the Model Context Protocol end-to-end
⚡ Master how to run and operate the Grafana MCP server
⚡ Deploy and harden Grafana MCP in production
⚡ Build a production observability stack on AWS with Terraform
⚡ Build a production observability stack on AWS with Terraform

Requirements
❗ Basic familiarity with Grafana: logging in, viewing dashboards, and adding datasources
❗ Grafana Cloud Account (free tier)
❗ Comfort with the command line: files, environment variables, and running scripts in Bash
❗ Basic Docker: run containers and map ports
❗ Basic Kubernetes: pods, Deployments, Services, and reading logs with `kubectl`
❗ Basic git and other installed tools: `terraform`, `kubectl`, and the `aws` CLI.

Description
I have taught ~17000 students about monitoring with Prometheus and Grafana. Recently, I've been receiving several questions about how to deploy Grafana MCP in production. This course contains an extensive library of information to help you get up and running with MCP from the ground up. You will gain practical experience that you can immediately apply in your production systems.

At the end of the course, you'll have experience with the following

✨ Running Grafana MCP locally with Docker and connecting it to a real Grafana instance.

✨ Connecting Grafana MCP to Grafana Cloud and query Cloud-backed metrics, logs, and dashboards.

✨ Deploying Prometheus, Loki, Grafana, and a demo app on AWS with Terraform.

✨ Deploying Grafana MCP on Amazon ECS Fargate and Amazon EKS with TLS, IAM, and Secrets Manager.

✨ Querying live dashboards, metrics, logs, and alerts from Cursor, Claude Code, Devin Desktop, and OpenCode.

✨ Explaining MCP architecture: clients, servers, tools, and transports (stdio, SSE, and streamable HTTP).

✨ Scoping Grafana service accounts and MCP tools so the assistant runs with least privilege, not admin.

✨ Identifying MCP security risks and apply the controls you actually need on AWS.

✨ Choosing an ECS or EKS architecture for Grafana MCP and monitoring the MCP server itself.

✨ Investigating incidents from your editor using live Prometheus and Loki data instead of screenshots.

Who this course is for
⭐ Platform and DevOps engineers who need to run and manage MCP servers in production
⭐ Developers who use Grafana daily and want production context in their AI assistant
⭐ SREs or oncall engineers who want faster incident triage while adhering to least-privilege principles

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• views: 157
• date: 30 September 2026
• posted by: 0nelove

Bootcamp: Oracle PL/SQL
Published 9/2026
Created by Jorge Vinsencio Figuera
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: Spanish | Duration: 45 Lectures ( 4h 24m ) | Size: 2 GB

Codificación en PL/SQL

What you'll learn
⚡ Aprender los fundamentos del lenguaje PL/SQL
⚡ Definir variables, sus tipos de datos y cursores
⚡ Crear packages , procedimientos y funciones
⚡ Gestion y tratamiento de errores en PL/SQL
⚡ Gestion de E/S de archivos utilizando el PL/SQL
⚡ PL/SQL dinámico, colecciones

Requirements
❗ Tener instalado una versión de Oracle RDBMS superior al 11G
❗ Descargar e instalar la aplicación DbVisualizer u otro aplicativo para conectar a una base de datos Oracle
❗ Tener conocimientos del Lenguaje de Consulta de Datos (SQL)
❗ Tener conocimientos previos de lógica de programación en cualquier otro lenguaje

Description
En el presente BootCamp , estaremos estudiando la programación en Oracle PL/SQL, en donde abordaremos los siguientes temas relacionados con la programación en este fascinante lenguaje de programación, se trata de un curso dirijido a todas aquellas personas interesadas en aprender a codificar en este lenguaje :

1. Estructuras de bloques

2. Variables

3. Manejo de errores

4. Tipos de variables

5. Bucles

6. Estructura de Cursores

7.- Nombre de indentificadores

8. Delimitadores

9. Conversion de tipos de datos

10. Comentarios en PL/SQL

11. Ambito y visibilidad de las variables

12. Estructuras IF-THEM-ELSE

13. Estructuras de control

14. Bucles en PL/SQL (Loop/End Loop, While /End Loop,For/End Loop)

15. Como colocar etiquetas en PL/SQL

16. Bucles anidados

17. Operadores del tipo %ROWTYPE

18. Variables de tipo tablas

19.- Cursores explicitos y parametrizados

10.- Subprogramas

21. Procedimientos, packages , funciones y mucho mas

Para ello se requerira que el estudiante cuente con una base de datos ORACLE y alguna herramienta /interfaz que le permita al usuario conectar a la base de datos tales como TOAD, DBBEAVER , DBVISUALIZER para que pueda llevar adelante los ejecicios practicos. Finalmente les invito a disfrutar y aprovechar al maximo el curso y hacernos llegar sus comentarios para asi mejorar el mismo

Who this course is for
⭐ El curso esta dirijido a estudiantes , profesionales del area de TI y personas en general interesadas en aprender el lenguaje de programación Oracle PL/SQL

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• views: 200
• date: 30 September 2026
• posted by: 0nelove

Copilot for Excel: Microsoft Copilot for Business Analysis
Published 9/2026
Created by Grant Gamble
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 26 Lectures ( 5h 0m ) | Size: 3.8 GB

Analyze business data with Copilot in Excel: formulas, dynamic arrays, reusable LAMBDAs and self-updating reports

What you'll learn
⚡ Explain what Copilot in Excel can see and prepare your workbooks so it gives accurate, relevant answers
⚡ Choose between Chat, Plan and Edit modes and write specific prompts that get the right result first time
⚡ Create, check and fix formulas with Copilot, from single cells to dynamic arrays such as FILTER, SORT and VSTACK
⚡ Turn business rules into readable, reusable formulas using LET, LAMBDA, BYROW and MAP
⚡ Build self-updating reports with GROUPBY and PIVOTBY, and decide when a PivotTable is still the better choice

Requirements
❗ A Microsoft 365 subscription that includes Copilot in Excel (the course shows you how to check your licence)
❗ Excel for Windows, Mac or the web, with your files saved to OneDrive or SharePoint
❗ Basic Excel skills: entering data, simple formulas such as SUM, and moving between sheets

Description
This course contains the use of artificial intelligence. AI tools are used in the production of this course, including AI-assisted speech delivery based on the instructor's own voice. All content, demonstrations, workbooks, and prompts have been personally designed, reviewed, and verified by the instructor to ensure accuracy and practical value.

Copilot in Excel can now read your workbook, write formulas, build reports and even create complete solutions from a single prompt. This course shows you how to use it properly: how to get reliable answers, how to check what it gives you and how to stay in control of your file.

Across 16 practical sections and more than four and a half hours of video, every lesson comes with a practice workbook so you can follow along and try each prompt yourself. You will work with realistic business data, not toy examples.

What you will cover
✨Getting started: what Copilot can see, licensing and platforms, preparing your data and choosing a model.

✨Working with Copilot: Chat, Plan and Edit modes, writing specific prompts and iterating on results.

✨Formulas: everyday edits, dynamic arrays such as FILTER, SORT and VSTACK, and reusable logic with LET, LAMBDA and BYROW.

✨Reporting and analysis: GROUPBY and PIVOTBY reports, charts, PivotTables and plain-English questions of your data.

Why this course is different
✨ It is honest about where Copilot gets things wrong, and teaches practical checks so you can trust its output to the right degree.

✨ It explainswhy a prompt works, so you can write your own rather than copying templates.

✨ It shows whennot to use Copilot, because sometimes a shortcut or a simple formula is quicker.

✨ No Python or advanced formula experience is needed. Copilot does the heavy lifting; you learn to direct and review it.

By the end of the course you will be able to hand routine and complex Excel work to Copilot with confidence, knowing how to check the results and reverse any change.Enrol now and start getting more from Excel today.

Who this course is for
⭐ Excel users who want Copilot to save them time on everyday formulas, formatting and analysis
⭐ Finance, operations, HR and sales professionals who build reports, trackers and models in Excel
⭐ Analysts who want to use dynamic arrays, LAMBDA and Python in Excel without learning all the syntax first
⭐ Team leads and IT staff rolling out Copilot who need to understand licensing, safe editing and governance
⭐ Anyone who has tried Copilot in Excel, found the answers disappointing and wants dependable results

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• views: 220
• date: 30 September 2026
• posted by: 0nelove

GH-900 GitHub Foundations
Last updated 9/2026
Created by Shane Coffey
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 61 Lectures ( 7h 9m ) | Size: 3.9 GB

Targeted towards GitHub users who want to validate their understanding of the foundational topics, products, and concept

What you'll learn
⚡ Describe the purpose and value of GitHub in modern software development.
⚡ Explain the fundamentals of Git and version control.
⚡ Create and manage GitHub repositories.
⚡ Understand branches, commits, merges, and pull requests.
⚡ Collaborate effectively using GitHub workflows.
⚡ Manage project work using GitHub Issues and GitHub Projects.
⚡ Understand GitHub Actions and workflow automation concepts.
⚡ Describe GitHub security and code protection features.
⚡ Explain the role of GitHub Copilot in AI-assisted software development.
⚡ Recognize best practices for collaborative and open-source development.
⚡ Understand DevOps concepts and GitHub's role in software delivery pipelines.
⚡ Navigate GitHub confidently as a developer, administrator, contributor, or project stakeholder.

Requirements
❗ No prior GitHub experience required
❗ No software development experience required
❗ Basic computer and internet navigation skills
❗ Interest in software development, DevOps, or technology collaboration
❗ Desire to learn how teams manage code, projects, and software delivery using GitHub
❗ A GitHub account is recommended for hands-on practice, but not required

Description
"This course contains the use of artificial intelligence."

Course Introduction
Software development has become one of the most important drivers of innovation in the modern world. Whether building web applications, cloud services, mobile apps, or artificial intelligence solutions, development teams need tools that allow them to collaborate effectively, manage code changes, automate workflows, and deliver high-quality software at scale.GitHub has emerged as the world's leading platform for software development collaboration, helping millions of developers and organizations build software more efficiently.

TheGH-900: GitHub Foundations course provides a comprehensive introduction to GitHub, version control, collaborative development practices, and modern software development workflows. Designed for both technical and non-technical learners, this course helps students understand how GitHub supports software projects from initial development through deployment and ongoing maintenance.

Throughout this course, you'll explore the core concepts of Git and version control, learning how developers track changes, manage code repositories, and collaborate on projects across teams. You'll gain hands-on knowledge of repositories, branches, commits, pull requests, and issues, while also learning how GitHub enables communication and collaboration among developers, project managers, and stakeholders.

The course also introduces GitHub's powerful platform capabilities, including GitHub Actions for automation, GitHub Projects for work management, GitHub Discussions for community collaboration, and GitHub security features that help organizations build secure software. As artificial intelligence becomes increasingly integrated into software development, you'll also examine GitHub Copilot and how AI-powered coding assistants are transforming developer productivity and software engineering practices.

In addition to technical concepts, you'll learn how GitHub supports open-source development, enterprise collaboration, DevOps workflows, and modern software delivery practices. Through real-world examples and practical scenarios, you'll discover how organizations use GitHub to manage projects, improve code quality, streamline development processes, and accelerate innovation.

By the end of this course, you'll have a solid understanding of GitHub fundamentals, collaborative development workflows, repository management, and modern software development practices. Whether you're preparing for the GitHub Foundations certification, beginning a career in technology, supporting development teams, or simply looking to understand how software projects are managed, this course provides the ideal starting point.

Learning Objectives
After completing this course, you will be able to

✨ Describe the purpose and value of GitHub in modern software development.

✨ Explain the fundamentals of Git and version control.

✨ Create and manage GitHub repositories.

✨ Understand branches, commits, merges, and pull requests.

✨ Collaborate effectively using GitHub workflows.

✨ Manage project work using GitHub Issues and GitHub Projects.

✨ Understand GitHub Actions and workflow automation concepts.

✨ Describe GitHub security and code protection features.

✨ Explain the role of GitHub Copilot in AI-assisted software development.

✨ Recognize best practices for collaborative and open-source development.

✨ Understand DevOps concepts and GitHub's role in software delivery pipelines.

✨ Navigate GitHub confidently as a developer, administrator, contributor, or project stakeholder.

This course serves as an excellent foundation for students, developers, IT professionals, project managers, business analysts, technology leaders, and anyone seeking to understand how GitHub powers modern software development and collaboration.

Who this course is for
⭐ Students exploring software development careers
⭐ Aspiring developers and engineers
⭐ IT professionals seeking GitHub knowledge
⭐ DevOps and platform engineering beginners
⭐ Project managers working with development teams
⭐ Business analysts involved in technology projects
⭐ Open-source contributors
⭐ Team leaders and technology managers
⭐ Professionals preparing for the GitHub Foundations certification exam
⭐ Anyone interested in modern software development practices

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• views: 60
• date: 30 September 2026
• posted by: DrZero

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• date: 30 September 2026
• posted by: DrZero

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