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Course Overview
The Evergreen Ads + Upsells course teaches students how to build profitable advertising funnels that run continuously without the need for constant updates or manual management.

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Tiana Asperjan - Advanced Ad Copy Secrets

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Salvora A.I Avatars Amazon Affiliate Course

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Advanced AI Chatbots: Build, Evaluate, and Deploy Agents RAG Chatbots
Released 9/2026
With Roya Kandalan
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Skill level: Beginner + Intermediate | Genre: eLearning | Language: English + subtitle | Duration: 2h 39m | Size: 875.8 MB

Learn how to build, measure, and deploy an advanced RAG chatbot with LangChain 1.0. Discover how to add structured citations, extend behavior with middleware, and ship it publicly.

Course details
A RAG chatbot is only useful if you can trust its answers and ship it with confidence. In this advanced course with Roya Kandalan, discover how to build and deploy a RAG chatbot that is customizable, reliable, and production-ready. Starting from a LangChain 1.0 starter project, learn how to create a chatbot with structured citations that keep responses accurate and traceable. Explore how to add middleware for logging and fallback behavior without touching your core code, then measure chatbot quality using tracing and automated metrics. Discover how to prepare your chatbot for public deployment as an accessible, adaptable tool, ready for regulated environments such as healthcare and education. By the end of this course, you'll be able to build, evaluate, extend, and deploy a RAG chatbot you can adapt for many use cases. This beginner-to-intermediate course suits developers, AI engineers, data scientists, and anyone eager to build conversational AI.

Skills covered
Retrieval-Augmented Generation (RAG), AI Agents, LangChain, Fine Tuning, Artificial Intelligence (AI)

Homepage

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TypeSafe AI: Jev Essentials - Building Decision Systems
Published 9/2026
Created by Mohammad Azam
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 22 Lectures ( 1h 49m ) | Size: 1.6 GB

Turn Unstructured Data into Structured Decisions with Jev

What you'll learn
⚡ Understand how Jev transforms unstructured data into structured, actionable decisions.
⚡ Build a real-world resume evaluator using Jev and the jаvascript SDK.
⚡ Classify and evaluate customer support messages using Python and Jev.
⚡ Process multiple records using batch evaluation for scalable decision workflows.
⚡ Build intelligent routing systems that direct requests to specialized AI profiles or assistants.

Requirements
❗ Basic programming knowledge is recommended.
❗ Familiarity with jаvascript or Python is helpful but not required.
❗ Basic understanding of APIs and JSON will be useful.
❗ No prior experience with Jev or TypeSafe AI is required.
❗ No prior machine learning or advanced AI knowledge is required.

Description
AI is great at generating text, but many real-world applications need something much more useful:structured decisions that your software can act on.

InTypeSafe AI: Jev Essentials - Building Decision Systems, you will learn how to useJev by TypeSafe AI to transform unstructured information into structured judgments that can be used directly inside your applications.

This is a practical, project-based course. Instead of spending hours on theory, you will learn Jev by building real-world applications and solving common AI decision-making problems.

We will start by exploring what Jev is, how it works, and how to obtain and configure your API key. From there, you will quickly move into hands-on projects.

Build a Resume Evaluator
You will build a complete resume evaluation application using the Jev jаvascript SDK.

You will learn how to define questions for evaluating resumes, organize and refactor your evaluation criteria, process Jev responses, map those responses into your application's UI model, and display structured results in a web application.

Classify Customer Support Messages
Next, you will use Jev to classify customer support requests.

Using Python and Google Colab, you will evaluate individual customer messages and then expand the solution to process multiple messages in batches.

This project demonstrates how unstructured customer communication can be converted into structured information that your software can categorize, organize, and act upon.

Route Requests to Specialized AI Assistants
Finally, you will exploreAI routing.

Instead of sending every request to the same model or assistant, you will learn how Jev can analyze an incoming request and help determine which specialized profile or assistant should handle it.

You will examine the request and response process using Postman and then walk through the implementation of a routing system.

What You Will Learn
By the end of this course, you will understand how to

✨ Understand the role of Jev in AI-powered applications

✨ Configure and use the Jev API

✨ Work with the Jev jаvascript SDK

✨ Define structured questions and evaluation criteria

✨ Turn unstructured data into structured judgments

✨ Build an AI-powered resume evaluator

✨ Classify customer support messages

✨ Perform batch evaluations using Python and Google Colab

✨ Use Jev for intelligent request routing

✨ Route requests to specialized AI profiles or assistants

✨ Integrate Jev results into real-world applications

Who Is This Course For?
This course is designed for developers who want to move beyond simple AI chat applications and build systems that canevaluate, classify, route, and make structured decisions from unstructured data.

You don't need to be an AI or machine learning expert. If you are comfortable with basic programming concepts and want to learn how AI can become part of your application's decision-making pipeline, this course will give you a practical starting point.

By the end of the course, you won't just understand what Jev does—you will have built multiple applications demonstrating how structured AI judgments can be used to create intelligent decision systems.

Who this course is for
⭐ Software developers who want to build intelligent decision systems using AI.
⭐ jаvascript and Python developers interested in integrating Jev into real-world applications.
⭐ AI developers who want to turn unstructured data into structured, actionable decisions.
⭐ Developers building AI-powered workflows for evaluation, classification, and intelligent routing.
⭐ Anyone interested in practical AI development without needing a machine learning or data science background.

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• date: 30 September 2026
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RAG Agents with LangChain & LangGraph: A Practical Guide
Last updated 9/2026
Created by Anton Voroniuk• 1.250.000+ Students, Anton Voroniuk Support, George Paterakis
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 11 Lectures ( 1h 49m ) | Size: 688 MB

Ground your AI agents in real dаta: build a full RAG agent in LangChain, then rebuild it as a LangGraph workflow

What you'll learn
⚡ Build a complete RAG agent in LangChain that retrieves relevant documents and generates grounded, source-based answers
⚡ Rebuild the same RAG agent as a LangGraph workflow, with retrieval as an explicit, controllable step in the graph
⚡ Understand how chunking, embeddings, retrieval, and grounding work together and how each one affects answer quality
⚡ Explain what RAG is, why LLMs need it, and when retrieval is the right fix for hallucinations and outdated knowledge
⚡ Decide where a retrieval step belongs inside an agent's reason–act–observe loop
⚡ Compare a plain LangChain RAG implementation with a graph-based design and choose the right one for your project
⚡ Explain what an AI agent is, how it makes decisions, and when a single LLM call is the better choice
⚡ Recognize the most common ways agent projects fail and apply design habits that prevent them

Requirements
❗ Working knowledge of Python (functions, classes, working with lists and dictionaries)
❗ Basic familiarity with LangChain — you should have called a chat model and ideally defined a tool at least once
❗ Some exposure to LangGraph is helpful for the final build but not required; the graph is explained step by step
❗ An API key for an LLM provider such as OpenAI or Anthropic, plus access to an embedding model (free tiers are enough)
❗ A computer with Python 3.10+ and a code editor installed

Description
Ask an LLM about a document written yesterday and you get a confident, detailed, wrong answer. The model only knows what it saw in training. Retrieval-Augmented Generation fixes this by letting the agent look things up before it responds, and this course teaches you to build a RAG agent that answers from your own data.

What you will learn
✨ Explain what RAG is, why LLMs need it, and when retrieval is the right fix for hallucinations and outdated knowledge

✨ Understand how chunking, embeddings, retrieval, and grounding work together and how each affects answer quality

✨ Build a complete RAG agent in LangChain, from loading documents to a source-based answer

✨ Rebuild the same agent as a LangGraph workflow, with retrieval as an explicit, controllable step

✨ Choose between a plain LangChain implementation and a graph-based design for your own project

Inside the course
After a short grounding in agent fundamentals, the course explains the full RAG pipeline in plain terms before any code is written. Then come two hands-on builds: the same RAG agent first in LangChain, then rebuilt in LangGraph. Building it twice is deliberate: you will understand both implementations well enough to pick the right one for your needs. Written references close each section.

Who this course is for
⭐ Python developers who want their agent to answer from their own documents instead of guessing
⭐ AI engineers building assistants that need current, verifiable knowledge rather than what the model memorized in training
⭐ Developers who have built a basic "chat with your PDF" demo and want to understand what's actually happening under the hood
⭐ Backend engineers who need to ground LLM output in internal data such as docs, tickets, or knowledge bases
⭐ Anyone who wants a short, practical introduction to RAG before committing to a longer, broader course

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• date: 30 September 2026
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Advanced Network Forensics: Threat Hunting with Wireshark
Published 9/2026
Created by Learnsector LLP, Tushar Kanchan
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 27 Lectures ( 2h 13m ) | Size: 2.3 GB

Analyze PCAPs with Wireshark and tshark to isolate stealth C2, lateral movement, and stop enterprise breaches.

What you'll learn
⚡ Formulate testable hunt hypotheses using intelligence frameworks to uncover unflagged adversary persistence.
⚡ Engineer complex boolean Wireshark display filters to isolate anomalous TCP flag states and stealth port scans.
⚡ Profile network sessions using conversation tables and I/O graphs to detect covert rate-limited data exfiltration.
⚡ Reconstruct multi-stream TCP sessions and carve staged binaries to verify malicious payload hashes.
⚡ Evaluate TLS metadata and apply JA3, JA3S, and JA4 cryptographic fingerprints to unmask offensive toolkits.
⚡ Calculate Shannon entropy on outbound DNS traffic to identify automated domain generation algorithms and tunneling.
⚡ Audit SMB and DCE/RPC traffic streams to intercept administrative share abuse and PsExec service creation.
⚡ Uncover Kerberos ticket harvesting and cipher downgrade attacks by analyzing TGS-REQ packets and Event ID 4769.
⚡ Automate bulk PCAP triage pipelines with tshark, capinfos, and editcap to process multi-gigabyte captures.
⚡ Author defensible forensic incident reports and operationalize findings into production Suricata and Zeek rules.

Requirements
❗ Working knowledge of the TCP/IP protocol suite, standard networking concepts (IP addressing, subnets, routing), and core protocols (HTTP, DNS, TLS, SMB).
❗ Experience utilizing basic command-line environments (Linux/Unix shell or Windows PowerShell).
❗ Administrative workstation access capable of installing and running Wireshark, tshark, and related open-source network utilities.

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

Modern enterprise perimeters face sophisticated adversary evasion where endpoint detection and response (EDR) agents are blinded or bypassed via signed driver manipulation. While host telemetry degrades under kernel-level tampering, raw packet captures provide immutable evidentiary ground truth. Securing mission-critical infrastructure requires moving beyond reactive security operations center (SOC) triage queues toward hypothesis-driven network threat hunting.

This curriculum delivers an enterprise-grade framework for deep packet analysis, behavioral anomaly detection, and proactive hypothesis testing across high-throughput capture perimeters. Learners transition from passive observers to active forensic investigators, mastering raw protocol dissection, stream reassembly, binary carving, and statistical telemetry analysis. The content addresses mission-critical organizational needs, enabling teams to rapidly identify persistent compromise, contain lateral spread, and defend complex network topologies.

Key skills and technical competencies acquired include

✅ Master advanced Wireshark and tshark syntax to isolate covert communication channels.

✅ Evaluate cryptographic handshakes using JA3, JA3S, and JA4 fingerprinting frameworks.

✅ Uncover lateral movement sequences across SMB, DCE/RPC, WinRM, and Kerberos protocols.

✅ Quantify periodic Command-and-Control (C2) beaconing and mathematical sleep jitter.

✅ Detect DNS tunneling, Domain Generation Algorithms (DGA), and data exfiltration.

✅ Operationalize investigative findings into durable Suricata, Snort, and Zeek detection rules.

Frequently Asked Questions

What is hypothesis-driven threat hunting?

Hypothesis-driven threat hunting is a proactive security methodology where analysts assume network defenses have been bypassed. Investigators formulate testable, falsifiable behavioral questions based on threat intelligence and protocol baselines, querying raw packet telemetry to uncover persistent adversaries that evaded automated detection systems.

How does JA4 fingerprinting improve TLS traffic analysis?

JA4 improves TLS traffic analysis by sorting extensions and cipher suites alphabetically prior to hashing. This structure neutralizes client hash-randomization evasion techniques and generates human-readable protocol indicators, allowing security teams to identify offensive toolkits and rogue communication runtimes without performing full payload decryption.

Structured as an executive architecture briefing and applied forensic operational pipeline, this program bridges deep packet analysis with MITRE ATT&CK enterprise categorization, cross-source timestamp reconciliation, and legal chain of custody standards.

Updated for the 2025/2026 enterprise landscape, the course equips practitioners with resilient methodologies to triage multi-gigabit PCAP datasets, bypass modern obfuscation, and harden perimeter defenses.

Who this course is for
⭐ Enterprise SOC Analysts (Tiers 2 and 3) seeking to move from reactive alert triage to proactive hypothesis-driven network hunting.
⭐ Incident Response Engineers and Digital Forensics Specialists requiring packet-level proof to validate compromise and build defensible intrusion timelines.
⭐ Detection Engineers and Security Architects tasked with converting novel adversary techniques into resilient automated detection logic.
⭐ Network Security Professionals seeking to master deep packet dissection, traffic baselining, and covert channel identification.

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• views: 155
• date: 30 September 2026
• posted by: 0nelove

Microsoft Copilot for Accounting by Eduardo Aponte, PhD
Published 9/2026
Created by Eduardo Aponte, PhD
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 13 Lectures ( 1h 43m ) | Size: 1.2 GB

Automate financial reporting, accelerate reconciliations and optimize email communications using Microsoft Copilot AI

What you'll learn
⚡ Extract trends, highlight variances, and generate complex formulas in Excel using natural language.
⚡ Draft professional financial reports and board-ready presentations in minutes.
⚡ Compare datasets for faster reconciliations and build audit-ready documentation.
⚡ Design highly effective, accounting-specific prompts that deliver accurate results without hallucinations.

Requirements
❗ No previous experience is necessary.

Description
Welcome to Microsoft Copilot for Accounting.

Are you spending hours each month wrestling with manual reconciliations, drafting repetitive emails, or staring at a blank page when writing financial summaries? Want to elevate your role from data cruncher to strategic advisor? This course is built for finance professionals, auditors, and bookkeepers who want to eliminate tedious tasks and leverage AI to work faster and smarter.

You'll learn how to integrate Microsoft Copilot seamlessly into the tools you already rely on daily—focusing on Excel. We'll move past the hype and focus strictly on practical accounting workflows. Everything we cover is grounded in real-world finance scenarios and strict enterprise data privacy standards, ensuring your confidential information remains secure.

By the end, you'll be able to

· Extract trends, highlight variances, and generate complex formulas in Excel using natural language.

· Draft professional financial reports and board-ready presentations in minutes.

· Compare datasets for faster reconciliations and build audit-ready documentation.

· Design highly effective, accounting-specific prompts that deliver accurate results without hallucinations.

Actionable frameworks, prompt libraries, and hands-on labs will help you implement these AI strategies immediately—no prior coding or AI experience required.

If you're ready to trade manual data entry for high-value strategic analysis, hit Start—and transform your accounting workflow today.

Who this course is for
⭐ All

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• views: 142
• date: 30 September 2026
• posted by: 0nelove

Advanced Context Engineering: Systems, Evaluation & Research
Published 9/2026
Created by Superposition AI
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English + subtitle | Duration: 62 Lectures ( 19h 16m ) | Size: 8.6 GB

Build, measure and test context systems for text, knowledge and agents, and check ideas from research yourself.

What you'll learn
⚡ Build context you can verify: extract text from PDFs, split documents, and assemble task packets whose claims trace to a file and a line.
⚡ Compare word search (BM25), embeddings, rank fusion and reranking on the same questions, and choose a method from measured evidence.
⚡ Chain dependent searches, add source-grounded context to chunks, and audit a small NetworkX graph against current permissions.
⚡ Manage long context with budgeted selection, checked shortening, handoffs after compaction, and caches whose keys include content and permissions.
⚡ Build memory with provenance, validity dates and lifecycle rules in SQLite, and test it across separate conversations.
⚡ Build a local MCP server with typed inputs, pagination and failure handling that does not invent answers.
⚡ Run long tasks with explicit state and checkpoints, resume after a stop, and measure whether several agents beat one.
⚡ Detect instructions hidden in data, keep sources through handoffs, and enforce permissions in code, testing for leaks and for over-blocking.
⚡ Evaluate systematically: test checkers against hand labels, catch regressions, run ablations, and improve instructions from feedback.
⚡ Read a research paper for its claim and limits, test its idea against fair baselines, and finish a system on a new domain with a sealed test.

Requirements
❗ Context Engineering, Part I, or equivalent comfort with the Codex CLI: working in a terminal with folders and files, and checking what a tool actually did. Lesson 1 assumes Codex is already installed and signed in.
❗ A Codex CLI account of your own. The lessons were recorded with GPT-6-Luna at medium reasoning, and results on a different setup may differ. Model usage counts against your account, and the course does not promise a fixed cost.
❗ A Linux (or other POSIX) shell, Python 3.12 and the uv package manager, which lesson 2 walks through installing. Git is added in lesson 6, and free Python packages such as pytest are installed as lessons need them. Only the Linux route was demonstrated.
❗ Willingness to read and run Python line by line. No programming knowledge beyond Part I is assumed, and Python starts in lesson 2, but the pace is fast and you may need to stop and look things up.
❗ Optional: an Alibaba Model Studio account with a Singapore-region API key for the live embedding and reranking calls in lessons 15, 16 and 18. Saved responses are supplied so you can follow without an account; live calls may be billed.
❗ Time for real work: lessons run from about 10 to about 42 minutes, and the first half of the course is the heaviest.

Description
This course contains the use of artificial intelligence.

Advanced Context Engineering is the advanced course that follows Context Engineering, Part I. It follows one idea through 60 lessons in 12 modules, about 19 hours in total: the context an AI agent works with is data that you assemble, check and measure. You start from a small baseline and finish with a complete system that you test on a sealed test set and defend with evidence.

Along the way you prepare documents and PDFs you can trust, assemble task packets whose claims trace to a file and a line, and compare word search, embeddings, rank fusion and reranking on the same questions. You build chained searches and a small graph, manage long context with budgets, handoffs and caches, and build memory with sources, dates and SQLite. You then build a local MCP server, run long tasks that can stop and resume, measure whether several agents help, plant instructions in data, enforce permissions in code, and build evaluation suites that catch regressions and show which component actually helped. The last modules cover reading a research paper and testing its idea, and a final project: a staff assistant for a fictional warehouse handbook.

Every lesson follows the same pattern: a short refresh, slides that explain the new idea, a continuous demonstration in Codex CLI, a close reading of the results, and a recap. Almost every lesson ends with an exercise and a solution to compare against. Lessons come with starter files and saved results and, in the later lessons, offline checks and scripted stand-ins that make no model calls, so you can inspect what was recorded before spending anything. The demonstrations use Codex CLI 0.156.1 with GPT-6-Luna at medium reasoning, and all data is synthetic, drawn from fictional organizations.

It is written for graduates of Part I and for people who write code or are ready to learn as they go. No programming knowledge beyond Part I is assumed, and Python is introduced from lesson 2, but the pace is fast and the code is read line by line. If Context Engineering or the Codex CLI is new to you, start with Part I.

What it is not: it does not promise research training equivalent to a doctorate. GraphRAG, GEPA, ACE and Recursive Language Models are taught as principles and as small, clearly labeled adapted demonstrations, not as reproductions of the papers. Most results come from small synthetic experiments with one recorded run, so they show a method and not a benchmark, and the safety lessons demonstrate mechanisms without promising protection in production. OCR of scanned documents, real-time audio and video, and model training are outside the course. Model usage counts against your own account, and the course does not promise a fixed cost.

Who this course is for
⭐ Graduates of Context Engineering, Part I who want to go from prompts and folders to systems they can measure and improve.
⭐ Developers and engineers who build assistants, retrieval or agent systems and want habits for evaluation, permissions and testing.
⭐ Learners without a programming background who are ready to learn Python as they go and to stop and look things up.
⭐ People who want to read a research paper and test one of its ideas themselves in a small, fair experiment.

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• views: 121
• 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: 149
• date: 30 September 2026
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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: 280
• 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: 249
• 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: 180
• 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: 158
• 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: 202
• 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: 221
• 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

Homepage

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• views: 61
• date: 30 September 2026
• posted by: DrZero

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