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From Beginner To Full Stack Hero

Tag: Intermediate

  • Prompt Engineering Fundamentals: Talk to AI

    Prompt Engineering Fundamentals: Talk to AI

    ✍️ Prompt Engineering Fundamentals: Speak AI’s Language with Precision

    🧾 Course Description

    Prompt engineering is the skill of designing effective inputs to get accurate, creative, and actionable outputs from Generative AI models like GPT-4, Claude, LLaMA, Gemini, and Stable Diffusion. In “Prompt Engineering Fundamentals”, you’ll learn to craft, refine, and automate prompts for use in text, code, images, chatbots, and more.

    Designed for developers, content creators, AI product designers, educators, and automation experts, this course turns you into a prompt expert with a mix of strategy, structure, and experimentation.

    Whether you’re building AI copilots, chat agents, creative pipelines, or marketing tools, mastering prompts is your key to unlocking GenAI’s full potential.

    ✅ Key Benefits

    • 🧠 Master the Craft of Prompting — Understand how large language models think and respond
    • 🎯 Structured & Strategic — Learn techniques like role prompting, few-shot, chain-of-thought
    • 💬 Cross-Modal Mastery — Text, code, image, and tool-augmented prompting
    • 🤖 Build Prompt-Driven Apps — Ready for ChatGPT, LangChain, and OpenAI API integration
    • 🚀 Boost Productivity & Creativity — Write, code, ideate, and build faster with AI

    🎯 Pre-requisites

    • Basic understanding of Generative AI tools (like ChatGPT, Claude, DALL·E)
    • Comfortable with using text editors, APIs, or no-code tools
    • Optional: experience in Python, copywriting, or LLMs helpful but not required
    • No prior AI/ML background needed

    📚 Curriculum Breakdown

    📘 Module 1: Introduction to Prompt Engineering

    • What is a prompt? What makes it effective?
    • Language models and prompt-response architecture
    • Prompt vs fine-tuning vs embeddings vs agents

    ✍️ Module 2: Core Prompting Techniques

    • Instructional prompting
    • Role prompting (You are a...)
    • Zero-shot vs one-shot vs few-shot
    • Prompt patterns for summarization, rewriting, ideation, coding

    🔗 Module 3: Advanced Prompt Patterns

    • Chain-of-Thought (CoT) and self-consistency
    • ReAct (Reasoning + Action)
    • Tree of Thought and step-wise solving
    • Injecting context, memory, and constraints

    🛠️ Module 4: Prompt Engineering in Tools & APIs

    • Prompting in OpenAI Playground, ChatGPT, Claude
    • API-based prompting via OpenAI or HuggingFace
    • Prompt templates and injection in LangChain / LlamaIndex
    • Temperature, top_p, max_tokens explained

    🎨 Module 5: Cross-Modal Prompting

    • Prompting DALL·E, Midjourney, Stable Diffusion
    • Crafting image prompts: style, composition, structure
    • Prompting for audio, video, and multi-turn conversations

    📊 Module 6: Evaluation & Iteration

    • Measuring output quality: relevance, bias, hallucination
    • Refining prompts with prompt chaining and feedback loops
    • Designing reusable and modular prompts

    🧪 Module 7: Final Projects

    Choose one:

    • Build a multi-turn conversational AI prompt suite
    • Create a prompt-powered writing assistant
    • Design text-to-image pipelines using structured prompts
    • Build prompt workflows using Zapier + OpenAI API

    ⏱️ Estimated Duration

    Daily Study TimeEstimated DurationIdeal For
    2 hours/day10–12 days (~2 weeks)Best for thoughtful learning pace
    4 hours/day5–6 days (1 week)Balanced practice + experimentation
    6 hours/day3–4 days (bootcamp)Intense, project-driven immersion

    🎓 Outcome

    By the end of Prompt Engineering Fundamentals, you will:

    • Write clear, powerful prompts for text, code, image, and chat
    • Use prompting as a superpower in creative, analytical, and technical work
    • Automate and integrate prompt workflows using APIs or GenAI tools
    • Be ready for AI product design, LLM app development, and AI agent engineering
  • Generative AI Tools & Workflows: Create with AI

    Generative AI Tools & Workflows: Create with AI

    🎨 Generative AI Tools & Workflows: Build, Create, and Automate with Intelligence

    🧾 Course Description

    Generative AI is revolutionizing the way we create content, design experiences, and build intelligent systems. In “Generative AI Tools & Workflows”, you’ll explore how to use modern GenAI platforms — like OpenAI, HuggingFace, Google Vertex AI, and Stability AI — to generate text, images, code, and audio, and integrate them into real-world applications.

    This course is designed for developers, creatives, product designers, marketers, and AI enthusiasts who want to master tools like ChatGPT, DALL·E, Midjourney, GitHub Copilot, and build end-to-end GenAI workflows that go beyond experimentation and into usable products.

    ✅ Key Benefits

    • 🧠 Understand GenAI Foundations — Learn how LLMs, diffusion models, and transformers work
    • 🔌 Tool-Focused & Practical — Hands-on with OpenAI, HuggingFace, DALL·E, and more
    • 🤖 Automate & Accelerate — Build GenAI agents, pipelines, and integrations
    • 🧰 No-Code to Code — Combine GUI tools and APIs for flexible workflows
    • 🚀 Real Use Cases — Product copywriting, code generation, image synthesis, voice AI, and chatbots

    🎯 Pre-requisites

    • Basic knowledge of Python, APIs, and working with JSON
    • Experience with web apps, automation, or creative tools is helpful
    • No prior machine learning or deep AI background required

    📚 Curriculum Breakdown

    📘 Module 1: Foundations of Generative AI

    • What is Generative AI? Types: Text, Image, Code, Audio, Video
    • Core models: LLMs (GPT), Diffusion (Stable Diffusion), GANs
    • Understanding prompts, embeddings, transformers

    💬 Module 2: Text Generation Workflows

    • Prompt engineering with OpenAI GPT-4 / ChatGPT API
    • Use cases: blog writing, summarization, email generation, chatbot flows
    • Prompt chaining, few-shot examples, system messages

    🎨 Module 3: Image Generation Tools

    • Tools: DALL·E 3, Midjourney, Stable Diffusion, Canva AI
    • Text-to-image prompts, styles, inpainting, variations
    • Use cases: social media graphics, storyboarding, thumbnails

    🧠 Module 4: Code Generation & Developer Tools

    • Using GitHub Copilot, OpenAI Codex
    • Generating Python/JavaScript snippets
    • Automating documentation and test generation
    • Safety and hallucination issues in generated code

    🧩 Module 5: APIs, Agents, and Workflows

    • Calling GenAI models via API (OpenAI, HuggingFace, Replicate)
    • Building intelligent agents using LangChain or custom Python wrappers
    • Connecting multiple tools into one pipeline (e.g., input → process → output → deploy)

    🎛️ Module 6: Automation, Ethics & Limitations

    • Using Zapier, Make, and no-code tools to trigger GenAI flows
    • Handling latency, token limits, hallucination, cost
    • Ethical considerations: bias, misuse, data privacy

    🧪 Module 7: Final Projects (Choose One)

    • Build a content generator for blogs/social media
    • Create a prompt-based image+text portfolio builder
    • Develop a simple AI chatbot using OpenAI API + LangChain
    • Deploy a GitHub Copilot-style code assistant

    ⏱️ Estimated Duration

    Daily Study TimeEstimated DurationIdeal For
    2 hours/day14–16 days (~2 weeks)Explorers and builders
    4 hours/day6–8 days (1 week)Balanced project-driven learners
    6 hours/day3–4 days (bootcamp)Intensive fast-track learning

    🎓 Outcome

    By the end of Generative AI Tools & Workflows, you will:

    • Use text, image, and code generation tools effectively
    • Integrate GenAI APIs into products and creative workflows
    • Build simple agents and automation using OpenAI and LangChain
    • Be ready to pursue LLM app development, GenAI product design, or AI agent engineering
  • Introduction to Machine Learning: ML & DL Unlocked

    Introduction to Machine Learning: ML & DL Unlocked

    🧠 Introduction to Machine Learning & Deep Learning: From Data to Intelligence

    🧾 Course Description

    Machine Learning (ML) and Deep Learning (DL) are the engines powering today’s intelligent systems — from recommendation engines and fraud detection to self-driving cars and chatbots. In “Introduction to Machine Learning & Deep Learning”, you’ll learn the mathematical intuition, practical tools, and coding skills to build and deploy your first models.

    This beginner-friendly course covers supervised and unsupervised learning, neural networks, model evaluation, and hands-on implementation using Python, Scikit-learn, TensorFlow, and Keras. Whether you’re a developer, data enthusiast, or aspiring AI engineer, this course builds the right foundation for your ML journey.

    ✅ Key Benefits

    • 🧠 Build Strong Foundations — Learn core concepts like regression, classification, loss functions, and overfitting
    • 🧰 Hands-On Implementation — Code real ML/DL models using Scikit-learn and TensorFlow/Keras
    • 🔄 Understand Model Lifecycle — Train, evaluate, optimize, and deploy ML models
    • 🎯 Clarity over Complexity — Visual explanations of neural networks and backpropagation
    • 🚀 Career Gateway — Prepares you for ML specialization, GenAI, or MLOps tracks

    🎯 Pre-requisites

    • Proficiency with Python (data types, functions, loops, libraries like NumPy/Pandas)
    • Basic understanding of linear algebra, statistics, and probability (conceptual level)
    • No prior experience in ML or DL required

    📚 Curriculum Breakdown

    📘 Module 1: Introduction to Machine Learning

    • What is ML? Types of ML: supervised, unsupervised, reinforcement
    • Use cases: NLP, vision, recommendation, healthcare
    • ML workflow: data → model → prediction → evaluation

    📊 Module 2: Data Preprocessing & Exploration

    • Cleaning and transforming data
    • Feature engineering and normalization
    • Data splitting: train, test, validation

    🔢 Module 3: Supervised Learning Algorithms

    • Linear regression, logistic regression
    • Decision trees, random forest, SVM
    • Model metrics: accuracy, precision, recall, F1-score

    🧩 Module 4: Unsupervised Learning

    • Clustering: K-Means, Hierarchical
    • Dimensionality reduction: PCA
    • Real-life use cases: customer segmentation, anomaly detection

    🧠 Module 5: Introduction to Deep Learning

    • What is a neural network? Perceptron to MLP
    • Layers, weights, activation functions
    • Forward pass and backpropagation (intuitive explanation)

    ⚙️ Module 6: Building Neural Networks with TensorFlow/Keras

    • Model architecture and compiling
    • Training and evaluating a model
    • Avoiding overfitting: dropout, early stopping, batch size

    🧪 Module 7: Real-World Projects

    Choose one:

    • Predict house prices (regression)
    • Classify images (MNIST / CIFAR-10)
    • Detect spam messages (NLP)

    ⏱️ Estimated Duration

    Daily Study TimeEstimated DurationIdeal For
    2 hours/day16–18 days (~3 weeks)Balanced pace with practice
    4 hours/day8–9 days (~1.5 weeks)For committed learners
    6 hours/day5–6 days (bootcamp)Immersive and project-intensive

    🎓 Outcome

    By the end of Introduction to Machine Learning & Deep Learning, you will:

    • Understand the mathematics and workflow behind ML/DL models
    • Build, train, and evaluate your own models using Scikit-learn and Keras
    • Apply ML/DL to real-world problems in vision, text, and prediction
    • Be ready to advance into deep learning, MLOps, LLMs, or AI product development
  • AI Concepts for Developers

    AI Concepts for Developers

    🤖 AI Concepts for Developers: Build Smarter Software with Intelligent Design

    🧾 Course Description

    Artificial Intelligence is no longer limited to research labs — it’s now a core part of modern software development. “AI Concepts for Developers” is designed to help developers, software engineers, and architects understand how AI works, where it fits into their tech stack, and how to start building intelligent features using practical tools and APIs.

    This course covers foundational concepts like machine learning, neural networks, natural language processing (NLP), computer vision, and introduces frameworks like TensorFlow, PyTorch, and OpenAI APIs — all explained with real-world developer use cases.

    Whether you’re building a chatbot, a recommendation engine, or a smart search feature, this course gives you the knowledge to get started confidently.

    ✅ Key Benefits

    • 🧠 Understand AI Without a PhD — Learn ML and AI principles as a developer
    • 🔌 Practical & Tool-Centric — Use Python, APIs, and SDKs to implement AI features
    • 🌍 Real-World Use Cases — Image recognition, sentiment analysis, intelligent automation
    • 🧰 Developer-Centric Approach — Write and deploy simple AI models, not just theory
    • 🚀 Foundation for Advanced Learning — Ideal gateway to deeper ML, LLMs, and GenAI tracks

    🎯 Pre-requisites

    • Familiarity with Python or JavaScript (control flow, functions, data structures)
    • Comfort with APIs, JSON, and basic HTTP concepts
    • No prior experience in AI or machine learning required

    📚 Curriculum Breakdown

    📘 Module 1: Introduction to AI & ML

    • What is AI, ML, DL? Differences and use cases
    • Supervised vs unsupervised learning
    • Real-world applications and trends

    🧠 Module 2: Key AI Concepts for Developers

    • Classification, regression, clustering
    • Data preprocessing basics
    • Accuracy, precision, recall explained

    🧰 Module 3: AI Tools & Frameworks

    • Python + Scikit-learn basics
    • TensorFlow & PyTorch (intro only)
    • Using HuggingFace & OpenAI APIs

    💬 Module 4: Natural Language Processing (NLP)

    • Tokenization, sentiment analysis
    • Text classification using pretrained models
    • Building simple chatbots using OpenAI or Cohere

    🖼️ Module 5: Computer Vision Basics

    • Image classification with MobileNet or ResNet
    • Object detection using pretrained models
    • Using OpenCV for image processing tasks

    📦 Module 6: Integrating AI into Applications

    • AI as a microservice
    • Using AI in frontend/backend via REST APIs
    • Security, latency, cost considerations

    🧪 Module 7: Final Project

    • Choose one:
      • Smart chatbot using OpenAI API
      • AI image recognizer (upload + predict)
      • AI-powered feedback summarizer or recommender

    ⏱️ Estimated Duration

    Daily Study TimeEstimated DurationIdeal For
    2 hours/day14–16 days (~2 weeks)Developers with full-time jobs
    4 hours/day7–8 days (~1 week)Intermediate learners
    6 hours/day4–5 days (bootcamp)Fast-track with mini-project

    🎓 Outcome

    By the end of AI Concepts for Developers, you will:

    • Understand the building blocks of AI and ML
    • Be able to use popular APIs and Python libraries to solve real-world problems
    • Build your first AI-powered features into apps
    • Be ready to pursue deeper learning in ML, LLMs, or GenAI
  • End-to-End Testing with Cypress

    End-to-End Testing with Cypress

    🌐 End-to-End Testing with Cypress: Automate the User Journey

    🧾 Course Description

    Cypress is a fast, powerful, and developer-friendly tool for end-to-end (E2E) testing of modern web applications. In “End-to-End Testing with Cypress”, you’ll learn to simulate real user behavior, verify critical workflows, and catch bugs before your users do — all within a sleek and scriptable browser-based environment.

    From first click to final checkout, this course equips you with practical Cypress testing skills including UI interaction, network mocking, visual assertions, custom commands, and CI integration.

    Perfect for frontend developers, QA engineers, and test automation teams, this course will help you ensure that your web app delivers a flawless user experience.

    ✅ Key Benefits

    • 🎯 Test Like a User — Click buttons, type inputs, and validate DOM behavior
    • ⚡ Fast & Reliable — Runs in real browsers with instant feedback and time travel
    • 🔁 Fully Scriptable — Chain commands, add waits, assert like a boss
    • 🔍 Debug with DevTools — See what happened and where it failed
    • 🔌 CI/CD Ready — Integrates with GitHub Actions, GitLab CI, CircleCI, and more

    🎯 Pre-requisites

    • Solid understanding of HTML, CSS, and JavaScript
    • Familiarity with modern web apps (React, Angular, Vue, etc.)
    • Optional: basic experience with Git, npm, or testing concepts

    📚 Curriculum Breakdown

    🧱 Module 1: Getting Started with Cypress

    • What is E2E testing vs unit/integration testing
    • Installing Cypress in a project
    • Cypress Test Runner: Overview and UI

    🖱️ Module 2: Writing Your First Tests

    • Visiting URLs with cy.visit()
    • Querying elements: cy.get(), cy.contains()
    • Performing actions: click(), type(), check(), etc.

    ✅ Module 3: Assertions & Validation

    • Built-in assertions with should(), expect()
    • Checking visibility, value, URL, length, classes
    • Timing strategies: retries and waiting

    🌐 Module 4: Testing Real App Workflows

    • Testing login, navigation, form validation
    • Testing lists, tables, and modals
    • Handling dynamic content and loading spinners

    🕹️ Module 5: Advanced Cypress Features

    • Fixtures for test data
    • Custom commands for DRY code
    • Aliases & chaining for better readability
    • Network mocking with cy.intercept()

    📷 Module 6: Debugging and Screenshots

    • Time travel and command log
    • Taking screenshots and videos
    • Debugging in browser DevTools

    🔁 Module 7: CI/CD Integration & Headless Runs

    • Headless mode testing
    • Running in GitHub Actions / Jenkins
    • Generating and publishing reports

    🧪 Module 8: Final Project

    • End-to-End Test Suite for a Sample Web App
      • Full test case coverage: login → CRUD → logout
      • Mocking API responses
      • CI execution + dashboard report

    ⏱️ Estimated Duration

    Daily Study TimeEstimated DurationIdeal For
    2 hours/day10–12 days (~2 weeks)Steady pace with hands-on demos
    4 hours/day5–6 days (1 week)Ideal for project-based learners
    6 hours/day3–4 days (bootcamp)Fast-paced with full walkthrough

    🎓 Outcome

    By the end of End-to-End Testing with Cypress, you will:

    • Automate UI workflows like a real user
    • Handle asynchronous content, network calls, and authentication
    • Organize maintainable test suites with reusable components
    • Integrate Cypress tests into your CI pipelines for release confidence
  • Jest Framework: The quest of Javascript to run as per expectations

    Jest Framework: The quest of Javascript to run as per expectations

    🧪 Test with Jest: Fast & Reliable Unit Testing for JavaScript

    🧾 Course Description

    Jest is the most popular testing framework for JavaScript applications — especially in the React and Node.js ecosystem. In “Test with Jest”, you’ll learn how to write unit tests, snapshot tests, and mock dependencies to ensure your code works reliably, every time.

    This hands-on course helps developers build confidence in their JS/TS codebases using Jest’s powerful APIs, fast execution, and simple syntax. From basic assertions to asynchronous testing, mocking, and test coverage, this course equips you with everything needed to test modern frontend and backend apps.

    ✅ Key Benefits

    • ⚡ Blazing-Fast Testing — Run lightweight, in-memory tests with zero config
    • 🔒 Write Bug-Free Code — Catch regressions and failures early
    • 📦 Out-of-the-Box Magic — Built-in assertion library, coverage reports, and mocking
    • 🔁 Great for React & Node — Preferred choice for full-stack JavaScript apps
    • 🧰 CI-Friendly — Works well with GitHub Actions, GitLab CI, CircleCI, etc.

    🎯 Pre-requisites

    • Familiarity with JavaScript or TypeScript (functions, variables, objects, arrays)
    • Basic knowledge of Node.js and package managers (npm, yarn)
    • Optional: experience with React or APIs is helpful
    • No prior testing experience needed

    📚 Curriculum Breakdown

    📘 Module 1: Introduction to Testing with Jest

    • Why testing matters
    • Installing Jest in a Node.js or React project
    • Writing and running your first test

    🧪 Module 2: Core Testing Concepts

    • The AAA pattern: Arrange, Act, Assert
    • Matchers and assertions (toBe, toEqual, toContain, etc.)
    • Grouping tests with describe, beforeEach, afterEach

    🧱 Module 3: Mocking and Spies

    • Manual and automatic mocks
    • Using jest.fn(), jest.mock()
    • Asserting calls, parameters, return values

    🔁 Module 4: Asynchronous Testing

    • Testing promises and async/await functions
    • Using done(), resolves/rejects, and async functions
    • Testing APIs and database calls

    🧪 Module 5: Snapshot Testing

    • Snapshot test basics
    • Best practices and snapshot updating
    • Use cases in React, UI components

    📊 Module 6: Code Coverage and Debugging

    • Generating coverage reports
    • Understanding uncovered branches/lines
    • Debugging failing tests

    🧰 Module 7: Integrating with CI and Git Hooks

    • Running tests on pre-commit (husky, lint-staged)
    • Using Jest with GitHub Actions or GitLab CI
    • Test report output formats

    🧪 Module 8: Final Project

    • Mini Test Suite for a Node.js Utility or React Component
      • Write unit and async tests
      • Add mock functions
      • Include snapshots (optional)
      • Generate and analyze test coverage

    ⏱️ Estimated Duration

    Daily Study TimeEstimated DurationIdeal For
    2 hours/day8–10 days (~2 weeks)Part-time with examples
    4 hours/day4–5 days (1 week)Ideal for rapid learners
    6 hours/day2–3 days (bootcamp)Immersive with hands-on focus

    🎓 Outcome

    By the end of Test with Jest, you will:

    • Write efficient, readable, and reusable unit tests
    • Mock functions, modules, and APIs for isolation
    • Perform snapshot and asynchronous testing
    • Automate testing as part of your dev workflow and CI/CD pipelines
  • Github Actions: Code, Push, Automate

    Github Actions: Code, Push, Automate

    ⚙️ GitHub Actions Mastery: Automate CI/CD from Your Repo

    🧾 Course Description

    GitHub Actions is a powerful CI/CD platform built right into GitHub — allowing you to automate builds, tests, deployments, and workflows directly from your repositories.

    In “GitHub Actions Mastery”, you’ll learn to write, manage, and optimize workflows using YAML syntax, reusable actions, environment secrets, matrix builds, and deployment strategies. This hands-on course is ideal for developers, DevOps engineers, and open-source maintainers who want to create fast, secure, and reliable automation pipelines.

    By the end of this course, you’ll be able to automate anything — from code validation and test runs to Docker builds, cloud deployments, and Slack notifications — without leaving GitHub.

    ✅ Key Benefits

    • ⚙️ Native CI/CD in GitHub — No external tool setup required
    • 🔁 Automate Everything — Build, test, lint, deploy, notify, tag, and release
    • 🧱 Modular and Reusable — Use public actions or build your own
    • 🔐 Secure Deployments — Manage secrets, environments, and approvals
    • 🚀 Perfect for Individuals and Teams — From solo projects to enterprise pipelines

    🎯 Pre-requisites

    • Familiarity with Git & GitHub (push, pull, clone, branches)
    • Comfortable using basic YAML syntax
    • Optional: experience with Docker, Node.js, or build tools like Maven/npm is helpful
    • No prior CI/CD experience required

    📚 Curriculum Breakdown

    🔧 Module 1: Introduction to GitHub Actions

    • What is GitHub Actions and how it compares to Jenkins/GitLab CI
    • Key concepts: workflows, jobs, steps, runners, triggers
    • Enabling Actions in a GitHub repo

    📄 Module 2: Writing Your First Workflow

    • main.yml structure and location
    • Trigger on push, pull_request
    • Running shell commands using runs-on, steps, and run
    • Using actions/checkout and actions/setup-*

    🧱 Module 3: Workflow Features

    • Using env, secrets, if conditionals
    • Artifacts, caching, matrix builds
    • Reusable workflows and templates

    🧰 Module 4: Popular Use Cases

    • Run unit tests on push
    • Lint and format code
    • Auto-assign PR reviewers
    • Tag releases and publish to npm/DockerHub

    ☁️ Module 5: Deployments with GitHub Actions

    • Using environments and manual approvals
    • Deploying to:
      • Netlify/Vercel
      • AWS (via IAM credentials)
      • Heroku, Render, or Firebase
    • Rollback strategies and monitoring tips

    🧪 Module 6: Final Project

    • CI/CD Pipeline for a Node.js or Python app
      • Run tests, lint, build, and deploy
      • Store artifacts and use secrets
      • Notify team via Slack or email

    ⏱️ Estimated Duration

    Daily Study TimeEstimated DurationNotes
    2 hours/day8–10 days (~2 weeks)Relaxed pace with practice
    4 hours/day4–5 days (1 week)Focused pace + demo pipelines
    6 hours/day2–3 days (bootcamp)Full immersion + project-ready

    🎓 Outcome

    By the end of GitHub Actions Mastery, you will:

    • Automate testing, deployment, and DevOps workflows from within GitHub
    • Write and manage reusable YAML-based workflows
    • Securely handle credentials and secrets in real-world projects
    • Be ready to implement CI/CD pipelines for solo projects, teams, and open-source contributions
  • CI/CD Concepts: Release Like a Pro

    CI/CD Concepts: Release Like a Pro

    🔄 CI/CD Essentials: Automate, Integrate, and Deliver with Confidence

    🧾 Course Description

    Continuous Integration (CI) and Continuous Delivery/Deployment (CD) are the backbone of modern DevOps workflows, enabling teams to deliver software faster, safer, and more reliably. In “CI/CD Essentials”, you’ll learn the core principles, tools, and real-world workflows that drive automation across the development pipeline.

    This vendor-neutral course focuses on concepts, best practices, and architecture — using examples from Git, Jenkins, GitHub Actions, Docker, and Kubernetes. Perfect for developers, testers, and DevOps engineers seeking a solid foundation in CI/CD principles before diving into specific tools.

    ✅ Key Benefits

    • 🚀 Understand CI/CD Architecture — Learn pipelines, triggers, build stages, and deployments
    • 🔄 End-to-End Automation — From code commit to production-ready release
    • 🧩 Tool-Agnostic Knowledge — Concepts apply to Jenkins, GitHub Actions, GitLab CI, CircleCI, etc.
    • 🧪 Test & Validate Faster — Integrate testing, linting, and code quality checks early
    • ☁️ Bridge Dev & Ops — Align developer workflows with production reliability

    🎯 Pre-requisites

    • Familiarity with basic software development workflow (Git, code editing, debugging)
    • Optional: Basic exposure to Git, build tools (Maven, npm), or terminal usage
    • No prior CI/CD tool experience required

    📚 Curriculum Breakdown

    📘 Module 1: Introduction to CI/CD

    • What is CI? What is CD?
    • Benefits & challenges of automation
    • CI vs CD vs CD (Continuous Deployment)

    🔁 Module 2: CI/CD Workflow Components

    • Code repository & version control (Git basics)
    • Build automation: compile, package, test
    • Artifact creation & storage

    🛠️ Module 3: CI Pipeline Anatomy

    • Pipeline stages: checkout, build, test, deploy
    • Triggers: commit, schedule, manual
    • Feedback loops: failing fast and early

    🔒 Module 4: Testing & Code Quality

    • Unit testing and test automation
    • Static code analysis (SonarQube, ESLint)
    • Security scanning and shift-left testing

    🔄 Module 5: CD Pipelines

    • Staging vs production environments
    • Manual approvals and gated releases
    • Blue-green and canary deployments

    🔌 Module 6: Tools & Ecosystem Overview

    • Popular CI/CD tools: Jenkins, GitHub Actions, GitLab CI, CircleCI
    • Introduction to pipeline-as-code
    • Docker and K8s in CI/CD pipelines (overview)

    🧪 Module 7: CI/CD Use Case Project

    • Simulate a CI/CD pipeline for a web app:
      • Git push → run tests → build → deploy to staging
      • Optional: include code quality check and rollback step

    ⏱️ Estimated Duration

    Daily Study TimeEstimated DurationLearning Style
    2 hours/day10–12 days (~2 weeks)Ideal for foundational learners
    4 hours/day5–6 days (1 week)Good pace with live demos
    6 hours/day3–4 days (bootcamp)Hands-on intensive & accelerated

    🎓 Outcome

    By the end of CI/CD Essentials, you will:

    • Understand CI/CD lifecycle, purpose, and real-world benefits
    • Design and explain modern pipelines from code commit to deployment
    • Be prepared to adopt any CI/CD tool (Jenkins, GitHub Actions, GitLab CI)
    • Confidently work in or contribute to DevOps and automation teams
  • Jenkins: Code Pipelines on their way to Production

    Jenkins: Code Pipelines on their way to Production

    ⚙️ Jenkins Jumpstart: Master Continuous Integration & Delivery

    🧾 Course Description

    Jenkins is the most popular open-source automation server used to implement Continuous Integration (CI) and Continuous Delivery (CD) in modern software pipelines. In “Jenkins Jumpstart”, you’ll learn how to automate building, testing, and deploying software using pipelines and plugins.

    This hands-on course takes you from installing Jenkins to writing pipeline-as-code with Jenkinsfile, integrating Git, Docker, Maven/Gradle, and even deploying to cloud or staging environments. Whether you’re a developer, DevOps engineer, or release manager — Jenkins is a must-have tool in your workflow.

    ✅ Key Benefits

    • 🚀 Automate Everything — Build, test, and deploy software without manual steps
    • 🔁 Implement CI/CD — Set up reliable integration and delivery pipelines
    • 🔌 Plugin-Powered Flexibility — Use Git, Docker, Maven, Node, AWS, and more
    • 🧰 Pipeline as Code — Learn to version and maintain Jenkins configurations
    • 💼 Enterprise Ready — Gain skills sought after in modern software teams

    🎯 Pre-requisites

    • Basic understanding of software development life cycle (SDLC)
    • Familiarity with Git and command-line interface (CLI)
    • Exposure to build tools (Maven, Gradle, npm) is helpful
    • Optional: Basic understanding of Docker or Linux

    📚 Curriculum Breakdown

    🧱 Module 1: Introduction to Jenkins

    • What is Jenkins? Why CI/CD matters
    • Jenkins architecture and use cases
    • Installing Jenkins on local or cloud (Linux or Docker)

    🧪 Module 2: Jenkins Dashboard & Freestyle Jobs

    • Jenkins UI tour
    • Creating freestyle jobs
    • Source code integration with Git
    • Triggering builds manually and automatically

    🧰 Module 3: Build Tools Integration

    • Using Maven, Gradle, or npm in builds
    • Understanding build steps and post-build actions
    • Generating artifacts and storing them

    🔄 Module 4: Pipeline-as-Code (Declarative & Scripted)

    • What is a Jenkinsfile?
    • Creating basic declarative pipelines
    • Stages, steps, environment, post conditions
    • Scripted pipelines overview

    🔌 Module 5: Plugins & Integrations

    • Installing and configuring plugins
    • GitHub, Slack, Docker, JUnit integrations
    • Adding credentials securely

    🔁 Module 6: Triggering & Automation

    • Webhooks and GitHub auto-triggers
    • Poll SCM and scheduled builds
    • Conditional and parallel execution

    🐳 Module 7: Docker & Deployment (Optional Deep Dive)

    • Using Docker agents in pipelines
    • Building Docker images in Jenkins
    • Deploying to test/staging environments

    🛠️ Module 8: Final Project

    • CI/CD Pipeline for a Web App
      • Code checkout from GitHub
      • Run tests, package with Maven
      • Build Docker image
      • Push to DockerHub
      • Deploy to staging server

    ⏱️ Duration Estimates (with hands-on practice)

    Daily Study TimeEstimated DurationNotes
    2 hours/day12–14 days (~2 weeks)Ideal for professionals
    4 hours/day6–7 days (~1 week)Balanced pace with project
    6 hours/day3–4 days (bootcamp)Fast-paced with practice

    🎓 Outcome

    By the end of Jenkins Jumpstart, you will:

    • Set up and manage Jenkins with real CI/CD workflows
    • Automate testing, building, and deployment using pipelines
    • Write and maintain Jenkinsfiles for version-controlled automation
    • Be ready to integrate Jenkins into DevOps, microservices, and cloud workflows
  • Docker: Build Once, Ship Everywhere

    Docker: Build Once, Ship Everywhere

    🐳 Docker Unboxed: Containerization for Developers & DevOps

    🧾 Course Description

    Docker is a game-changing tool for modern software development — enabling you to package applications with all their dependencies into lightweight, portable containers.

    In “Docker Unboxed”, you’ll learn how to containerize applications, manage images and volumes, and orchestrate services using Docker CLI and Docker Compose. Whether you’re a developer, DevOps engineer, or system admin, this hands-on course teaches you how to simplify deployment, eliminate “it works on my machine”, and build scalable systems from day one.

    ✅ Key Benefits

    • 📦 Package Once, Run Anywhere — Learn to containerize any app with zero dependency hell
    • 🔁 Faster Development Cycles — Consistent dev environments using Docker images
    • 🌍 Deploy Like a Pro — Use containers in staging, CI/CD, or production
    • 🔗 Connect Services Easily — Work with multi-container apps using Docker Compose
    • 🧰 Foundation for Kubernetes — Build the skills to move into container orchestration

    🎯 Pre-requisites

    • Basic understanding of Linux commands (recommended)
    • Familiarity with software development or DevOps workflows
    • Optional: Prior exposure to Git, APIs, and web apps

    📚 Curriculum Breakdown

    📘 Module 1: Introduction to Docker

    • What is Docker? Containers vs VMs
    • Installing Docker (Desktop or CLI)
    • Basic commands: docker version, docker info

    📦 Module 2: Working with Docker Images

    • Understanding Docker images vs containers
    • Pulling and running images (docker run, docker pull)
    • Exploring Docker Hub

    🏗️ Module 3: Creating Your Own Containers

    • Writing a Dockerfile
    • Building images using docker build
    • Managing images: tag, remove, reuse

    🔄 Module 4: Volumes and Persistence

    • Why containers are stateless by default
    • Mounting volumes to persist data
    • Bind mounts vs managed volumes

    🌐 Module 5: Networking in Docker

    • Bridge, host, and overlay networks
    • Exposing ports (-p)
    • Linking multiple containers

    🧱 Module 6: Docker Compose

    • Writing a docker-compose.yml file
    • Defining multi-container applications (e.g., app + DB)
    • Compose commands: up, down, logs, exec

    🛡️ Module 7: Best Practices & Cleanup

    • Layer caching and image size optimization
    • Deleting unused images/containers (prune)
    • Environment variables and secrets

    🛠️ Module 8: Final Project

    • Build a Multi-Container App (Node.js + MongoDB or Python + PostgreSQL)
      • Containerized backend
      • Persistent DB
      • Shared network
      • Docker Compose orchestration

    ⏱️ Duration (Based on Daily Study Time)

    Daily Study TimeDurationNotes
    2 hours/day10–12 days (~2 weeks)Includes hands-on labs
    4 hours/day5–6 days (1 week)Great for project-based sprint
    6 hours/day3–4 days (bootcamp)Fast-paced with deep practice

    🎓 Outcome

    By the end of Docker Unboxed, you’ll:

    • Build, run, and manage containers using Docker CLI and Docker Compose
    • Understand how containers simplify testing, deployment, and scaling
    • Be ready to integrate Docker in CI/CD, cloud environments, or move to Kubernetes