Playwright E2E Test Automation with AI for Beginners

Playwright E2E Test Automation with AI for Beginners

Build robust E2E tests with TypeScript, use Playwright MCP for AI test gen, learn POMs, add automation with CI/CD

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This hands-on course is designed for complete beginners who want to master end-to-end testing with Playwright using JavaScript and TypeScript.

Starting from the very first installation and project setup, you’ll learn how to write robust, maintainable tests that automate real-world browser workflows—from logging in and filling forms to navigating multi-page applications.

Along the way, you’ll build confidence with Playwright’s intuitive context/page model, powerful selector strategies, and built-in fixtures and hooks to organize your test suites.

You’ll discover how to:

Interact with pages: Automate clicks, typing, drag-and-drop, dialogs, and frame navigation.

Assert application state: Use Playwright’s TypeScript expect API for visibility, timing, text, and value checks.

Handle networks: Intercept and mock HTTP requests to simulate back-end failures, control test data, and speed up execution.

Scale tests: Run suites in parallel across Chromium, Firefox, and WebKit for broad browser coverage.

Structure frameworks: Implement the Page Object Model, reusable utilities, and custom fixtures for scalable, team-ready code.

Playwright automation: Automate your workflows with Playwright automation tools. You may even use it for web scrapping.

Use AI tools and Playwright MCP to generate and manage test cases.

Add API checks: Send REST requests via APIRequestContext, validate response payloads, and chain API flows alongside UI tests.

Integrate CI/CD: Configure GitHub Actions (or your preferred pipeline) to run tests on every commit, generate HTML reports, and fail builds on regressions.

Use Playwright with TypeScript: Use the most popular programming language for Playwright automation. All the TypeScript code is clean, properly formatted and professionally written.

Whether you’re a manual tester stepping into automation, a developer wanting to catch regressions early, or a QA professional seeking modern JavaScript/TypeScript tools, this course will equip you with everything you need to deliver fast, reliable, and maintainable end-to-end and API tests.

Legal Disclaimer

This course is an independent training program and is not endorsed by, sponsored by, or affiliated with Playwright, Microsoft, or any of their subsidiaries. All product names, logos, and trademarks are the property of their respective owners.

This course contains promotional materials.

Ultimate RAG Bootcamp Using Langchain,LangGraph & Langsmith

Build powerful RAG pipelines: Traditional, Advanced, Multimodal & Agentic AI with LangChain,LangGraph and Langsmith

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Unlock the Power of Retrieval-Augmented Generation (RAG) – From Traditional to Advanced Agentic AI Systems

In today’s AI-driven world, Retrieval-Augmented Generation (RAG) is one of the most impactful and in-demand techniques, powering everything from intelligent chatbots and personal assistants to automated research agents and enterprise AI systems.

The Ultimate RAG Bootcamp is your complete, step-by-step guide to mastering RAG using the latest and most powerful tools — LangChain, LangGraph, and LangSmith. Whether you’re an AI beginner or an experienced developer, this course takes you from the fundamentals of RAG pipelines all the way to advanced Agentic RAG architectures used in production by leading companies.

Why This Course?

Unlike other courses that only touch on basic RAG concepts, this bootcamp goes deeper. You will:

Learn traditional RAG step-by-step.

Master advanced retrieval strategies like hybrid search, vector optimization, and multimodal RAG.

Implement multi-agent, autonomous AI pipelines that can think, plan, and act collaboratively.

Use LangSmith for experiment tracking, debugging, and performance optimization.

Build real-world, deployable AI applications from start to finish.

By the end, you won’t just understand RAG — you’ll be able to design, optimize, and deploy advanced AI systems for real-world scenarios.

What You’ll Learn

1. RAG Foundations

What RAG is and why it matters.

Traditional RAG architecture: data ingestion, parsing, embeddings, and retrieval.

Choosing and using vector databases effectively.

Building retrieval + generation workflows with LangChain.

2. Advanced RAG Techniques

Advanced chunking strategies for precision retrieval.

Hybrid search: combining vector and keyword search.

Multimodal RAG for text, images, and more.

Persistent memory for context retention.

Self-RAG for improving retrieval quality.

Adaptive & Corrective RAG for dynamic and error-resistant pipelines.

3. Agentic RAG Pipelines

Multi-agent architectures with LangGraph.

Designing agents for research, summarization, and decision-making.

Autonomous RAG with minimal human intervention.

Collaborative AI reasoning with specialized agents.

4. LangSmith for RAG Evaluation & Optimization

Tracking and managing RAG experiments.

Debugging retrieval pipelines and fixing bottlenecks.

Running evaluation metrics to boost accuracy.

5. Real-World RAG Projects

Chatbot with domain-specific knowledge.

Multi-agent research assistant for automated reports.

Multimodal AI assistant with text and image retrieval.

Deploying RAG applications to the cloud.

Who This Course Is For

AI developers & machine learning engineers.

Data scientists integrating retrieval systems.

Software developers building intelligent assistants.

Researchers exploring advanced RAG workflows.

Anyone aiming to master RAG from scratch to production-ready deployment.

Tools & Frameworks You’ll Master

LangChain – Build modular RAG pipelines.

LangGraph – Create advanced agent-based workflows with memory.

LangSmith – Track, debug, and evaluate RAG systems.

Vector Databases – FAISS, Pinecone, Weaviate, and more.

Cloud Deployment – Take AI apps from development to production.

Your Learning Journey

Understand RAG fundamentals.

Build real-world retrieval pipelines.

Advance to agentic and autonomous AI systems.

Deploy and monitor in production.

Optimize for continuous improvement.

RAG is more than just an AI trend — it’s the foundation of intelligent, context-aware applications.

By the end of this bootcamp, you’ll have hands-on, production-ready skills to build and deploy cutting-edge RAG pipelines with LangChain, LangGraph, and LangSmith.

Join the Ultimate RAG Bootcamp today — and start building AI systems that truly understand, reason, and deliver results.

Machine Learning A-Z [2026]: ML, DL, AI with AWS, Python & R

Machine Learning A-Z [2026]: ML, DL, AI with AWS, Python & R

Learn to build, train and deploy ML, DL and AI models in AWS, Python and R from two AI experts. Code templates included.

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Interested in the field of Machine Learning? Then this course is for you!

This course has been designed by two AI & Machine Learning experts so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way.

We will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.

This course can be completed by doing either the AWS tutorials, Python tutorials, or R tutorials, or the three of them - AWS, Python & R. Pick the ones you need for your career.

This course is fun and exciting, and at the same time, we dive deep into Machine Learning. It is structured the following way:

Part 1 - Data Preprocessing: Importing the dataset with pandas, Matrix of Features and Target Vector, Training & Test Sets, Imputing Missing Data, Encoding Categorical Variables, Feature Scaling

Part 2 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression

Part 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification

Part 4 - Clustering: K-Means, Hierarchical Clustering

Part 5 - Association Rule Learning: Apriori, Eclat

Part 6 - Reinforcement Learning: Upper Confidence Bound, Thompson Sampling

Part 7 - Natural Language Processing: Bag-of-words model and algorithms for NLP

Part 8 - Deep Learning: Artificial Neural Networks, Convolutional Neural Networks

Part 9 - Dimensionality Reduction: PCA, LDA, Kernel PCA

Part 10 - Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost

Part 11 - ML Data Preprocessing with AWS: Data types (Apache Parquet, JSON, CSV), Data Preparation with S3, ETL with AWS Glue, Data Wrangling with AWS Glue DataBrew & SageMaker Data Wrangler, Feature Engineering with SageMaker

Part 12 - ML Model Development with AWS: XGBoost, LightGBM, CatBoost, Ensemble Models, Hyperparameter Tuning Techniques, Building Ensemble Models for Regression & Classification with Amazon SageMaker AI, Natural Language Processing with Amazon Comprehend, Computer Vision with Amazon Rekognition, Text to Speech with Amazon Polly, Speech To Text with Amazon Transcribe, Text Extraction with Amazon Textract, Machine Translation with Amazon Translate

Part 13 - ML Model Deployment with AWS: Methods for Deploying Models in Production, Deployment in Amazon SageMaker AI, Serverless vs. Real-Time vs. Asynchronous Inference, Deployment Endpoints in Amazon SageMaker, SageMaker vs. ECS vs. EKS vs. Lambda Deployment Targets, CloudFormation & Cloud Development Kit (CDK), Elastic Container Registry (ECR), Elastic Container Service (ECS) & Fargate, Building Containers with Amazon ECR, ECS & EKS

Part 14 - ML Workflow Automation (CI/CD Pipelines) with AWS: AWS CodePipeline, AWS CodeBuild, AWS CodeCommit, AWS CodeDeploy, Creating an ML pipeline with Amazon SageMaker Pipelines

Part 15 - ML Solution Monitoring and Maintenance with AWS: Features of Responsible AI, Legal Risks of Generative AI, Tools for Responsible ML, Model/Data Quality and Bias Drift with SageMaker Clarify, Monitoring Models in Production with SageMaker Model Monitor, SageMaker Model Cards, SageMaker Inference Recommender, SageMaker Savings Plans

Each section inside each part is independent. So you can either take the whole course from start to finish or you can jump right into any specific section and learn what you need for your career right now.

Moreover, the course is packed with practical exercises that are based on real-life case studies. So not only will you learn the theory, but you will also get lots of hands-on practice building your own models.

And last but not least, this course includes both Python and R code templates which you can download and use on your own projects.

The AI Engineer Course 2026: Complete AI Engineer Bootcamp

The AI Engineer Course 2026: Complete AI Engineer Bootcamp

Complete AI Engineer Training: Python, NLP, Transformers, LLMs, LangChain, Hugging Face, APIs

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AI Engineers are best suited to thrive in the age of AI. It helps businesses utilize Generative AI by building AI-driven applications on top of their existing websites, apps, and databases. Therefore, it’s no surprise that the demand for AI Engineers has been surging in the job marketplace.

Supply, however, has been minimal, and acquiring the skills necessary to be hired as an AI Engineer can be challenging.

So, how is this achievable?

Universities have been slow to create specialized programs focused on practical AI Engineering skills. The few attempts that exist tend to be costly and time-consuming.

Most online courses offer ChatGPT hacks and isolated technical skills, yet integrating these skills remains challenging.

The Solution

AI Engineering is a multidisciplinary field covering:

AI principles and practical applications

Python programming

Natural Language Processing in Python

Large Language Models and Transformers

Developing apps with orchestration tools like LangChain

Vector databases using PineCone

Creating AI-driven applications

Each topic builds on the previous one, and skipping steps can lead to confusion. For instance, applying large language models requires familiarity with Langchain—just as studying natural language processing can be overwhelming without basic Python coding skills.

So, we created the AI Engineer Bootcamp 2025 to provide the most effective, time-efficient, and structured AI engineering training available online.

This pioneering training program overcomes the most significant barrier to entering the AI Engineering field by consolidating all essential resources in one place.

Our course is designed to teach interconnected topics seamlessly—providing all you need to become an AI Engineer at a significantly lower cost and time investment than traditional programs.

The Skills

1. Intro to Artificial Intelligence

Structured and unstructured data, supervised and unsupervised machine learning, Generative AI, and foundational models—these are familiar AI buzzwords; what exactly do they mean?

Why study AI? Gain deep insights into the field through a guided exploration that covers AI fundamentals, the significance of quality data, essential techniques, Generative AI, and the development of advanced models like GPT, Llama, Gemini, and Claude.

2. Python Programming

Mastering Python programming is essential to becoming a skilled AI developer—no-code tools are insufficient.

Python is a modern, general-purpose programming language suited for creating web applications, computer games, and data science tasks. Its extensive library ecosystem makes it ideal for developing AI models.

Why study Python programming?

Python programming will become your essential tool for communicating with AI models and integrating their capabilities into your products.

3. Intro to NLP in Python

Explore Natural Language Processing (NLP) and learn techniques that empower computers to comprehend, generate, and categorize human language.

Why study NLP?

NLP forms the basis of cutting-edge Generative AI models. This program equips you with essential skills to develop AI systems that meaningfully interact with human language.

4. Introduction to Large Language Models

This program section enhances your natural language processing skills by teaching you to utilize the powerful capabilities of Large Language Models (LLMs). Learn critical tools like Transformers Architecture, GPT, Langchain, HuggingFace, BERT, and XLNet.

Why study LLMs?

This module is your gateway to understanding how large language models work and how they can be applied to solve complex language-related tasks that require deep contextual understanding.

5. Building Applications with LangChain

LangChain is a framework that allows for seamless development of AI-driven applications by chaining interoperable components.

Why study LangChain?

Learn how to create applications that can reason. LangChain facilitates the creation of systems where individual pieces—such as language models, databases, and reasoning algorithms—can be interconnected to enhance overall functionality.

6. Vector Databases

With emerging AI technologies, the importance of vectorization and vector databases is set to increase significantly. In this Vector Databases with Pinecone module, you’ll have the opportunity to explore the Pinecone database—a leading vector database solution.

Why study vector databases?

Learning about vector databases is crucial because it equips you to efficiently manage and query large volumes of high-dimensional data—typical in machine learning and AI applications. These technical skills allow you to deploy performance-optimized AI-driven applications.

7. Speech Recognition with Python

Dive into the fascinating field of Speech Recognition and discover how AI systems transform spoken language into actionable insights. This module covers foundational concepts such as audio processing, acoustic modeling, and advanced techniques for building speech-to-text applications using Python.

Why study speech recognition?

Speech Recognition is at the core of voice assistants, automated transcription tools, and voice-driven interfaces. Mastering this skill enables you to create applications that interact with users naturally and unlock the full potential of audio data in AI solutions.

What You Get

$1,250 AI Engineering training program

Active Q&A support

Essential skills for AI engineering employment

AI learner community access

Completion certificate

Future updates

Real-world business case solutions for job readiness

We're excited to help you become an AI Engineer from scratch—offering an unconditional 30-day full money-back guarantee.

With excellent course content and no risk involved, we're confident you'll love it.

Why delay? Each day is a lost opportunity. Click the ‘Buy Now’ button and join our AI Engineer program today.

Ultimate AWS Certified Generative AI Developer Professional

Ultimate AWS Certified Generative AI Developer Professional

Hands-on GenAI development on AWS with Bedrock, SageMaker, and Flows - includes a complete 75-question practice exam.

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Complete Agentic AI Bootcamp With LangGraph and Langchain

Complete Agentic AI Bootcamp With LangGraph and Langchain

Learn to build real-world AI agents, multi-agent workflows, and autonomous apps with LangGraph and LangChain

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Are you excited about the future of AI where intelligent agents can think, act, and collaborate to solve complex tasks autonomously? Welcome to the Complete Agentic AI Bootcamp with LangGraph and LangChain — your one-stop course to master the art of building agentic AI applications from scratch!

This course is designed to teach you everything you need to know about Agentic AI, LangGraph, and LangChain — two of the most powerful frameworks for building intelligent AI agents and multi-agent systems.

You will start by understanding the fundamentals of Agentic AI — how it differs from traditional AI models, the key components of agents (memory, tools, decision-making), and real-world use cases.

We will then dive deep into LangGraph, a cutting-edge framework that helps you design complex agent workflows using graphs, events, and state transitions. You’ll also learn how to combine LangChain's power with LangGraph to build production-ready agent applications.

Throughout the course, you will build real-world projects step-by-step, including:

Creating single intelligent agents with memory and tool-usage capabilities.

Designing multi-agent collaboration systems with message passing and shared goals.

Implementing autonomous research assistants, task automation bots, and retrieval-augmented generation (RAG) agents.

You will not just learn theory — you will build and deploy multiple end-to-end agentic applications, gaining real-world experience in constructing powerful AI systems.

By the end of this course, you will have the skills and confidence to create your own AI agents and deploy complex agentic applications for various domains like search, research, task planning, customer support, and beyond.

What You Will Learn:

Core concepts behind Agentic AI and how intelligent agents operate.

Hands-on mastery of LangGraph and LangChain for building agent systems.

Building autonomous, event-driven AI workflows with memory, reasoning, and tools.

Deploying and optimizing single-agent and multi-agent applications.

Real-world project experience with RAG agents, auto-research agents, and more.

Why Take This Course?

Hands-on, Project-Based Learning: Build actual AI agent applications, not just toy examples.

Complete and Beginner-Friendly: Designed to take you from beginner to advanced agent builder.

Real-World Skills: Learn techniques that companies are starting to use for next-generation AI products.

Cutting-Edge Technologies: Master the latest innovations in AI agent orchestration with LangGraph and LangChain.

If you are a developer, data scientist, AI/ML engineer, or tech enthusiast looking to future-proof your skills and build cutting-edge AI applications, this is the course for you!

Enroll now and start building the future with intelligent AI agents today!

GitHub Copilot Beginner to Pro - AI for Coding & Development

GitHub Copilot Beginner to Pro - AI for Coding & Development

GitHub Copilot for Agentic Coding. Use GitHub Copilot AI to generate code, unit tests, + more. (GitHub Copilot 2026)

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Supercharge Your Coding Workflow: A Complete Guide to GitHub Copilot

In the world of software development, writing code is only half the battle. The other half involves debugging, refactoring, and managing the sheer complexity of modern applications. What if you had an expert partner sitting right next to you, ready to suggest entire functions, write unit tests, or help you decipher complex code instantly?

Enter GitHub Copilot, the world’s most widely adopted AI developer tool.

If you are ready to transform the way you code and drastically boost your productivity, the Udemy course "GitHub Copilot" provides the ultimate roadmap to mastering this revolutionary technology. Here is why this course is essential for developers of all levels.

Why GitHub Copilot?

Think of GitHub Copilot as your AI Pair Programmer. Powered by OpenAI’s advanced models, it doesn't just auto-complete a single line of code; it understands the context of your entire project. It can suggest entire algorithms, boilerplate code, and even documentation, allowing you to focus on solving complex problems rather than typing syntax.

However, simply installing the extension isn't enough. To truly unlock its power, you need to understand how to interact with it effectively.

What You Will Master in This Course

This course goes beyond the basics, offering a deep dive into the capabilities of Copilot. Whether you are working in Visual Studio Code, JetBrains, or Vim, this guide ensures you get the most out of the tool.

Key takeaways include:

Seamless Setup: A step-by-step guide to installing and configuring GitHub Copilot in your preferred development environment.

Prompt Engineering for Code: Learn how to write better comments and prompts to get high-quality, secure, and accurate code suggestions.

Handling Edge Cases: Discover how to guide the AI when it gets stuck or provides incorrect logic, turning a potential frustration into a learning opportunity.

Real-World Application: See how Copilot assists in writing tests, refactoring legacy code, and learning new programming languages on the fly.

Stay Ahead of the Curve

The software industry is shifting. Developers who leverage AI tools are working faster and delivering higher quality code than those who don't. By mastering GitHub Copilot, you aren't just learning a tool; you are future-proofing your career.

This course is perfect for beginners looking to learn syntax faster and seasoned developers who want to eliminate repetitive tasks.

Ready to Code Smarter, Not Harder?

Stop wrestling with boilerplate and start building. Join thousands of developers who have upgraded their workflow with GitHub Copilot.

You can access the full training today at a special discount. Don't miss this chance to level up your skills.

👉 Click here to enroll in "GitHub Copilot" on Udemy

Use Coupon Code: UDEAFFHP22025

Embrace the future of coding and let your AI partner handle the heavy lifting!