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What If You Could Track Any Vehicle in Real Time — Using a Device You Built and Programmed Yourself?Live location updates. Google Maps integration. A custom IoT dashboard. All running on hardware that fits in your palm and costs a fraction of commercial tracking systems.That is exactly what you will build in this course — from the very first connection to a fully deployed real-time vehicle tracking system powered by ESP32.This Is a Build-It-Yourself Course. Not a Theory Lecture.Every section moves you forward with hands-on implementation. You will wire real components, write real code, and watch your vehicle's location update live on a map — using a system you designed yourself.No prior experience needed. If you can use a computer, you can build this.What You Will Build:A complete real-time vehicle tracking system that:Reads live GPS coordinates from a GPS moduleDisplays location data on a connected OLED screenSends real-time data to an IoT dashboardVisualizes live vehicle movement on Google MapsRuns entirely on ESP32 — no external computer requiredWhat You Will Learn — Section by Section:Introduction to ESP32 Understand the most powerful and versatile microcontroller in the IoT space. Learn its specifications, GPIO pin structure, power requirements, and exactly why it is the right choice for real-time tracking applications.ESP32 Setup and Testing Install the IDE, configure drivers, and run your first program on the ESP32 board. You will verify your hardware is working perfectly before writing a single line of project code.OLED Display Programming Install the required drivers, wire the OLED display to ESP32, and program it to show custom real-time data. Understanding each component independently ensures seamless integration later.

LLM Pentesting: Mastering Security Testing for AI ModelsCourse Description:Dive into the rapidly evolving field of Large Language Model (LLM) security with this comprehensive course designed for both beginners and seasoned security professionals. LLM Pentesting: Mastering Security Testing for AI Models will equip you with the skills to identify, exploit, and defend against vulnerabilities specific to AI-driven systems.What You’ll Learn:Foundations of LLMs: Understand what LLMs are, their unique architecture, and how they process data to make intelligent predictions.LLM Security Challenges: Explore the core aspects of data, model, and infrastructure security, alongside ethical considerations critical to safe LLM deployment.Hands-On LLM Hacking Techniques: Delve into practical demonstrations based on the LLM OWASP Top 10, covering prompt injection attacks, API vulnerabilities, excessive agency exploitation, and output handling.Defensive Strategies: Learn defensive techniques, including input sanitization, implementing model guardrails, filtering, and adversarial training to future-proof AI models.Course Structure:This course is designed for self-paced learning with 2+ hours of high-quality video content (and more to come). It’s divided into 4 key sections:Section 1: Introduction - Course overview and key objectives.Section 2: All About LLMs - Fundamentals of LLMs, data and model security, and ethical considerations.Section 3: LLM Hacking - Hands-on hacking tactics and a unique LLM hacking game for applied learning.Section 4: Defensive Strategies for LLMs - Proven defense techniques to mitigate vulnerabilities and secure AI systems.Whether you’re looking to build new skills or advance your career in AI security, this course will guide you through mastering the security testing techniques required for modern AI applications.<

Anyone interested in Deep LearningStudents who have at least high school knowledge in math and who want to start learning Deep LearningAny intermediate level people who know the basics of Machine Learning or Deep Learning, including the classical algorithms like linear regression or logistic regression and more advanced topics like Artificial Neural Networks, but who want to learn more about it and explore all the different fields of Deep LearningAnyone who is not that comfortable with coding but who is interested in Deep Learning and wants to apply it easily on datasetsAny students in college who want to start a career in Data ScienceAny data analysts who want to level up in Deep LearningAny people who are not satisfied with their job and who want to become a Data ScientistAny people who want to create added value to their business by using powerful Deep Learning toolsAny business owners who want to understand how to leverage the Exponential technology of Deep Learning in their businessAny Entrepreneur who wants to create disruption in an industry using the most cutting edge Deep Learning algorithmsDeep learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised.Deep-learning architectures such as deep neural networks, deep belief networks, deep reinforcement learning, recurrent neural networks, convolutional neural networks and Transformers have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, climate science, material inspection and board game programs, where they have produced results comparable to

Build, Deploy Scale a Production-Ready Microservices-Based E-Commerce AppMicroservices are the key to scalable, maintainable, and high-performance applications. But despite their popularity, developers often struggle with the toughest challenges—messy event-driven architectures, data consistency, and deployment headaches. That’s where this course comes in!~ What You'll LearnIn this course, you won’t just scratch the surface—you’ll tackle every major challenge that comes with building microservices head-on. From server-side rendering with React to async event-driven communication, you’ll work through real-world obstacles and discover solutions that scale.Build a production-ready microservices architecture for an e-commerce appCreate a custom event bus for communication between servicesMaster server-side rendering with React Next.jsDeploy your services with Docker KubernetesStore data in MongoDB Redis, optimized for high performanceSecure APIs with JWT-based authenticationImplement best practices for scalable designUnderstand the pros and cons of different microservices patterns~ Why This Course?Most online resources teach the easiest microservices concepts. This course does the opposite—it focuses on the hardest challenges you'll face every day while building scalable applications. You’ll see these difficulties firsthand, then solve them with proven strategies.No prior experience with microservices? No problem!All you need is basic JavaScript Express knowledge. Every concept is covered in treme

This course builds strong ML foundations by combining clear intuition, solid math, and hands-on implementation.You won’t just use ML libraries — you’ll understand how models work internally, why they work, and when they fail.After completing this course, you will:Think beyond black-box MLConfidently explain ML concepts in interviewsBuild and debug models on your ownChoose the right model for the right problemIn short: from following tutorials → to real ML understanding.This course is ideal for :Students freshers aiming for ML/Data rolesSoftware professionals transitioning into MLAnyone who knows “some ML” but lacks confidenceThis course helps you upgrade your career by building real ML depth, not just surface knowledge.What is covered?Math foundations for ML (basic → advanced)Core models: Linear Logistic Regression, Decision Trees, Neural NetworksEnsemble methods: Bagging, Boosting, Random ForestOptimizers, regularization, overfitting bias-variance tradeoffHands-On LearningMovie rating classification (Kaggle + GPUs)Neural Network implementation from scratchMusic genre classification using MFCC + Neural NetworksInterview preparation session for all covered topicsIn one line:A practical, concept-driven ML course that turns learners into confident ML engineersDetailed Course Breakdown: Section 1 : Overview - Introduction to the Instructor Course- Why knowledge of basic maths is crucial for intuition in AI ML- Things we will be learning during the course

Mobile application testing presents a unique set of challenges, from device fragmentation and OS diversity to fluctuating network conditions. This practice-based course is designed to help you navigate these complexities and prepare for the ISTQB Mobile Application Testing (CT-MAT) certification by reinforcing your knowledge through practical, scenario-driven questions.What sets this course apart is the depth of the feedback provided. We believe that true learning happens when you understand the "why" behind every answer. Every question in this course is accompanied by a detailed explanation for each option. This ensures that you don't just find the correct path, but also understand why the other options were incorrect, allowing you to master the underlying mobile testing principles.Comprehensive Domain CoverageOur practice sets are structured to provide exhaustive coverage of the CT-MAT syllabus, focusing on these critical areas:Mobile Fundamentals: Understanding the mobile ecosystem, device types, and specific user expectations.Planning and Design: Strategies for creating test plans that account for mobile-specific risks and constraints.Specialized Test Types: Deep diving into functional testing as well as non-functional aspects like performance, security, and usability for apps.Environments and Tools: Learning how to manage emulators, simulators, and real device clouds effectively.Automation Strategies: Understanding the approach to mobile-specific automation and the tools that support it.Lifecycle and Processes: How mobile testing integrates into various development models and the release management process.Flexible Learning StructureThe course is organized into three distinct formats to help you build your stamina and knowledge at a p