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The complete course on how to pass the interview and get a job as a Software Tester, QA, Manual Tester, QA Analyst, Website Tester & QA Engineer. Want to land a Software Testing job at a great company but you are intimidated by the interview process and the technical questions? Do you find yourself being confused or overwhelmed by the software testing interview? Look no further!! Because this course is your answer to all the problems, hesitations and fears you have about the software interview. Using the strategies, exercises, tips & tricks taught in this course, you will learn how to get job offers from top tech companies. The demand for software testers is increasing at an insane rate right now. There are more and more technology companies that are being started now a days and all of them require software testers. This is the best time to learn the concepts taught in this course and get a great paying job. This course is created based on personal interviews and feedback from managers who hire software testers. Nothing is held back as all the secrets are shared in this course to increase your chances at landing a job.Here is what you will learn in this course:How to answer the most important software testing interview questionsThis course will provide you scripts on how to answer questions and emails from employersHow to write a resume and how to apply for jobsHow to get more interviews Tips and tricks to stand out from other candidatesWe'll teach you all the fundamental concepts and how to apply them to pass the software testing interview.I will consider myself successful if I could be a part of your journey to success. Enroll today and see what this course can do for your career and life. Best of luck!Your Instructor:

Welcome to the third chapter of Miuul’s Ultimate ML Bootcamp—a comprehensive series crafted to elevate your expertise in the realm of machine learning and artificial intelligence. This chapter, Ultimate ML Bootcamp #3: Logistic Regression, expands on the knowledge you’ve accumulated thus far and dives into a pivotal technique used extensively across classification tasks—logistic regression.In this chapter, we explore the nuances of logistic regression, a fundamental method for classification in predictive modeling. We’ll begin by defining logistic regression and discussing its critical role in machine learning, particularly in scenarios where outcomes are categorical. You’ll learn about the logistic function and how it is used to model probabilities that vary between 0 and 1, thus facilitating binary classification tasks.The journey continues as we delve into gradient descent—a powerful optimization algorithm—to refine our logistic regression models. You’ll grasp how to implement gradient descent to minimize the loss function, a key step in improving the accuracy of your model.Further, we’ll cover essential model evaluation metrics specific to classification, such as accuracy, precision, recall, and the F1-score. Tools like the confusion matrix will be explained, providing a clear picture of model performance, alongside discussions on setting the optimal classification threshold.Advancing through the chapter, you’ll encounter the ROC curve and understand its significance in evaluating the trade-offs between true positive rates and false positive rates. The concept of LOG loss will also be introduced as a measure of model accuracy, providing a quantitative basis to assess model performance.Practical application is a core component of this chapter. We will apply logistic regression to a real-life scenario—predicting diabetes onset. This section includes a thorough walk-through from exploratory data analysis (EDA) and data preprocessing, to building the logistic

Computer architecture is the foundation of modern computing, influencing everything from system design to software performance. Whether you are preparing for an academic exam, certification test, or job interview, a solid understanding of computer architecture is essential. This course provides six full-length practice tests, covering around 300 multiple-choice questions (MCQs) to ensure you are fully prepared.Each question is carefully designed to test your knowledge across three difficulty levels:Basic Concepts: Covers fundamental principles such as CPU architecture, memory management, and instruction sets.Intermediate Scenarios: Includes real-world applications and performance optimization techniques.Advanced Topics: Explores pipeline execution, parallel processing, and cache memory optimization.With detailed explanations accompanying each question, you will not only test your knowledge but also strengthen your understanding of processor design, system architecture, and computational efficiency.Why You Should Enroll in This CourseComprehensive Exam PreparationGain access to 300 carefully curated questions covering key topics in computer architecture.Improve your ability to analyse, optimize, and troubleshoot computing systems.Boost Your Confidence with Real-World ScenariosEach question is designed to simulate real-world applications and exam conditions.Detailed explanations help you understand not just the correct answer, but the reasoning behind it.Structured Learning ApproachThe course is div

PyTorch: written in Python, is grabbing the attention of all data science professionals due to its ease of use over other libraries and its use of dynamic computation graphs. PyTorch is a Deep Learning framework that is a boon for researchers and data scientists. It supports Graphic Processing Units and is a platform that provides maximum flexibility and speed. With PyTorch, you can dynamically build neural networks and easily perform advanced Artificial Intelligence tasks.This comprehensive 2-in-1 course takes a practical approach and is filled with real-world examples to help you create your own application using PyTorch! Begin with exploring PyTorch and the impact it has made on Deep Learning. Design and implement powerful neural networks to solve some impressive problems in a step-by-step manner. Build a Convolutional Neural Network (CNN) for image recognition. Also, predict share prices with Recurrent Neural Network and Long Short-Term Memory Network (LSTM). You’ll learn how to detect credit card fraud with autoencoders and much more! By the end of the course, you’ll conquer the world of PyTorch to build useful and effective Deep Learning models with the PyTorch Deep Learning framework with the help of real-world examples!Contents and OverviewThis training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, Deep Learning with PyTorch, covers building useful and effective deep learning models with the PyTorch Deep Learning framework. In this course, you will learn how to accomplish useful tasks using Convolutional Neural Networks to process spatial data such as images and using Recurrent Neural Networks to process sequential data such as texts. You will explore how you can make use of unlabeled data using Auto-Encoders. You will also be training a neural network to learn how to balance a pole all by itself, using Reinforcement Learning. Throughout this journey, you

Are you ready to harness the power of SwiftUI and MapKit to create captivating, location-aware applications? Whether you're a seasoned iOS developer or a newcomer to SwiftUI, our comprehensive course will guide you from novice to expert in no time.In this course, you'll learn how to display maps seamlessly within your SwiftUI apps, establishing a strong location-based foundation. You'll also delve into the world of markers, pins, and custom annotations, turning your maps into interactive canvases for user engagement. Additionally, you'll have the opportunity to customize your maps with various styles, ensuring they align perfectly with your app's aesthetics.Understanding and displaying the user's real-time location is a crucial skill, and we'll show you how to do it effortlessly. Zooming and panning features will allow you to focus on specific locations of interest, enhancing user experience. But that's not all! We'll guide you through the development of a NearMe app, which will display nearby points of interest and intelligently sort them based on distance from the user's location. You'll also explore iOS 17's LookAroundPreview, staying at the forefront of technology integration. And to top it off, you'll learn to add routes and navigation, enabling users to easily find their way to selected destinations using Apple Maps.This course will cover the creation of a "Restroom Finder App." This unique app will utilize a third-party JSON API to pinpoint and display all nearby restrooms in the user's current position. Each restroom will also be capable of displaying all the amenities available, enhancing user convenience and accessibility.By enrolling in this course, you'll gain hands-on experience, master essential skills, and create real-world applications that utilize SwiftUI and MapKit. Whether you aspire to build location-based social networks, travel guides, or specialized apps like the Restroom Finder, this course will eq

Welcome to 'Unlock the Power of AutoML,' a comprehensive and hands-on guide designed to immerse you in the exciting world of Automated Machine Learning (AutoML). In this transformative course, we'll navigate the intricacies of AutoML, empowering you to build practical and impactful machine learning projects that resonate with real-world scenarios .Course Highlights:Hands-On Experience: Dive into a series of practical exercises and projects that bridge theory with application. Develop a deep understanding of AutoML concepts by working on real-world datasets, ensuring you're well-equipped for industry challenges .Practical Guidance: Our course isn't just about theory; it's about practical application. Learn how to leverage AutoML tools efficiently, saving time and resources while achieving robust and accurate results. Gain insights into the art of feature engineering, model selection, and hyperparameter tuning .Real-World Impact: Move beyond the theoretical realm and explore how AutoML is reshaping industries. Build projects that address actual challenges faced by businesses today, from predictive analytics to recommendation systems, with a focus on creating tangible impact .Skill Mastery: Hone your machine learning skills and become proficient in using popular AutoML frameworks. From Google AutoML to Auto-Sklearn, master the tools that are transforming the way machine learning models are developed and deployed .What You'll Learn:Introduction to AutoML: Grasp the fundamentals of Automated Machine Learning, understanding its significance and application in various domains .Hands-On Projects: Immerse yourself in the creation of real-world projects, covering a spectrum of applications such as finance, healthcare, and e-commerce .Optimizing M