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Python is a popular choice for implementing AI and web security, prompting the QA industry to adopt it for test automation in AI projects. This course guides you in using Python and Selenium WebDriver for automation testing. Starting with the installation of Python and Selenium, you'll learn the basics of Python and progress to writing flawless programs. You'll then run tests across different browsers using the Selenium Python package. The course covers running unit tests with the Pytest framework, developing an end-to-end Selenium Python framework, and integrating it with Jenkins CI. You'll also learn to read and write data from Excel within the framework. Additionally, you'll get an overview of Git, understand the importance of GitHub, and learn to manage staging, commit, and resolve merge conflicts. By the end, you'll be proficient in using Python and Selenium for automation testing. Key learning objectives include understanding Python's data types and program flow, automating HTML web tables, handling dynamic dropdowns, advanced user interactions, logging HTML reports, and implementing a data-driven approach by removing hard-coded data from tests. This course is ideal for both newcomers to testing and experienced manual testers looking to learn automation testing with Python and Selenium, requiring no prior experience in Python programming or automation testing. Applied Learning Project The included projects focus on real-world scenarios, such as automating web interactions with dynamic elements and handling cross-browser testing. Learners will apply these skills to develop robust test automation frameworks that address practical challenges in web application testing.

R is a programming language and environment designed for statistical computing, data analysis, and graphical representation, widely used by statisticians, data scientists, researchers, and analysts. This course guides learners through R programming, from foundational concepts to advanced techniques. It covers R fundamentals, data types, variables, structures, custom functions, control structures, and data manipulation. Learners will master data visualization with leading packages, statistical analysis, hypothesis testing, and regression modeling. The course also includes advanced data manipulation, outlier handling, missing data strategies, and text manipulation using regular expressions. Additionally, it covers machine learning with regression, classification, and clustering algorithms, as well as deep learning, neural networks, image classification, and semantic segmentation. The course concludes with the creation of dynamic web apps using Shiny. Designed for aspiring and established data scientists, analysts, programmers, researchers, and professionals, it accommodates various experience levels. Prerequisites include prior programming experience, but the course can accommodate learners with varying levels of data science and R programming familiarity. Applied Learning Project The included projects in "R Ultimate 2023 - R for Data Science and Machine Learning" are designed to provide hands-on experience with real-world data analysis and machine learning tasks. Learners will apply their skills to solve authentic problems, such as creating dynamic web apps with Shiny, building predictive models, and performing advanced data manipulations, enabling them to transform raw data into actionable insights and interactive applications.

Selenium WebDriver 4 with Java - Zero To Hero is a comprehensive course designed to equip learners with essential automation testing skills using Selenium WebDriver. Starting with the basics of Selenium WebDriver and Java installation, the course covers Java concepts and the TestNG framework. It progresses to advanced topics such as reporting with log47, the page object model, page factory, data-driven framework, and remote testing with Selenium Grid 2.0. The course also includes managing builds with Maven, continuous integration with Jenkins, and database and performance testing. Additionally, learners will explore behavior-driven testing using Cucumber and Gherkin. By course completion, participants will be proficient in performing cross-browser testing, implementing continuous integration, verifying data integrity, creating advanced reports, running performance tests, and converting Selenium tests to Cucumber. This course is ideal for both beginners and experienced QA professionals seeking to advance their automation testing skills. No prior automation or coding experience is required as the course covers all topics from scratch. Applied Learning Project Learners will work on real-world projects such as automating web application testing, creating advanced reports, and setting up continuous integration pipelines. By applying these skills, they will solve authentic problems like cross-browser compatibility testing, data integrity verification, and performance testing, ensuring robust and reliable software applications.

This course offers a comprehensive dive into app development using SwiftUI, specifically updated for iOS 17. Crafted by expert Steve DeStefano and based on J.D. Gauchat’s best-selling book, it provides an all-encompassing resource for developers. Starting with Swift fundamentals through interactive Swift playgrounds, you’ll explore advanced topics like SwiftUI's declarative syntax, state management, and the newest iOS 17 features. The curriculum includes Swift programming paradigms such as functions, generics, arrays, dictionaries, and enums, focusing on object-oriented and protocol-oriented programming alongside memory management and access control techniques. You’ll master SwiftUI to create stunning and functional user interfaces, use modifiers for custom layouts, and implement navigation within apps. The course emphasizes practical application, delivering a streamlined learning experience without unnecessary chatter. Ideal for beginners to advanced developers, it provides a solid foundation in Swift and a thorough SwiftUI reference, enhancing expertise in iOS, macOS, watchOS, and tvOS app development. Prior programming knowledge is recommended. Applied Learning Project The included projects in this course are designed to provide hands-on experience with SwiftUI and iOS 17 features, allowing learners to apply their skills by developing real-world applications. These projects will enable learners to tackle authentic problems such as creating dynamic user interfaces, managing data efficiently, and integrating advanced functionalities, preparing them for professional app development scenarios.

In this course, you’ll build an advanced AutoGPT code generation tool using Rust and GPT-4. Starting with a solid foundation in Rust, you'll progress from basic syntax to advanced features like concurrency and memory management. As you progress, you’ll dive deeper into Rust’s capabilities, learning to leverage its robust ecosystem to create modular, efficient, and scalable code. The course meticulously guides you through the integration of GPT-4, where you'll learn to harness this state-of-the-art language model for AI-driven code generation. Through practical exercises and real-world examples, you’ll see how to use Rust to interact with GPT-4, enabling the automation of complex coding tasks. In the final stages, you'll put it all together, crafting the AutoGPT project from scratch. This hands-on project will culminate in a fully functional tool that not only generates code but also tests, refines, and deploys it—ushering in a new era of AI-augmented development. By the end of the course, you’ll be equipped with the skills to innovate and implement AI solutions in your projects, revolutionizing how you approach software development. This course is ideal for experienced software developers, AI enthusiasts, and DevOps professionals looking to integrate AI into their development workflows. Prerequisites include a strong foundation in programming, familiarity with Rust or similar languages, and an interest in AI-driven automation. Applied Learning Project Learners will engage in a hands-on project to develop an AutoGPT code generation tool named Auto-Gippity using Rust and GPT-4. Through this course, they will gain practical skills in advanced Rust programming and AI integration, enabling them to solve real-world problems by automating code generation and enhancing development workflows.

This course takes you through the step-by-step process of creating a sophisticated Realtor Clone using ReactJS 18 and Firebase 9. Starting with an overview, you'll quickly dive into installing ReactJS and Tailwind CSS, setting up project templates, and creating essential components. By the end of the initial modules, you'll have a solid foundation and a functional project structure. Moving forward, you'll explore user authentication using Firebase Auth Version 9. This section will guide you through setting up sign-in, sign-up, and password recovery pages, implementing OAuth, and ensuring secure user access. You'll also learn to create and protect profile pages, adding functionalities like editing and listing management to make your application robust and user-friendly. The final modules focus on enhancing user experience and deploying your application. You'll integrate features like dynamic headers, image sliders, maps, and landlord contact components. The course concludes with a comprehensive guide to deploying your completed Realtor Clone to Vercel, ensuring your project is live and accessible. Each section is designed to build your skills progressively, making complex concepts manageable and practical. This course is designed for intermediate to advanced developers with a basic understanding of JavaScript and ReactJS. Familiarity with Firebase and web development concepts is beneficial but not required. Applied Learning Project Learners will build a full-featured realtor application using ReactJS, Firebase, and Tailwind CSS, gaining practical experience in modern web development technologies. They will apply these skills to create a dynamic, user-authenticated platform for real estate listings, solving real-world problems such as user authentication, data management, and UI design.

Dive into the world of mobile game development with this comprehensive course focused on Unity and C#. Start by learning how to set up Unity 2022 and get familiar with its interface. The course will guide you through the creation of your first game, a 3D Endless Runner, introducing you to the basics of game mechanics and design. As you progress, you’ll delve deeper into C# scripting, covering essential topics such as variables, functions, loops, and conditionals. These scripting skills will be applied to various game projects, each designed to teach you different aspects of game development. From creating a dodging game with animated obstacles to building a hyper-casual line runner, you'll gain hands-on experience with Unity's powerful tools. Beyond just game creation, the course also explores how to implement Unity Ads to monetize your games. Learn to set up, display, and reward video ads, ensuring your games can generate revenue. By the end of the course, you’ll have the skills and confidence to create, publish, and monetize your own mobile games. This course is perfect for aspiring game developers looking to master Unity and C# to create, publish, and monetize mobile games. Applied Learning Project In this course, learners will build eight mobile games using Unity and C#, applying their skills to develop and monetize real-world game projects. By the end, participants will be able to create complete Android games, implement effective game mechanics, and integrate ads for monetization.

Embark on an immersive journey into deep learning, where theoretical concepts meet practical applications. This course begins with a foundational understanding of perceptrons and neural networks, gradually advancing to complex topics such as backpropagation and convolutional neural networks (CNNs). Each module is meticulously designed to provide hands-on experience, allowing you to apply what you've learned to real-world scenarios. Our curriculum emphasizes the practical aspects of deep learning, ensuring you gain valuable skills in building and training neural networks. You'll explore cutting-edge techniques and tools like TensorFlow and Keras, essential for modern AI development. From working with image data to implementing transfer learning, the course covers a broad spectrum of applications, including medical image analysis and natural image classification. By the end of this course, you'll have a robust portfolio of projects showcasing your expertise in deep learning. You'll be equipped to tackle complex problems, optimize neural networks, and deploy models in real-world environments. Whether you're looking to advance your career in AI or start your journey in data science, this course provides the comprehensive knowledge and practical experience you need. Ideal for data scientists, and ML engineers, with a basic understanding of Python programming and mathematics, including linear algebra and calculus. Familiarity with ML algorithms is recommended. Applied Learning Project The included projects are designed to solve authentic problems by applying deep learning techniques to real-world datasets. Learners will engage with practical applications such as analyzing natural images, diagnosing medical conditions using X-ray images, and implementing advanced recurrent neural network models for tasks like text generation and part-of-speech tagging. These projects ensure that learners not only understand theoretical concepts but also gain hands-on experience, enabling them to apply their deep learning skills effectively in real-life scenarios.

Embark on a transformative learning experience with our PyTorch Ultimate 2024 course. Begin with a solid foundation, understanding the key topics and objectives, and seamlessly transition through machine learning essentials and deep learning principles. From setting up your environment to mastering tensors and neural networks, each section is meticulously crafted to build your expertise. Advanced modules dive into PyTorch modeling, CNNs, RNNs, GANs, and more, ensuring you stay ahead in the rapidly evolving field of AI. With practical coding exercises and real-world applications, this course is your gateway to becoming a PyTorch expert. Our course is designed for tech professionals, data scientists, and AI enthusiasts eager to deepen their understanding of PyTorch. Prior experience in Python and basic machine learning concepts is recommended. By the end of the course, you'll be equipped with the skills to tackle complex AI projects and leverage PyTorch for innovative solutions. Applied Learning Project The included projects offer practical, hands-on experience where learners apply their skills to real-world problems. These projects range from constructing neural networks from scratch to developing complex models for image, audio, and object detection tasks. By engaging with these projects, learners will solve authentic problems, enhancing their ability to deploy robust deep-learning solutions in various practical scenarios.

Embark on a journey to master AJAX and revolutionize your web development skills. Starting with the basics, this course provides a solid foundation for understanding how AJAX works and its significance in creating asynchronous web applications. Through detailed explanations and practical examples, you'll learn to differentiate between traditional synchronous requests and modern AJAX requests, gaining proficiency in both the XMLHttpRequest object and the Fetch API. The course is structured to ensure a gradual learning curve, guiding you from simple AJAX requests to more complex implementations. You'll engage in hands-on mini-projects, where you'll build and refine applications using AJAX to fetch and manipulate data from both internal servers and third-party APIs. As you progress, you'll delve into advanced topics such as handling CORS issues, integrating basic authentication, and working with server-side code using PHP and Node.js. By the end of the course, you'll have a comprehensive understanding of AJAX and its applications, equipped with skills to build responsive, interactive web applications that offer seamless user experiences. This course is designed for web developers and programmers with a basic understanding of HTML, CSS, and JavaScript. No prior knowledge of AJAX is required, but familiarity with web development concepts will be beneficial. Applied Learning Project Learners will engage in a journey to master AJAX and elevate their web development skills. This course starts with the basics, providing a solid foundation for understanding AJAX and its role in creating asynchronous web applications. You'll learn to distinguish between traditional synchronous and modern AJAX requests, mastering both XMLHttpRequest and Fetch API. Through hands-on mini-projects, you'll build and refine applications, fetching and manipulating data from internal servers and third-party APIs. Advanced topics include handling CORS, integrating basic authentication, and working with PHP and Node.js. By the end, you'll have a comprehensive understanding of AJAX and the skills to create responsive, interactive web applications.

With the exponential growth of user-generated data, mastering RNNs is essential for deep learning engineers to perform tasks like classification and prediction. Architectures such as RNNs, GRUs, and LSTMs are top choices, making mastering RNNs a priority. This course starts with the basics and gradually builds your theoretical and practical skills to build, train, and implement RNNs. You will engage in several exercises on topics like gradient descents in RNNs, GRUs, and LSTMs, and learn to implement RNNs using TensorFlow. The course concludes with two exciting and realistic projects: creating an automatic book writer and a stock price prediction application. By the end, you will be equipped to confidently use and implement RNNs in your projects. No prior RNN knowledge is required; Python experience is helpful. This course is ideal for beginners, seasoned data scientists looking to start with RNNs, business analysts, and those wanting to implement RNNs in projects. Through engaging exercises, carefully designed modules, and realistic RNN implementations, you will master RNNs, gain an overview of deep neural networks, understand RNN architectures, and perform text classification using TensorFlow. Applied Learning Project Learners will work on projects like creating an automatic book writer and a stock price prediction application, applying their RNN, LSTM, and TensorFlow skills to solve real-world problems and build practical, impactful solutions. Through these projects, they will gain hands-on experience in data preparation, model training, and evaluation, equipping them with the confidence to implement RNNs in diverse applications.

Embark on a transformative journey into Oracle Primavera P6 PPM Professional with this comprehensive course. Begin with an introduction to the software, building a solid foundation in project management principles tailored to Primavera P6. Progress through creating detailed project plans, mastering different planning levels, and navigating the software's powerful features. Explore Primavera P6’s full capabilities, from setting up and customizing projects to defining calendars and establishing a robust Work Breakdown Structure (WBS). Learn essential processes like adding activities, formatting displays, and establishing task relationships, ensuring you can manage complex project environments. Engage in practical workshops to reinforce your knowledge and apply it in real-world scenarios. In the advanced stages, master resource optimization, earned value management, and global change functions. By the end of the course, you'll have comprehensive expertise in managing and controlling large-scale projects, fully equipping you to tackle modern project management challenges with confidence and efficiency. This course is designed for project managers, schedulers, and planners who are either new to Oracle Primavera P6 or looking to deepen their knowledge of the software. A basic understanding of project management principles is recommended but not required. Applied Learning Project The included projects are real-world scenarios that mirror complex project management challenges commonly faced in industries such as construction, engineering, and IT. Learners will apply their skills to create, manage, and optimize detailed project plans in Primavera P6, addressing authentic problems like resource allocation, schedule management, and earned value analysis, ensuring they are well-equipped to handle large-scale projects efficiently.