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Get ready to put all your gen AI engineering skills into practice! This guided project will test and apply the knowledge and understanding you've gained throughout the previous courses in the program. You will build your own real-world gen AI application. During this course, you will fill the final gaps in your knowledge to extend your understanding of document loaders from LangChain. You will then apply your new skills to uploading your own documents from various sources. Next, you will look at text-splitting strategies and use them to enhance model responsiveness. Then, you will use watsonx to embed documents, a vector database to store document embeddings, and LangChain to develop a retriever to fetch documents. As you work through your project, you will also implement RAG to improve retrieval, create a QA bot, and set up a simple Gradio interface to interact with your models. By the end of the course, you will have a hands-on project that provides engaging evidence of your generative AI engineering skills that you can talk about in interviews. If you're ready to add some real-world experience to your portfolio, enroll today and fuel your AI engineering career.

Embark on a journey through the intricate world of deep learning and neural networks. This course starts with a foundation in the history and basic concepts of neural networks, including perceptrons and multi-layer structures. As you progress, you'll explore the mechanics of training neural networks, covering activation functions and the backpropagation algorithm. The course then advances to artificial neural networks and their real-world applications, drawing inspiration from the human brain's architecture. You'll gain practical insights into input and output layers, the Sigmoid function, and key datasets like MNIST. Specialized topics such as feed-forward networks, backpropagation, and regularization techniques, including dropout strategies and batch normalization, are thoroughly covered. You'll also be introduced to powerful frameworks like TensorFlow and Keras. The course concludes with an in-depth study of convolutional neural networks (CNNs), focusing on their applications and principles for image and video analysis. This course is ideal for tech professionals and students with a basic understanding of programming and mathematics, particularly linear algebra, calculus, and basic probability.

Embark on an exciting journey into game development with this comprehensive course on Unreal Engine 5. You'll begin by learning how to set up the fundamental elements of your game, such as creating the night mood for a spooky atmosphere and designing the game area. Through hands-on lessons, you'll explore key tools and assets available in the Unreal Engine Marketplace, mastering the fundamentals that bring your FPS game to life. Next, you'll delve into character creation and animations, where you'll build a fully functioning FPS protagonist. From basic movements to advanced controller input settings, this section will teach you how to implement smooth character controls, including the addition of dynamic animations like jumping and aiming. You'll also dive into the weapon system, learning how to attach weapons, animate firing actions, and add sound effects to bring combat to life. Finally, you'll focus on creating AI-driven zombies. This section covers every detail from adding enemy characters and setting up AI behaviors, to designing complex combat interactions and damage systems. You'll also learn how to enhance gameplay with health pickups, ammo management, and HUD improvements. By the end of this course, you will have built a fully functional zombie shooter, complete with AI enemies and dynamic gameplay mechanics. This course is perfect for aspiring game developers and intermediate Unreal Engine users. A basic understanding of Unreal Engine 5's interface and basic game design concepts is recommended. Prior experience with game development is helpful but not required.

This course takes you from the basics of HTML and CSS to advanced web development techniques, empowering you to build professional and responsive websites. You'll start with the essentials, creating your first website while learning key concepts like HTML structure, headings, paragraphs, links, images, and forms. Alongside this, you'll master CSS to style your web pages, ensuring they look polished and professional. As the course progresses, you will dive into more sophisticated styling techniques using Flexbox, CSS Grid, and Sass. These tools allow you to create flexible layouts, align content effectively, and handle responsive design challenges. You'll also engage with projects, including creating an Amazon checkout page, a photo gallery, and a mathematics tutorial website, all while applying your newfound Flexbox and Grid skills. By the end of the course, you'll be able to structure, style, and host fully responsive websites. With projects designed to challenge and build your skills, you'll gain a strong foundation in modern web design principles. Sass will also be introduced to help streamline your CSS workflow, allowing for more efficient and maintainable code. This course is perfect for beginners and intermediate web developers, hobbyists, and anyone interested in creating websites from scratch. No prior experience is required, but basic computer literacy is recommended.

This course takes you on a journey through the fundamentals and advanced techniques of behavior-driven development (BDD) using Specflow and Cucumber in a .NET environment. You'll begin by understanding the core principles of BDD, why it matters, and how Cucumber, combined with Gherkin, allows you to write easy-to-read test scenarios that everyone on your team can understand. You'll also explore the benefits of using Gherkin as a universal language for expressing business requirements, along with a deep dive into user stories, testing workflows, and maintaining a single source of truth. As you progress, you'll immerse yourself in Gherkin's keyword syntax, learning to write feature files that effectively describe scenarios and outcomes. You will move on to mastering Specflow for .NET, installing necessary extensions in Visual Studio, and creating acceptance tests that align with business goals. Through hands-on exercises, you'll become proficient in writing and organizing tests, binding steps, handling parameters, and efficient hooks for advanced test execution. By the end of this course, you'll have the knowledge and skills to implement BDD efficiently in any .NET project. Designed for both beginners and experienced developers, this course equips you with practical skills in using Specflow and Cucumber, empowering your team to create cleaner, more collaborative code bases while ensuring that everyone, from product owners to developers, can easily contribute to the testing process . This course is designed for .NET developers who want to learn or enhance their knowledge of BDD using Specflow and Cucumber. Prior experience with .NET development is required, and basic knowledge of unit testing will be beneficial.

Embark on a journey that takes you from the fundamental aspects of Raspberry Pi to advanced projects that showcase its versatility and power. Starting with an introduction to the Raspberry Pi platform, this course ensures that you gain a solid understanding of the hardware, software, and configurations necessary to maximize its use. You'll learn to set up your Raspberry Pi, understand SD card intricacies, and get hands-on with software installation and configuration. As you progress, the course dives into remote connectivity options, offering you the flexibility to manage your Raspberry Pi in various modes, including headless setups. You'll also explore the Terminal, mastering commands and shortcuts that enhance productivity. The learning curve continues with detailed sessions on installing web servers and working with GPIO pins, where you'll engage in practical projects like controlling LEDs and integrating sensors. The final segments of the course elevate your Raspberry Pi skills to new heights. From working with cameras and creating time-lapse videos to building a fully functional Amazon Echo clone, you'll experience the full spectrum of what the Raspberry Pi can achieve. By the end, you'll be equipped with the knowledge and confidence to tackle any Raspberry Pi project, no matter how complex. This course is ideal for hobbyists, tech enthusiasts, and anyone with a passion for DIY electronics. A basic understanding of programming and Linux is recommended but not required, as the course is designed to cater to beginners and intermediate users alike.

This training program provides a detailed exploration of Just-In-Time, a critical methodology in Lean Management that ensures processes deliver exactly what's needed, when it's needed. The course starts with fundamental concepts, including its origin in Toyota's production system, and explains how it revolutionized modern manufacturing by minimizing waste and maximizing productivity. As you progress, you'll dive into key components like teamwork, discipline, and supplier involvement, understanding how these factors drive the success of Just-In-Time systems. The course offers practical insights through interactive activities, helping you apply these concepts in real-world settings, whether in large enterprises or small-scale organizations. By the end of the course, you'll have a complete understanding of how to implement Just-In-Time in your organization. From learning to manage uniform workloads to optimizing setups, the lessons provide the tools you need to streamline processes and achieve continuous improvement. Whether you're a seasoned manager or a Lean beginner, this course prepares you for Just-In-Time certification. This course is designed for operations managers, production planners, supply chain professionals, and anyone involved in manufacturing or service operations. No prior knowledge of Lean Management is required, but a basic understanding of business processes is helpful.

In this comprehensive course, you'll dive into the world of real-time object detection with YOLO, one of the most powerful algorithms for detecting objects in images and videos. The course begins with an introduction to YOLO and object detection, followed by setting up your development environment with Anaconda and installing essential libraries like OpenCV. A review of Python basics ensures you are equipped with the necessary programming knowledge before delving into convolutional neural networks (CNNs). Once your environment is ready, the course progresses into more advanced topics such as implementing YOLO for pre-trained object detection. You'll explore practical examples, including detecting objects in images, videos, and live webcam feeds. The course then takes you through custom training with YOLOv4, where you will learn to collect and label data, train-test split, and prepare Darknet for training your own models. Each phase of custom training is covered step by step, including synchronization with Google Colab and Drive, testing Darknet, and fine-tuning the training process. By the end of the course, you'll be adept at training YOLO models for specific use cases, including the detection of various objects and even custom challenges such as COVID-19 detection. Along the way, you'll troubleshoot common issues like GPU usage limits in Colab and explore real-world case studies to solidify your understanding. No prior knowledge of YOLO is required, but a basic understanding of machine learning concepts will be helpful. This course is designed for data scientists, machine learning engineers, and computer vision enthusiasts who are familiar with Python programming.

Immerse yourself in the comprehensive world of Hadoop with this expertly designed course. Starting with the basics, you'll learn to install the Hortonworks Data Platform Sandbox on your local machine, providing you with a powerful environment to explore Hadoop's core functionalities. The course meticulously guides you through essential concepts such as the Hadoop Distributed File System (HDFS) and MapReduce, offering practical exercises to solidify your understanding. As you progress, you'll delve into advanced Hadoop programming with tools like Pig, Hive, and Spark. These modules are designed to give you hands-on experience with real-world datasets, allowing you to build complex queries, analyze large datasets, and even venture into machine learning with Spark's MLLib. The course also covers integrating relational and non-relational databases with Hadoop, ensuring you can handle a wide range of data scenarios in your career. The final sections focus on managing and optimizing your Hadoop cluster, introducing you to tools like YARN, ZooKeeper, Oozie, and Kafka. You'll learn how to feed data into your cluster efficiently, manage resources, and analyze streaming data in real time. By the end of this course, you'll be well-equipped to design and implement Hadoop-based solutions in any data-driven environment. This course is ideal for data engineers, software developers, and IT professionals who have a basic understanding of programming and data management. Familiarity with Java, SQL, and Linux command-line interfaces is recommended but not required.

This course is your comprehensive guide to mastering regression analysis and modeling using STATA. Starting with an introduction to the basics of linear regression, it takes you through essential concepts such as ordinary least squares, best linear unbiased estimators, and the crucial Gauss-Markov assumptions. You will also explore the difference between causality and correlation, learning how to apply these concepts practically in STATA with real-world examples. By the end of the linear regression module, you'll be equipped with a deep understanding of regression analysis fundamentals. Moving beyond linear regression, the course delves into non-linear regression analysis, providing a robust framework for more advanced statistical modeling. You will gain expertise in models such as logit and probit transformations, maximum likelihood estimation, and techniques for managing multiple non-linear regression variables. Practical examples with STATA are woven throughout, ensuring that your learning is as practical as it is theoretical. The course rounds off with regression modeling strategies, including managing multicollinearity, handling missing values, and working with categorical explanatory variables. You'll also explore dynamic relationships using time-based data and understand how to interpret regression outputs effectively. This training is packed with applied STATA demonstrations, allowing you to master both the technical and interpretative aspects of regression modeling. This course is designed for statisticians, data analysts, econometricians, and researchers. A basic understanding of statistics is required, with some familiarity with regression analysis and statistical software being advantageous.

This course introduces you to quantum computing, focusing on IBM's Qiskit framework. You'll start with the basics of quantum mechanics, install and test Qiskit 0.23.0, and then explore qubits, learning how they differ from classical bits. Finally, you'll implement quantum gates like the Pauli X, Y, and Z gates. As you progress, the course offers detailed guidance on setting up your environment with Anaconda and Qiskit, ensuring you can follow along with practical demonstrations. You'll learn how to create and manipulate qubits, explore vector and matrix quantum states, and apply your knowledge through real-world examples on IBM's quantum computers. The hands-on exercises are designed to reinforce your learning, giving you the confidence to build and test quantum circuits on your own. By the end of the course, you'll have a comprehensive understanding of quantum gates, circuits, and algorithms, including the DJ algorithm and quantum key distribution. You will also be introduced to more advanced topics like quantum teleportation and multi-qubit states, providing you with a strong foundation for further exploration in the quantum computing field. This course is ideal for beginners with a basic understanding of Python programming. No prior knowledge of quantum mechanics or quantum computing is required, making it accessible to anyone interested in starting their journey in this cutting-edge field.

In this course, you'll embark on a journey to master data visualization using R, one of the most popular programming languages ​​among data scientists. Starting with the basics, you'll learn how to set up R and RStudio, ensuring your environment is ready for data analysis. You'll then acquire data from the US National Weather Service, focusing on real-world data to make the learning process relevant and engaging. The initial module walks you through inspecting the data to understand its structure and nuances. Next, you will dive into writing R code to read and manipulate data. You'll explore various data types and values ​​within R, building a solid foundation in handling complex datasets. The course then moves on to practical applications, teaching you how to plot data and create scatter plots. You'll learn to apply linear regression models to identify trends within the data, enhancing your analytical skills. Through hands-on lessons, you'll generate multiple graphs efficiently using loops and display them comprehensively for better comparison. In the final module, you'll learn to install and use essential R packages like ggplot2, which significantly simplifies the process of creating advanced visualizations. You'll culminate the course by plotting temperature critical data, highlighting significant trends. By the end of this course, you will have a robust understanding of data visualization in R, equipped with the skills to handle and visualize complex datasets effectively. This course is designed for technical professionals, data enthusiasts, and analysts who are looking to enhance their data visualization skills using R. A basic understanding of programming and data concepts is recommended to fully benefit from this course.