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Passing the HCIA-AI V3.0 certification will indicate that you: 1) Have mastered the AI development history, the Huawei Ascend AI system, the full-stack all-scenario AI strategy， and algorithms related to traditional machine learning and deep learning. 2) Are able to build, train, and deploy neural networks by using development frameworks TensorFlow and MindSpore. 3) Competent in sales, marketing, product manager, project management, technical support, and other AI positions. Learned Knowledge: The cutting-edge AI technologies and development trends, the full-stack all-scenario AI strategy， and the algorithms and development practices related to traditional machine learning, deep learning, and development frameworks. Knowledge Content 1. AI Overview 2. Machine Learning Overview 3. Deep Learning Overview 4. Mainstream Development Frameworks for AI 5. Huawei AI Development Framework MindSpore 6. Huawei AI Computing Platform Atlas 7. Huawei Open AI Platform for Smart Devices 8. HUAWEI CLOUD Enterprise Intelligence Application Platform 9. Comprehensive AI Experiment HCIA-AI - Exam Code: H13-311 - Exam Type: Written examination - Exam Format: Single-answer Question, Multiple-answer Question, True or false, Short Response Item, Drag and Drop Item Time: 90min - Passing Score/Total Score: 600/1000 - Language: Chinese,English, Spanish Our team prepared these tests course for all those candidates who are planning of taking the HCIA-AI exam in near future. This is an Unofficial course and this course is not affiliated, licensed, or trademarked with Huawei Technologies Co. Ltd in any way.

"AI-900" is a certification exam for Microsoft Azure. Its official name is "Microsoft Azure AI Fundamentals" and it tests basic knowledge of machine learning and artificial intelligence, as well as their implementation on Azure. AI-900 (Microsoft Azure AI Fundamentals) is one of the certification exams for Microsoft Azure. It is an official Microsoft exam that tests your skills in basic knowledge of machine learning (ML) and artificial intelligence (AI). You will also need to understand basic Microsoft Azure terminology to pass the certification exam. Microsoft Azure is a very popular cloud computing service along with Amazon's AWS (Amazon Web Services) and Google's GCP (Google Cloud Platform). Passing Azure certification exams will prove your skills in machine learning and artificial intelligence. Prepare for the AI-900: Microsoft Azure AI Fundamentals exam with this comprehensive practice exam. This exam is designed to help you assess your knowledge and readiness for the actual certification exam. This practice exam focuses on AI concepts and services within Microsoft Azure and covers a wide range of topics including machine learning, natural language processing, computer vision, and more. By simulating the format and content of the actual exam, this practice test can help you identify areas you need to improve and gain confidence in your abilities. The AI-900: Microsoft Azure AI Fundamentals practice exam contains a variety of questions that closely resemble the questions you will encounter in the actual certification exam. It includes multiple choice questions, case studies, and interactive items to ensure you are fully prepared for the exam format. In addition, each question is accompanied by a detailed explanation so you can understand the reasoning behind the correct answer and learn from your mistakes. This comprehensive approach will not only help you pass the exam, but also help you gain a deeper understanding of AI concepts and services within Microsoft Azure. Whether you're new to AI or looking to validate your existing knowledge, this practice exam is an essential tool for your preparation. It's suitable for those who understand the basics of cloud computing and Microsoft Azure, as well as those who have experience with AI and want to demonstrate their expertise. With this practice exam, you can approach the AI-900 certification exam with confidence and take the next step in your career. Don't leave your success to chance. Use the AI-900: Microsoft Azure AI Fundamentals practice exam to fully prepare for the real exam.

Elevate your career with our intensive AI-900: Microsoft Azure AI Fundamentals certification practice exams. Designed to propel you towards success, this course includes five in-depth practice exams, each comprising 40 meticulously crafted questions. We offer a remarkable opportunity for learners to immerse themselves in the world of Azure AI and demonstrate their expertise. Our practice exams simulate the real certification experience, providing you with a taste of the challenges that lie ahead. Each exam offers a rigorous assessment of your knowledge, honing your critical thinking and problem-solving skills. With a strict 1-hour time limit and a high 90% minimum pass rate, you'll be prepared to excel in the actual exam and showcase your proficiency in Microsoft Azure AI Fundamentals. Dive into the realm of Azure AI, armed with detailed explanations for every question and valuable reference links to enrich your understanding. Overview: - Five comprehensive practice exams, featuring 40 questions each. - 1-hour duration for each exam, replicating the exam's time constraints. - Realistic exam format and question variety. - 90% minimum pass rate to ensure your confidence and expertise. - Access detailed explanations for every question, aiding your learning journey. - Gain an edge with reference links that deepen your knowledge base. - Identify strengths and areas for improvement through detailed performance insights. - Equip yourself with the skills and mindset to excel in the AI-900 exam. Upon completing this course, learners can expect to: - Develop a deep understanding of key AI concepts in the Microsoft Azure environment. - Gain hands-on experience with a wide range of AI services and tools. - Enhance critical analysis and problem-solving skills required for AI application. - Master the confidence to tackle the AI-900 certification exam. Requirements: - Basic familiarity with AI concepts. - No prior experience with Microsoft Azure is required. - Access to a computer or device to take the practice exams. Who can take this course: - Professionals seeking to validate their knowledge in Microsoft Azure AI Fundamentals. - Aspiring AI enthusiasts looking to dive into the world of cloud-based AI solutions. - Beginners eager to explore AI in the Microsoft Azure ecosystem. - Individuals committed to achieving the AI-900 certification and advancing their careers. Side Note: Our course caters to diverse skill levels, making it a valuable resource for both newcomers and those with foundational knowledge. We aim to empower you with the expertise needed to excel in the AI-900 exam and embark on a rewarding journey into the world of Azure AI.

Become a MASTER of Adobe Express and acquire the skills to craft compelling content for social media and the web. This comprehensive tutorial course guides you step by step through the entire process, from grasping the interface and creating documents from scratch to producing polished professional work. This course is adaptable for both the free and paid versions of Adobe Express. Throughout the program, we'll engage in various hands-on projects that can enhance your portfolio. Adobe provides access to a vast library of stock images, videos, and templates, ensuring a wealth of resources for your creations, supplemented by royalty-free images from other sources. Spanning nearly 2 hours, this well-structured course covers many tools and techniques employed by professional graphic designers. Each Section is designed to reinforce your skills and introduce new ones, allowing you to apply a diverse range of techniques. Working with pre-made templates (both still and video), text styles, animation, AI generative fills, AI generative text styles, text-to-image, sound, voiceovers, effects, color adjustments, and more, you'll gain proficiency in creating and editing a variety of documents. Whether you aspire to monetize your Adobe Express skills, transition from Canva, or pursue this for personal enjoyment and self-improvement, this course and its projects are designed to boost your confidence and equip you with the skills to create stunning documents. Keep in mind also that Adobe Express is the new version of Adobe Express Spark.

If you want to multiply your content output with the help of artificial intelligence, you've come to the right place. Over the last 15 years, I've seen a lot of shifts in the digital media landscape. But artificial intelligence (AI) is quite possibly the greatest change to our industry ever. By integrating AI into our workflow, my team and I have managed to increase our output and efficiency tenfold—without compromising quality. The goal for this course is simple: to give you the tools and strategies you need to create high-quality content with an unprecedented level of efficiency. Here are some of the things we'll cover: - The role of AI in modern content creation - Setting up and using AI tools like ChatGPT - Developing an effective content strategy in the age of AI - Prompt engineering techniques to help you give clear instructions to the AI - Creating a content outline with ChatGPT - Training AI to write in your unique style / brand voice - How to draft full-length content with ChatGPT - Editing and refining AI-generated text - Using AI to generate irresistible headlines, meta descriptions, and social media captions - The AI Content Machine: a process for creating an endless stream of fresh content by repurposing what you've already created If you're ready to increase your output and create content faster than ever before, get yourself enrolled, and I'll see you inside.

Welcome to the comprehensive course on Linear Algebra for Machine Learning. This Machine Learning course is your gateway to mastering the essential tools needed for Machine Learning. Designed specifically for Machine Learning students, Machine Learning Engineers, and those pursuing a Masters in Machine Learning, this course focuses on practical, algorithmic applications within Machine Learning. We go beyond theory to show you how Linear Algebra powers the algorithms and techniques that drive Machine Learning. This isn't for pure Math students; it's for those who want to harness the power of Linear Algebra to solve real-world Machine Learning problems and advance their careers. Join us and gain the skills to excel in the dynamic world of Machine Learning. Linear Algebra for Machine Learning is an essential foundation for understanding the mechanics behind many Machine Learning algorithms. From data preprocessing to model optimization, the principles of Linear Algebra are woven into every aspect of Machine Learning. This course will guide you through these principles with a clear emphasis on their relevance and application to Machine Learning. For Machine Learning students, grasping the concepts of Linear Algebra is crucial for success. The course content includes matrix operations, vector spaces, and eigenvalues, all framed within the context of Machine Learning problems. Understanding Linear Algebra will enable you to comprehend complex Machine Learning models and improve your ability to implement Machine Learning algorithms effectively. Machine Learning Engineers will find this course invaluable for optimizing their Machine Learning models and solving computational problems. Linear Algebra provides the tools needed to handle large datasets efficiently and to perform operations such as dimensionality reduction and data transformation. By mastering Linear Algebra, Machine Learning Engineers can enhance the performance and accuracy of their Machine Learning algorithms. For those pursuing a Masters in Machine Learning, this course offers an in-depth exploration of Linear Algebra. Advanced topics such as dimensionality reduction and Singular Value Decomposition (SVD) will be covered extensively, showcasing their applications in Machine Learning. This knowledge is critical for research and development in multifarious fields, making Linear Algebra a cornerstone of advanced Machine Learning studies. In summary, this course on Linear Algebra for Machine Learning is designed specifically for individuals who aim to apply mathematical concepts to Machine Learning, rather than for pure Math students. By focusing on practical applications and real-world examples, we ensure that you gain the skills necessary to excel in the dynamic field of Machine Learning. Please find the Syllabus for the Course below Linear Algebra for Machine Learning: 1. Introduction to linear Algebra Difference between Algebra and Linear Algebra, Definition of Linear Algebra, Linear Equation and System of linear equations with an Example, Attributes and properties of system of linear equation. Linear Algebra for Machine Learning: 2. Geometric representation of an expression Geometric visualization of an algebraic expression with an example, Gradient of a straight line, Generalization of an expression geometrically on an N dimensional plane. Linear Algebra for Machine Learning: 3. Importance of a System of linear Equation Definition and Goal of System of Linear Equations, General form of system of Linear Equations, representing a dataset in terms of System of linear equations, Applications of system of linear equations in solving a classification and a regression problem with an example of a dataset. Linear Algebra for Machine Learning: 4. Vector representation of a System of linear equations Need for vector representation of a system of linear equations while solving a Machine Learning problem, Properties, and advantages of vector representation of a system of linear equations. Linear Algebra for Machine Learning: 5. Introduction to Vectors for Machine Learning Scalar, 2-D and 3-D data representation of vectors geometrically, generalization of N-D data into N-dimensional plane. Linear Algebra for Machine Learning: 6. Vector: Magnitude and Direction Different types of representation of a Vector, Component form, Row & Column Vector form, Determining the magnitude of a vector, determining direction of a vector using Unit vector. Linear Algebra for Machine Learning: 7. Application of Magnitude of a Vector Distance between vectors in a 2-D plane and its generalization onto N-D plane, Euclidian distance between two vectors. Linear Algebra for Machine Learning: 8. Position and Displacement Vector Representing the position of a Point, line and a plane using position vector geometrically, Introduction to an Online tool to visualize a vector geometrically, Visualization of a displacement vector with an example. Linear Algebra for Machine Learning: 9. Addition, Subtraction and Scaling of a Vector Explanation of Geometric Visualization of Addition, Subtraction and Scaling of two vectors. Linear Algebra for Machine Learning: 10. Dot Product between two vectors Types of Vector Multiplications, Need for Dot product between two vectors, Two forms of Dot product, Determining Similarity and Dissimilarity of two vectors using dot product, Difference between component form and polar form of a dot product, Application of dot product between vectors with an example. Linear Algebra for Machine Learning: 11. Projection of a Vector Explanation of projection of a Vector, Two types of projection of a Vector, Deriving formula of types of projection of Vectors, Difference between Scalar and Vector projection. Linear Algebra for Machine Learning: 12. Application of Projection of a Vector Understanding the need for projection of a Vector while solving a Machine Learning problem with an Example. Linear Algebra for Machine Learning: 13. Vector Spaces and Subspaces Definition of Mathematical Structure, Definition of Vector Space, Mathematical definition of Vector Space, Example of a vector space, Mathematical definition of Subspace along with an example. Linear Algebra for Machine Learning: 14. Feature space and Input feature vector Geometric visualization of a feature space and Input feature vector, Assumptions of vector space, Simple Application of Vector Addition and Multiplication on a feature space, Mean of a Vector, Linear transformation of a Vector. Linear Algebra for Machine Learning: 15. Span of Vectors Mathematical and theoretical definition of Span of Vectors, Geometric intuition of Span of a Vector, Example of Span of a Vector, Geometric intuition and mathematical definition of span of two vectors, dependent and independent vectors, Span of dependent and independent vector. Linear Algebra for Machine Learning: 16. Linear Independence of vectors Mathematical definition of linear Independence of vectors, linear combination of vectors, determining linearly independent vectors. Linear Algebra for Machine Learning: 17. Application of linearly independent vectors Solving a classification and a regression Machine learning problem using linearly independent vectors, property of dimension of a decision boundary. Linear Algebra for Machine Learning: 18. Basis of a Subspace Choosing vectors to form the basis, Definition of basis of a subspace, Dimension of a subspace Linear Algebra for Machine Learning: 19. Gaussian Elimination Basis of a Vector Space, Finding the basis and dimension of Vectors using Gaussian Elimination, Row Echelon form of a Matrix, Rank of a Matrix. Linear Algebra for Machine Learning: 20. Gaussian Elimination Application Solving system of Linear Equations using Gaussian Elimination, Augmented Matrix, Reduced Row Echelon form and its properties. Linear Algebra for Machine Learning: 21. Orthogonal Basis Orthogonal Set, Orthogonal Vectors, Orthogonal Basis and its definition, formula to represent any vector in terms of Basis vectors with an Example. Linear Algebra for Machine Learning: 22. Orthonormal Basis Orthonormal Set, Orthonormal Vectors, Orthonormal Basis, and its definition. Linear Algebra for Machine Learning: 23. Gram-Schmidt Orthogonalization Need for Orthogonalization, Gram-Schmidt Orthogonalization procedure, Determining Orthogonal and Orthonormal Basis using Gram-Schmidt method. Linear Algebra for Machine Learning: 24. Span Visualization Span of a Vector on 2-D space, Span of 2 Vectors on a 2-D space, Span of a vector on a 3-D space, Span of 2 vectors on a 3-D space, Span of 3 Vectors on a 3-D space. Linear Algebra for Machine Learning: 25. Linear Transformation Definition of Linear Transformation, Domain and Codomain, Properties of linear transformation with examples, Matrix Vector multiplication. Linear Algebra for Machine Learning: 26. Kernel and Image Kernel and its Definition, Image and its Definition, Attributes of linear transformation. Linear Algebra for Machine Learning: 27. Application of Linear Transformation AX=b as a function, projecting a vector from higher dimensional space onto a lesser dimensional space using linear transformation. Linear Algebra for Machine Learning: 28. Application of Linear Transformation in ML Methods of linear transformation, Normalization and Standardization of features, Demonstration of Normalization and Standardization using a Python code, Non-linear Transformation. Linear Algebra for Machine Learning: 29. Types of Matrix and Matrix Equations Types of Matrix for solving ML problems, Types of Matrix equations, Homogeneous equation and its properties, Non Homogeneous equation and its properties, Consistent and Inconsistent solution, Example of Non trivial solution AX=0. Linear Algebra for Machine Learning: 30. Determinant and its Application Definition of Determinant, determining the determinant of a matrix, Singular and Non-Singular matrix, Matrix transformation and its properties, five different applications of determinants in ML. Linear Algebra for Machine Learning: 31. Inverse of a Matrix Definition of Inverse of a matrix, Invertible and Non-Invertible matrix with an example. Linear Algebra for Machine Learning: 32. Determinants II Demonstration of five applications of Determinant of a matrix using a Python Code. Linear Algebra for Machine Learning: 33. Inverse of a Matrix II Application of Inverse of a matrix in Machine Learning, Rules for invertibility of matrix, Hurdles to determine the invertibility of a matrix in Machine Learning, Methods to overcome the hurdles. Linear Algebra for Machine Learning: 34. Eigen vector and Eigen value Definition of Eigen vector and Eigen value, Example of Eigen vector, Procedure to calculate Eigen vector and Eigen value, Determining Eigen Vector and Eigen Value using a Python Code. Linear Algebra for Machine Learning: 35. Similar Matrix and Similarity transformation Transformation matrix, Similar matrix, Similarity Transformation, Similarity matrix, Properties of Similar matrix. Linear Algebra for Machine Learning: 36. Diagonalization of a Matrix Derivation of formula for Diagonalization of a Matrix, Geometric intuition of Diagonalization of a Matrix, Definition of Diagonalization of a matrix, Application of diagonalization of a matrix in Machine Learning. Linear Algebra for Machine Learning: 37. Eigen Decomposition Definition and derivation of Eigen decomposition of a matrix, Rules to perform eigen decomposition, Algebraic and geometric multiplicity, Application of Eigen decomposition in Machine Learning. Linear Algebra for Machine Learning: 38. Orthogonal Matrix Definition of Orthogonal matrix, Properties of Orthogonal Matrix, Demonstration of properties of an Orthogonal matrix using a Python code. Linear Algebra for Machine Learning: 39. Symmetric Matrix Definition of Symmetric matrix, Properties of Symmetric matrix. Linear Algebra for Machine Learning: 40. Singular Value Decomposition Definition of Singular value decomposition, Derivation of SVD along with its geometric intuition, Determining the matrices to perform SVD, Properties of SVD, Application of SVD in Machine Learning. Please Note: This course is not designed for Math graduates learning Linear Algebra, instead it is designed for Machine Learning and Artificial Intelligence aspirants, who can quickly grasp the essential fundamentals of linear algebra before diving deep into Machine Learning Concepts. Happy Learning!!!

According to a recent IBM report, cyberattacks have increased by 71%! This alarming statistic highlights the huge demand for cybersecurity professionals. This IBM course will introduce you to the fundamental concepts of cybersecurity, threats, and preventative measures to start your journey in cybersecurity. In this course, you will learn about the evolution of cybersecurity and be introduced to a critical thinking model. You will also learn about actors, malware, ransomware, and social engineering defenses. Additionally, you will learn about internet security threats and security controls. You will learn the fundamentals of identity and access management (IAM), authentication, and access control. You will also look at the physical threats that organizations face and effective security measures. Throughout the course, you will gain practical knowledge through hands-on activities and gain technical experience through insights from industry experts. The final project will allow you to effectively demonstrate your understanding of cybersecurity principles. This course is designed for anyone who wants a basic understanding of cybersecurity and is part of a series designed to help you launch a career as a cybersecurity analyst.

In this course, you will: - Appreciate the challenges of GAN evaluation and compare different generative models - Use the Fréchet Inception Distance (FID) method to evaluate the fidelity and diversity of GANs - Identify sources of bias and how to detect it in GANs - Learn and implement techniques related to modern StyleGANs DeepLearning Specialization The Generative Adversary Networks (GANs) Specialization provides a fascinating introduction to image generation with GANs, leading the way from fundamental concepts to advanced techniques in a simple and straightforward approach. It also covers social implications, including bias in ML and how to detect it, privacy preservation, and more. Build a comprehensive knowledge base and gain hands-on experience with GANs. Train your own model with PyTorch, use it to generate images, and evaluate different advanced GANs. This specialization provides an accessible path for students of all levels who want to enter the field of GANs or apply GANs in their own projects, even without prior exposure to advanced mathematics and machine learning research.

In this first course in the AI ​​Product Management Specialization offered by the Duke University Pratt School of Engineering, you'll gain a fundamental understanding of what machine learning is, how it works, and when and why it's used. To successfully manage an AI team or product and collaborate with data scientists, software engineers, and customers, you'll need to understand the fundamentals of machine learning technology. This course is a non-coding introduction to machine learning, focusing on the model development process, evaluating and interpreting ML models, and the intuition behind common ML and deep learning algorithms. The course culminates in a hands-on project where you'll have the opportunity to train and optimize a machine learning model on a simple, real-world problem. By the end of this course, you should be able to: 1) Explain how machine learning works and the different types of machine learning 2) Describe modeling challenges and strategies for overcoming them 3) Identify the main algorithms used to solve common machine learning problems and examples of their application 4) Explain deep learning and its advantages and challenges compared to other forms of machine learning 5) Apply best practices for evaluating and interpreting machine learning models

COURSE DESCRIPTION This course is an introduction to the most powerful engineering principles you will ever learn - thermodynamics: the science of transferring energy from one place or form to another place or form. We will introduce you to the tools needed to analyze energy systems - from solar panels to motors to insulated coffee mugs. More specifically, we will cover the principles of conservation of mass and energy, first law analysis for controlled mass and controlled volume systems, the properties and behavior of pure substances, and applications to steady state thermodynamic systems. COURSE FORMAT The course consists of lecture videos that average 8-12 minutes in length. The videos include integrated video quiz questions. There are also quizzes at the end of each section that include problems to practice analytical skills that are not part of the lecture videos. There are no exams. GRADING POLICY Each question is worth 1 point. The correct answer is worth +1 point. An incorrect answer is worth 0 points. There is no partial grading. You can attempt each test up to three times within 8 hours, with an unlimited number of total attempts. The number of questions that must be answered correctly to pass the test is displayed at the beginning of each test. In accordance with the Mastery Learning model, students must pass all 8 practice tests with a score of 80% or higher to complete the course. SAMPLE WORK If you follow the suggested timelines, lectures and tests will each take approximately 3 hours per week, for a total of ~6 hours per week. TARGET AUDIENCE Students majoring in engineering or science. FREQUENTLY ASKED QUESTIONS - What are the prerequisites for taking this course? A basic background (high school or junior college level) in chemistry, physics, and calculus will help you succeed in this class. - What will this course prepare me for in the academic world? Thermodynamics is a prerequisite for many subsequent courses such as heat transfer, internal combustion engines, power plants, gas dynamics, etc. - How will this course prepare me for the real world? Energy is one of the major challenges we face as a global society. Energy needs are closely tied to other major issues such as clean water, health, food supplies, and poverty. Understanding how energy systems work is key to understanding how to meet all of these needs worldwide. Because energy needs are only growing, this course also lays the foundation for many rewarding professional careers.

You're almost there! This is the seventh and final course of the Google Advanced Data Analytics Certificate. In this course, you have the opportunity to complete an optional capstone project that includes key concepts from each of the six preceding courses. During this capstone project, you'll use your new skills and knowledge to develop data-driven insights for a specific business problem. Google employees who currently work in the field will guide you through this course by providing hands-on activities that simulate relevant tasks, sharing examples from their day-to-day work, and helping you enhance your data analytics skills to prepare for your career. Learners who complete the seven courses in this program will have the skills needed to apply for data science and advanced data analytics jobs. This certificate assumes prior knowledge of fundamental analytical principles, skills, and tools covered in the Google Data Analytics Certificate. By the end of this course, you will: -Create and/or update your resume -Create and/or update your professional portfolio -Develop a data frame -Compose data visualizations -Use statistics to analyze and interpret data -Build, interpret, and evaluate regression models -Utilize machine learning techniques in Python

In this course, we provide a broad overview of the medical software field. You will learn from Yale professors and a range of industry experts who connect course concepts to their real-world applications. We begin by discussing the regulatory landscape of medical devices, data privacy and cybersecurity regulations, and key enabling technologies such as quality management and risk management systems. We then take a detailed look at the medical software lifecycle, from identifying user needs, mapping them to system requirements, and continuing through the process of developing, coding, testing, and validating the software. Next, we consider issues related to the use of machine learning techniques in medical applications, from both a software engineering and regulatory perspective. Finally, we discuss business and management issues in this field, focusing on the impact of digital health on modern healthcare and the challenges associated with creating new businesses. This course is designed for third- or fourth-year students studying computer science, biomedical engineering, and related fields, as well as aspiring software engineers working in or interested in moving into the medical device industry.