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via Udemy |
Go to Course: https://www.udemy.com/course/microsoft-ai-900/
Certainly! Here's a comprehensive review and recommendation for the Coursera course based on the provided details: --- **Course Review and Recommendation: Unlocking the Fundamentals of Artificial Intelligence on Coursera** If you're looking to embark on your journey into the world of Artificial Intelligence (AI) and cloud computing, this latest syllabus-based course on Coursera is an excellent starting point. Designed for beginners and those planning to sit for AI-related exams, the course offers a well-rounded introduction to fundamental AI concepts without the need for prior coding experience. **Course Content & Structure:** The course covers a broad spectrum of essential topics, including: - **AI Workloads & Considerations:** Understanding different AI applications like prediction, anomaly detection, computer vision, natural language processing (NLP), and conversational AI. - **Responsible AI Principles:** Emphasizing fairness, reliability, safety, privacy, security, inclusiveness, transparency, and accountability in AI solutions. - **Machine Learning Fundamentals on Azure:** Exploring various types of machine learning (regression, classification, clustering), core concepts such as datasets, features, labels, model training, evaluation metrics, and deployment. - **No-Code Machine Learning Tools:** Demonstrating Azure's no-code solutions like Automated Machine Learning and Azure Machine Learning Designer. - **Computer Vision Workloads:** Teaching how to create solutions for image classification, object detection, segmentation, OCR, facial recognition, using Azure’s Computer Vision, Custom Vision, and Face services. - **NLP Workloads:** Covering key NLP tasks such as key phrase extraction, entity recognition, sentiment analysis, language modeling, speech recognition, and translation using Azure tools like Text Analytics and Language Understanding services. - **Conversational AI:** Exploring practical applications like chatbots and voice assistants with tools like QnA Maker and Bot Framework. **Why I Recommend This Course:** - **Comprehensive yet Accessible:** The course is structured to cater to beginners, explaining core concepts clearly and effectively, making complex AI topics understandable. - **Hands-On and Practical:** It focuses on real-world Azure tools and services, providing a practical foundation that can be directly applied in projects or further study. - **No Coding Required:** Ideal for learners who want to understand AI fundamentals without the barrier of coding. It’s perfect for aspiring solutions architects, business analysts, or anyone interested in the AI and Azure ecosystem. - **Future-Ready Skills:** Covering the latest Azure capabilities ensures you are learning cutting-edge tools for cloud-based AI solutions. **Final Verdict:** This course is highly recommended for beginners eager to understand AI principles and explore its applications in the cloud. Whether you plan to pursue certifications, develop AI solutions, or simply deepen your understanding of AI fundamentals, this course provides a solid foundation to build upon. Enroll now to start your journey into AI and Azure, and take your first step towards becoming proficient in cloud-based artificial intelligence solutions! --- Feel free to ask if you'd like a shorter summary or specific insights!
This is the course based on latest syllabus , by attending this course you will be gaining the fundamental knowledge on Artificial Intelligence. Even if you are planning to write the exam later then also you can go through this course it will help you to understand and clear your basic for AI.If you are looking to start your journey into the Azure then this course is for you too. You can start your journey into the cloud with Artificial Intelligence. There is no need to write any code. You need to understand the basics.You will be taught below Skills MeasuredDescribe Artificial Intelligence workloads and considerations (15-20%)Identify features of common AI workloads· identify prediction/forecasting workloads· identify features of anomaly detection workloads· identify computer vision workloads· identify natural language processing or knowledge mining workloads· identify conversational AI workloads Identify guiding principles for responsible AI· describe considerations for fairness in an AI solution· describe considerations for reliability and safety in an AI solution· describe considerations for privacy and security in an AI solution· describe considerations for inclusiveness in an AI solution· describe considerations for transparency in an AI solution· describe considerations for accountability in an AI solutionDescribe fundamental principles of machine learning on Azure (30- 35%)Identify common machine learning types · identify regression machine learning scenarios· identify classification machine learning scenarios· identify clustering machine learning scenarios Describe core machine learning concepts· identify features and labels in a dataset for machine learning· describe how training and validation datasets are used in machine learning· describe how machine learning algorithms are used for model training· select and interpret model evaluation metrics for classification and regression Identify core tasks in creating a machine learning solution· describe common features of data ingestion and preparation· describe common features of feature selection and engineering· describe common features of model training and evaluation· describe common features of model deployment and management Describe capabilities of no-code machine learning with Azure Machine Learning:· automated Machine Learning tool· azure Machine Learning designerDescribe features of computer vision workloads on Azure (15-20%)Identify common types of computer vision solution:· identify features of image classification solutions· identify features of object detection solutions· identify features of semantic segmentation solutions· identify features of optical character recognition solutions· identify features of facial detection, recognition, and analysis solutions Identify Azure tools and services for computer vision tasks· identify capabilities of the Computer Vision service· identify capabilities of the Custom Vision service· identify capabilities of the Face service· identify capabilities of the Form Recognizer service Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%) Identify features of common NLP Workload Scenarios· identify features and uses for key phrase extraction· identify features and uses for entity recognition· identify features and uses for sentiment analysis· identify features and uses for language modeling· identify features and uses for speech recognition and synthesis· identify features and uses for translation Identify Azure tools and services for NLP workloads· identify capabilities of the Text Analytics service· identify capabilities of the Language Understanding Intelligence Service (LUIS)· identify capabilities of the Speech service· identify capabilities of the Text Translator serviceDescribe features of conversational AI workloads on Azure (15-20%)Identify common use cases for conversational AI· identify features and uses for webchat bots· identify features and uses for telephone voice menus· identify features and uses for personal digital assistants Identify Azure services for conversational AI· identify capabilities of the QnA Maker service· identify capabilities of the Bot Framework