Description

Dive into the dynamic universe of artificial intelligence (AI) with our comprehensive guide on Microsoft Azure's AI offerings. This meticulously crafted course caters to both AI novices and seasoned experts, ensuring a comprehensive grasp on AI's transformative potential and Azure's powerful suite of services.

While our primary focus isn't on creating professional data scientists or developers, we aim to empower you with a robust understanding of widespread AI applications and how to leverage Azure's resources for them.

Who's This Course For?
If you're intrigued by the breakthroughs AI offers and the myriad possibilities with Microsoft Azure, then this course beckons you. While prior Azure knowledge isn't mandatory, a fundamental grasp of digital technology and online navigation is beneficial. Grasping some concepts might require basic mathematical skills, such as interpreting graphs. Moreover, our hands-on modules will engage you with data handling and code execution, making a foundational knowledge of programming advantageous.

Prerequisites:
Certification is not a prerequisite. However, aspiring learners should have:

  • A foundational understanding of computing and the internet.
  • A keen interest in AI techniques and machine learning paradigms.
  • An adventurous spirit, ready for hands-on experimentation.

Embark on this transformative journey with us to harness the true potential of AI.

Course curriculum

    1. Lecture 1: Course Goals Unveiled

    2. Lecture 2: The AI Landscape: An Overview

    3. Lecture 3: Harnessing AI for Predictions and Projections

    4. Lecture 4: Detecting Data Anomalies with AI

    5. Lecture 5: Image Analysis through Machine Learning

    6. Lecture 6: Natural Language Processing Decoded

    7. Lecture 7: Knowledge Extraction via Data

    8. Lecture 8: Crafting Intelligent Chatbots and Conversational AI

    9. Lecture 9: Embracing Responsible AI: An Introduction

    10. Lecture 10: Fairness in AI: Ensuring Equal Representation

    11. Lecture 11: Reliability in AI: Building Robust Systems

    12. Lecture 12: Safety in AI: Protecting Users and Data

    13. Lecture 13: Privacy First: Ensuring Data Confidentiality in AI

    14. Lecture 14: Inclusivity: Creating AI for Everyone

    15. Lecture 15: Transparency and Accountability: Understanding AI Decisions

    1. Lecture 16: Machine Learning Demystified

    2. Lecture 17: Contrasting Rule-Based vs. Machine Learning Techniques

    3. Lecture 18: Types of Machine Learning: A Comparative Analysis

    4. Lecture 19: Feature Engineering: Refining Data for Optimal Outcomes

    5. Lecture 20: Data Management: Effective Training and Validation Split Techniques

    6. Lecture 21: Machine Learning Algorithms: Choosing the Right Fit

    7. Lecture 22: Hands-On: Setting Up an Azure Machine Learning Workspace

    8. Lecture 23: Practical Session: Building a Robust Regression Model

    9. Lecture 24: Efficient Resource Management: How to Delete Unwanted Azure Resources

    10. Lecture 25: Deep Dive: Constructing a Classification Model on Azure

    11. Lecture 26: Exploring Automated Machine Learning on Azure

    12. Lecture 27: Optimal Utilization: Deleting Unused Compute Resources

    1. Lecture 28: Image Mastery: Classification, Object Detection, and Semantic Segmentation

    2. Lecture 29: Optical Character Recognition (OCR): Converting Images to Text

    3. Lecture 30: Facial Dynamics: Detecting and Analyzing Faces with Azure

    4. Lecture 31: An Introduction to Azure's Cognitive Services

    5. Lecture 32: Azure's Computer Vision Services: A Comprehensive Overview

    6. Lecture 33: Hands-On: Tapping into Azure's Computer Vision Capabilities

    7. Lecture 34: Tailored Vision: An Introduction to Azure's Custom Vision Service

    8. Lecture 35: Practical Session: Using Azure's Custom Vision for Real-world Solutions

    9. Lecture 36: Face Service on Azure: Deep Dive into Facial Recognition

    10. Lecture 37: Automating Form Recognition with Azure's Form Recognizer Service

    1. Lecture 38: Natural Language Processing: Foundations and Principles

    2. Lecture 39: Insights from Text: Extracting Key Phrases and Entity Identification

    3. Lecture 40: AI's Linguistics: Exploring Language Models

    4. Lecture 41: Speech Recognition: From Sound Waves to Meaning

    5. Lecture 42: Bridging Language Barriers: Text and Speech Translation with Azure

    6. Lecture 43: Azure's Toolkit for NLP: An Overview

    7. Lecture 44: Analyzing Sentiment with Azure's Text Analytics Service

    8. Lecture 45: From Words to Action: Introduction to Azure's Speech Service

    9. Lecture 46: The Power of Translation: An Overview of Azure's Translator Service

    10. Lecture 47: Grasping Intent with Azure's Language Understanding Service (LUIS)

    11. Lecture 48: The Art of Conversation: Use Cases for Conversational AI on Azure

    12. Lecture 49: Building Knowledge Repositories: Introduction to Azure's QnA Maker and Bot Framework

    13. Lecture 50: Showcasing AI's Linguistic Prowess: A Comprehensive Demonstration on Azure's NLP Services

    1. Summary

About this course

  • $12.99
  • 51 lessons
  • 8 hours of video content

Discover your potential, starting today

FAQs

  • Do I need any prior knowledge about AI or Azure?

    No, while a basic grasp of digital technology is beneficial, the course is structured to guide you from foundational concepts to advanced applications.

  • Who is this course suitable for?

    This course is designed for anyone interested in understanding artificial intelligence and how Microsoft Azure caters to AI applications. Both beginners and experts will find value in our content.

  • Are there any hands-on activities or demonstrations?

    Absolutely! The course includes practical sessions, demonstrations, and hands-on activities to provide a holistic learning experience.