Azure Bay Area Tech Community

8/12/2026 :1pm-4pm PST
8/13/2026 :1pm-4pm PST

Come hang out and learn how to build your own AI that can whip up text, images, and sounds using Python!

Learning Objectives:
- After this course, you will have the opportunity to:
- Develop an understanding of using OpenAI generative models using Python
- Understanding different API endpoints
- Learn how to debug code, improve code, and even write code from scratch using API
- Generate interesting images using the DALL-E API
- Convert Audio into transcriptions and translation using powerful Whisper API

Course Outline:
Introduction To OpenAI Models
- Brief history of OpenAI
- Overview of OpenAI models: GPT-4, DALL-E, and Whisper
- Signing up for a developer account
- Hands-on: Sign up for a developer account
Getting Started With OpenAI Python API
- Using API key
- Generating first completion request
- Understanding Tokens, MAX_TOKENS
- Stop sequences, N and Echo
- Hands-on lab with these topics
Important API Parameters
- Temperature Parameter
- Understanding Top P
- Frequency Penalty and Presence Penalty
- Streaming Responses
- Using Completion and Chat API
- Cost Estimation with TikToken
- Hands-on lab with these topics
Working With Code
- Use API to understand code and calculate time complexity
- Make GPT-4 find and fix code bugs
- Translate code from JavaScript to Python
- Generating code
- Hands-on lab with these topics
DALL-E Image Generator
- What is DALL-E?
- Using DALL-E API to generate images
- Hands-on lab with DALL-E
Whisper Audio To Text
- Introducing Whisper
- Translating and Transcribing with Whisper API
- Transcribing Non-English Audio
- Hands-on lab with these topics

Get ready to dive into how Microsoft Copilot can make your workday way smoother and smarter!

Learning Objectives:
Participants will have the opportunity to:
- Understand the capabilities and limitations of Microsoft Copilot.
- Utilize Copilot in Microsoft Word to generate, summarize, and refine content.
- Leverage Copilot in Excel for data analysis, formula generation, and insights.
- Enhance email and scheduling efficiency with Copilot in Outlook.
- Improve collaboration and meeting productivity using Copilot in Teams.
- Use Copilot in PowerPoint to create AI-generated presentations.
- Implement best practices for AI-assisted work automation.

Topic Outline:
Introduction to Microsoft Copilot
- Overview of Microsoft Copilot and its AI capabilities
- Understanding Copilot¿s integration across Microsoft 365 apps
- Ethical and security considerations when using AI-powered tools
Copilot in Microsoft Word: AI-Powered Writing and Editing
- Content generation and refinement
- Summarizing and reformatting documents
- Hands-on: Creating and enhancing reports with Copilot
Copilot in Microsoft Excel: AI for Data Analysis
- Automated data insights and visualization
- Generating and explaining complex formulas
- Hands-on: Analyzing datasets and creating reports with Copilot
Copilot in Microsoft Outlook: Smarter Emails and Scheduling
- AI-assisted email drafting and summarization
- Scheduling and managing meetings efficiently
- Hands-on: Automating email and calendar tasks with Copilot
Copilot in Microsoft Teams: Enhancing Collaboration
- AI-powered meeting notes and action items
- Summarizing chat discussions and key takeaways
- Hands-on: Using Copilot to improve team collaboration
Copilot in Microsoft PowerPoint: AI-Generated Presentations
- Creating presentations with AI-generated content and design
- Automating slide formatting and improvements
- Hands-on: Building a professional presentation with Copilot
Best Practices for Using Copilot in Workflows
- Optimizing Copilot for everyday tasks
- Understanding AI limitations and when to verify outputs
- Hands-on: Applying Microsoft Copilot in a real-world work scenario

8/19/2026 : 9am-12pm PST
8/20/2026 : 9am-12pm PST

Get the lowdown on AI and machine learning without the tech jargon in this chill, beginner-friendly meetup.

In this course, you will have an opportunity to learn how to:

• Describe Supervised and Unsupervised learning techniques and usages
• Compare AI vs ML vs DL
• Understand techniques like Classification, Clustering and Regression
• Discuss how to identify which kinds of technique to be applied for specific use case
• Understand the popular Machine offerings like Amazon Machine Learning, TensorFlow, Azure Machine Learning, Spark mlib, Python and R etc.
• Understand the relation between Data Engineering and Data Science
• Understand the Data Science process
• Discuss Machine Learning use cases in different domains
• Identify when to use or not use Machine Learning
• Define how to form a ML team for success
• Understand usage of tools through a ML Demo and hands-on labs.

Topic Outline:

• Course Introduction
• History and background of AI and ML
• Compare AI vs ML vs DL
• Describe Supervised and Unsupervised learning techniques and usages
• Machine Learning patterns

- Classification
- Clustering
- Regression

• Gartner Hype Cycle for Emerging Technologies
• Machine Learning offerings in Industry
• Discuss Machine Learning use cases in different domains
• Understand the Data Science process to apply to ML use cases
• Understand the relation between Data Engineering and Data Science
• Identify the different roles needed for successful ML project
• Hands-on: Create account for Microsoft Azure Machine Learning Studio
• Demo: ML using Azure ML studio
• Demo: ML using Scikit-learn
• References and Next steps