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A conceptual description of principal component analysis, including:
- variance and covariance
- eigenvectors and eigenvalues
- applications
As usual, very little formulas, lots and lots of pictures!

0:00 Introduction
0:46 Taking a picture
1:13 Dimensionality Reduction
2:02 Housing Data
5:09 Mean
7:46 Variance?
12:47 Covariance matrix
13:58 Linear Transformations
18:12 Eigenstuff
19:16 Eigenvalues
19:53 Eigenvectors
20:51 Principal Component Analysis (PCA)
26:05 Thank you!

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Timestamps ⏰
00:00 AI agents course!
01:13 Basics of AI Agents - What is an AI Agent?
02:58 What tasks are agents good for?
05:47 Spectrum of Autonomy
06:45 Context Engineering
07:13 Task Decomposition
07:59 Demo: No-Code Agent System
09:22 Intermediate AI Agents - Measuring Performance
10:39 Memory
11:42 Guardrails
12:50 Reflection
13:56 Tool Use
15:51 Designing Good Tools
16:56 Planning
19:05 Multi-Agent Collaboration
20:38 Multi-Agent System Design - Roles
21:25 Multi-Agent System Design - Communication Patterns
23:09 Multi-Agent System Design - Communication Pitfalls
23:44 Multi-Agent System Design - Best Practices
25:02 Demo: Multi-Agent System in Python
26:14 Advanced AI Agents - Advanced Task Decomposition
29:02 Improving Performance
30:26 Reducing Latency
31:16 Reducing Cost
32:39 Observability and Monitoring
35:10 Security
37:17 Bonus Resource!
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Agentic System Design:
https://gerred.github.io/build....ing-an-agentic-syste

The slides and examples were adapted from a few different courses, including:
https://www.deeplearning.ai/co....urses/design-develop
https://learn.deeplearning.ai/....courses/agentic-ai/i
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☕ If you'd like to support my work, you can buy me a coffee (thank you!): https://ko-fi.com/marinawyss
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🎥 Other videos you might like:

AI Engineering in 76 Minutes (Complete Course/Speedrun!)
https://www.youtube.com/watch?v=JV3pL1_mn2M&t=2s

AI Engineering: A *Realistic* Roadmap for Beginners
https://www.youtube.com/watch?v=dbUIjFXIpis

4 *Real* Machine Learning Projects That Get You Hired - No More Tutorials!
https://www.youtube.com/watch?v=MFSFcPsMsuE

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🦫 About me
I am a Senior Applied Scientist (basically, a blend of Data Scientist/Machine Learning Engineer) at Twitch/Amazon. Outside of my full-time job I'm a 1:1 career coach for people looking to break into the field, with a focus on those from non-traditional backgrounds.

I’m also a Certified Personal Trainer, always busy with too many interests, and really, deeply happy with my life. I hope to be able to help others achieve these things, too.
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✉️ Contact
Instagram: https://www.instagram.com/marina.wyss/
Twitter/X: https://x.com/iammarinawyss
TikTok: https://www.tiktok.com/@gratitudedriven
Leave me a comment here on YouTube!
Business email: business@gratitudedriven.com

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⚖️ Disclaimer
The views and opinions expressed in this video are my own and do not reflect the official policy or position of Twitch/Amazon or any other company I have worked for. All advice and insights shared here are based on my personal experiences and should be considered as such.

Thank you to Kimi for sponsoring this video!

This description may contain affiliate links. If you make a purchase I may make a small commission at no cost to you.

#AI #AIEngineering #AIAgents

In this video, I dive into the world of agentic AI, a concept that's set to be a major buzzword in 2025. We explore how agentic AI combines multiple agents—some of which are language models (LLMs) and others that are tools—to create more sophisticated and effective AI workflows. We walk through a simple example of how an AI agent can draft a marketing plan, another can critique it, and a third can refine it based on the feedback. We also discuss how some agents can be tools, like a Google search to gather data, and how an orchestrator agent can manage the entire process, adjusting steps as needed.

Leave me a comment if you have new topics I should be discussing.

Check out my LinkedIn: https://www.linkedin.com/in/donwoodlock/

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YouTube Timestamps
0:00 – 0:20 Welcome
0:21 – 0:50 Introduction to the concept of Agentic AI
0:51 – 3:50 Explanation of how Agentic AI works
3:50 – 4:08 Advertisement plug-in
4:08 – 5:20 Example of using compound LLM’s
5:21 – 5:55 Why you should use a compound LLM approach
5:57 – 6:45 Best way to train and use LLM’s for optimal outcome
6:45 – 7:40 How to think of LLM as agents
7:41 – 10:05 Not every agent needs to be an LLM
10:06 - 10:50 Possibility of having an orchestrator agent
10:51 – 12:50 How to use these agents
12:51 – 13:26 Closing remarks

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ABOUT INTERSYSTEMS
Established in 1978, InterSystems Corporation is the leading provider of data technology for extremely critical data in healthcare, finance, and logistics. It’s cloud-first data platforms solve interoperability, speed, and scalability problems for large organizations around the globe. InterSystems Corporation is ranked by Gartner, KLAS, Forrester and other industry analysts as the global leader in Data Access and Interoperability. InterSystems is the global market leader in Healthcare and Financial Services.

Website: https://www.intersystems.com/
Youtube: https://www.youtube.com/@InterSystemsCorp
LinkedIn: https://www.linkedin.com/company/intersystems/
Twitter: https://twitter.com/InterSystems

#agenticai #aiagents #LLM

🚀 Complete Claude Code Resources & Course: https://skool.com/scrapes
Don't miss the next build - https://www.youtube.com/@simon....scrapes?sub_confirma
Grab the full resources for this video here (free): https://scrapeshq.notion.site/....Every-Level-of-Claud

Think you're getting the most out of Claude Code? You're probably just scratching the surface. In this video, we break down the 7 levels of mastery, guiding you from basic prompting all the way to building fully autonomous systems. Stop the trial-and-error and start unlocking Claude Code's true potential today.

00:00 - Every Level of Claude Code Explained in 39 Minutes
00:41 - Level 1: The One Thing You Need To Know
03:25 - Level 2: Personalizing CLAUDE for better responses
10:25 - Level 3: Slash Commands, Skills & Hooks - Repeatability
21:21 - Level 4: Connecting Claude Code to Your Apps (MCPs)
26:18 - Level 5: Move from Executor to Supervisor
29:34 - Level 6: Agent Teams
35:57 - Level 7: Fully Autonomous Systems


#claudecodetutorial #masterclaudecode #claudecode