What Most Engineers Get Wrong About AI and Career | Kelsey Hightower

Why DevOps Demand Is Exploding (5 Critical Factors)

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ML models need solid infrastructure to run in production. Grab our DevOps Roadmap to learn the foundational skills that power modern AI systems: https://bit.ly/ai-to-devops-roadmap 

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In this video, we're diving into something that's absolutely transforming our digital landscape: AI, Machine Learning, Neural Networks and Deep Learning. And we are gonna demystify every single one of these terms, all the confusing buzzwords and tech terms like supervised learning, reinforcement learning, all of them once and for all.

By the end of this Machine Learning explained video, you'll understand not just what these concepts are, but how they're reshaping our digital world - and why they are soo powerful and exciting.

So let's get started and demystify the world of artificial intelligence!


▬▬▬▬▬▬ T I M E S T A M P S ⏰  ▬▬▬▬▬▬
00:00 - Intro
01:53 - Netflix Use Case
02:16 - Symbolic AI
03:26 - Machine Learning
05:50 - From Symbolic AI to ML
10:29 - ML Variations
10:52 - Supervised Learning
14:36 - Unsupervised Learning
17:53 - Reinforcement Learning
25:02 - Comparison of ML Types
29:52 - Neural Networks
36:15 - Deep Learning
36:15 - Why Adoption Took So Long


▬▬▬▬▬▬ Connect with me 👋   ▬▬▬▬▬▬ 
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Machine Learning Explained | Machine Learning Models | Types of ML Models

The Big Picture: AI, Machine Learning, and Neural Networks

What Most Engineers Get Wrong About AI and Career | Kelsey Hightower

Why DevOps Demand Is Exploding (5 Critical Factors)

How a Software Developer Becomes DevOps in 6 Months

LIVE DevOps Career Workshop