🧠 Dive into the world of Agentic AI, where AI systems act autonomously to achieve goals without human guidance. From personal assistants to autonomous vehicles, agentic AI is redefining sectors with its ability to learn, adapt, and operate independently. However, this new frontier raises ethical questions about accountability and transparency. Learn more about its growing impact!

#AgenticAI #AutonomousAI #AIInnovation #TechEthics #FutureOfAI

MIT 14.02 Principles of Macroeconomics, Spring 2023
Instructor: Ricardo J. Caballero

View the complete course: https://ocw.mit.edu/courses/14....-02-principles-of-ma
YouTube Playlist: https://www.youtube.com/playli....st?list=PLUl4u3cNGP6

This lecture covers an introduction to the topic of macroeconomics, which is the study of the behavior of an economy as a whole. The instructor also talks about the objectives of the course.

License: Creative Commons BY-NC-SA
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Statistical Learning, featuring Deep Learning, Survival Analysis and Multiple Testing

Trevor Hastie, Professor of Statistics and Biomedical Data Sciences at Stanford University - https://statistics.stanford.ed....u/people/trevor-j-ha

Robert Tibshirani, Professor of Statistics and Biomedical Data Sciences at Stanford University - https://statistics.stanford.ed....u/people/robert-tibs

Jonathan Taylor, Professor Statistics at Stanford University - https://statistics.stanford.ed....u/people/jonathan-ta

You are able to take Statistical Learning as an online course on EdX, and you are able to choose a verified path and get a certificate for its completion. You can choose to take the course in R (https://www.edx.org/course/statistica) or in Python (https://www.edx.org/learn/data....-analysis-statistics

For more information about courses on Statistics, you can browse our Stanford Online Catalog: https://stanford.io/3QHRi72

For more information about Stanford's flexible graduate programs visit: https://learn.stanford.edu/YouTube-Grad.html

If you're interested in deepening your expertise and progressing your professional skill set, why not look into the graduate course and program options offered by Stanford Online? During our online information session, you'll discover the range of graduate opportunities available to you, what you can look forward to, and essential information to help you make an informed decision prior to enrollment.

The session includes:
Graduate Course Overview: Here’s what you can expect
Key information about applying and enrolling
Audience Q&A

Explore Stanford Online's graduate education options: https://online.stanford.edu/graduate-education

#gradschool #graduateprogram #onlineeducation

The imagedeep.io series serves to bridge the knowledge gap between medical imaging and AI education.

It was co-founded and is co-instructed by:

Mazen Zawaideh, MD. Chief Radiology Resident and imagedeep.io co-instructor.

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This Agentic AI Full Course 2026 by Simplilearn is designed to help beginners understand how modern AI systems can plan tasks, make decisions, and automate workflows. The course starts with the basics of Agentic AI, how it works, and how it is different from traditional AI models. You will then learn about AI agents, agent workflows, tools, and real-world use cases in simple terms. The course also introduces concepts like prompt engineering, automation, and building AI-powered applications. As you progress, you will explore practical examples and future trends of autonomous AI systems. This course is ideal for anyone who wants to understand next-generation AI technology and how it can be used in real business and development scenarios.

Following are the topics covered in the Agentic AI Full Course 2026:

00:00:00 - Introduction To Agentic AI
00:03:11 - Chapter 1: Basics of Agentic AI
00:08:06 - Chapter 2: Core Concepts of Agentic AI
00:15:15 - Chapter 3: Agentic AI Frameworks & Libraries
00:22:09 - Chapter 4: Memory and Long-Term Context
00:30:01 - Chapter 5: Planning & Decision Chains
00:36:25 - Chapter 6: Advanced Use-Cases of Agentic AI
00:39:48 - Chapter 7: Agent Evaluation and Prompt Optimization
00:43:50 - Chapter 8: Risks, Ethics, and Future of Agentic AI
00:47:27 - Chapter 9: Hands-On Project

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➡️ About Applied Generative AI Specialization

The Applied Gen AI Course equips learners with the skills to build AI-driven solutions. Learn to design, train, and deploy models while addressing ethics, bias, and compliance. Gain industry-relevant experience through hands-on projects to drive innovation and stay competitive in an AI-powered world.

Key Features

✅ Get a program completion certificate from Michigan Engineering Professional Education
✅ Engage in 70+ hours of live online sessions led by industry experts
✅ Access a comprehensive curriculum covering Python, AI literacy, and GenAI principles (VAEs, GANs, and LLMs)
✅ Explore agentic frameworks and create adaptive AI agents using LangChain
✅ Implement advanced prompt engineering, RAG, and fine-tune models for domain-specific AI solutions
✅ Apply best practices for AI transparency, fairness, security, and regulatory compliance
✅ Work with 12+ modern AI tools, including OpenAI, Stable Diffusion, Microsoft Copilot, and Streamlit
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Skills Covered
✅ Prompt Engineering
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✅ LLM Fine Tuning
✅ Stable Diffusion
✅ Variational Autoencoders VAEs
✅ Generative Adversarial Networks GANs
✅ Attention Mechanisms
✅ Transformers
✅ GenAI Application Development
✅ LLM Benchmarking
✅ GenAI Governance
✅ AI Image Generation

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In this video, you'll learn exactly how to build five different AI agents across four different platforms and start selling them to businesses. I walk you through my exact experience scaling an AI agency to six figures in 14 months so you can turn technical skills into a highly profitable business.

We'll cover:
- The core foundations of AI agents, APIs, and how they actually think, decide, and act in the real world.
- Step-by-step tutorials on building 5 high-value AI agents: Lead Gen, Inbound Receptionist, Speed-to-Lead, Executive Assistant, and a Sales Lead Research Copilot.
- How to confidently price your AI agents based on the high ROI and value you provide, rather than hourly rates.
- Proven warm outreach, sales, and delivery strategies to land your first paying clients and retain them long-term.

Timestamps
00:00 - Introduction
01:27 - Foundations
30:20 - Build 1 (inspo by Eddie Chen)
59:17 - Build 2
01:33:07 - Build 3
02:04:39 - Build 4
02:49:24 - Build 5
03:25:10 - Monetization

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You're probably using Claude like a chatbot. Same model, same subscription — but agents can open your browser, run 10 tasks at once, and finish a week of work before lunch.

This is the complete guide to agentic AI in 2026: how to create AI agents, how agents actually work, which platforms are worth your time, and the prompt structure that makes them actually deliver.

▶ WHAT'S IN THIS VIDEO
• What separates a chatbot from a real AI agent (LLM + tools + memory + goals loop)
• Claude Code — Anthropic's reasoning agent, and why it's not just for coding
• OpenAI Codex — lowest-friction entry if you already pay for ChatGPT
• OpenClaw — open-source agent that lives inside Telegram, WhatsApp, iMessage
• Google Antigravity — visual agent built on Gemini, best for front-end and design
• Prompt Contracts: Goal → Constraints → Format → Failure (the 4-part structure that stops agents from going off the rails)
• Memory files: teach your agent once and never repeat yourself

⏱️ TIMESTAMPS:
00:00 - The most expensive AI mistake right now
01:21 - What an AI agent actually is
03:30 Claude Code — install & first run
06:45 OpenAI Codex — install & first run
09:07 OpenClaw — install & first run
12:25 Google Antigravity — install & first run
16:44 Prompt Contracts: the 4-section structure
21:53 Memory files: teach once, never repeat

#AIAgents #ClaudeCode #AIAutomation #Brevo

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This video on Agentic AI Full Course 2026 by Simplilearn will help you learn Agentic AI from beginner to advanced level and understand how autonomous AI systems can plan, reason, make decisions, and execute complex tasks with minimal human intervention. The course begins with an introduction to Agentic AI and explains how AI agents differ from traditional AI models by combining reasoning, planning, memory, tool usage, and goal-oriented execution. You will learn key concepts such as AI agents, large language models (LLMs), prompt engineering, Retrieval-Augmented Generation (RAG), tool calling, agent workflows, multi-agent systems, memory management, and orchestration frameworks.

Following are the topics covered in the Agentic AI Full Course 2026

00:00:00 - Introduction To Agentic AI Full Course 2026
00:03:54 - Basics of Agentic AI
00:08:06 - Core Concepts of Agentic AI
00:15:15 - Agentic AI Frameworks & Libraries
00:22:09 - Memory and Long-Term Context
00:30:01 - Planning & Decision Chains
00:36:25 - Advanced Use-Cases of Agentic AI
00:39:48 - Agent Evaluation and Prompt Optimization
00:43:50 - Risks, Ethics, and Future of Agentic AI
00:47:27 - Hands-On Project
04:08:49 - Top Hacks To Speed Up Your AI Coding Workflow
05:51:53 - Generative Adversarial Tutorial
06:52:32 - Emergent AI App Builder Tutorial For Beginners
07:03:58 - Top 5 AI Automation Tools 2026
07:15:15 - How To Build WhatsApp AI Agent Using n8n
08:14:34 - Mobile App Development In 12 Minutes Using AI
09:40:37 - LangSmith Tutorial For Beginners 2026
10:14:58 - LangChain Tutorial For Beginners 2026
11:00:54 - LangGraph vs LangChain vs LangFlow vs LangSmith 2026
11:10:27 - Build LLM Apps Using Langchain
12:37:42 - Build a Chatbot Using Langchain
13:43:40 - What Is Miro And How To Use It?
13:59:30 - MetaGPT Tutorial For Beginners
14:56:55 - Streamlit Tutorial For Beginners
15:23:37 - Prompt Engineering Tutorial For Beginners
15:57:03 - LLM scale and deployment
16:07:26 - Large language models overview
16:14:53 - Features, tokens, and model choice
16:21:24 - AI tools across industries
16:26:32 - Resume and job matching demo
16:37:05 - Prompt engineering fundamentals
16:54:41 - Everyday prompt use cases
17:04:21 - Multimodal AI capabilities
18:19:50 - Key takeaways and next steps
18:26:41 - Effective prompt design principles
19:08:36 - Gen AI For Data Analytics
20:53:11 - Data integrity and EDA setup
21:42:42 - Data modeling with genAI
22:07:34 - Modeling techniques and use cases
23:46:43 - Future trends and wrap-up
24:03:57 - Gen AI Powered SQL For Data Analytics
25:09:42 - ER model fundamentals
26:26:15 - Database commands and basic setup
27:04:37 - Table creation and constraints
28:33:57 - Query logic and aggregation
29:00:27 - SQL functions introduction
30:19:37 - Using ChatGPT for SQL practice
30:22:09 - Agentic AI Projects For Beginners 2026
30:46:15 - What Is GenAI?
30:50:35 - How To Become Generative AI Engineer?
31:00:40 - Generative AI Vs Agentic AI Vs AI Agents
32:37:26 - LLM As Operating System
33:30:16 - LLM Benchmarking
34:46:06 - Prompt engineering with GPT-4
35:41:26 - Vibe coding tools comparison
36:06:01 - Generative AI learning roadmap
36:58:56 - AI for research and content
37:22:52 - Data insights and AI workflows
38:12:03 - Gemini CLI versus Claude Code
38:37:03 - ChatGPT attempts LeetCode problems
40:56:55 - Prompt library foundations
41:11:56 - Reasoning with o1 models
42:57:42 - Prompt library recap
43:56:35 - How To Build AI Automations Using n8n
44:26:47 - How To Build a Self Learning AI Agent
46:20:12 - Top 30 Generative AI Interview Questions 2026

✅Subscribe to our Channel to learn more about the top Technologies: https://bit.ly/2VT4WtH

⏩ Check out More AI Videos By Simplilearn: https://youtube.com/playlist?l....ist=PLEiEAq2VkUULyr_

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Everyone is talking about AI agents. Almost nobody explains what they actually are or how to build one. That changes right now.

Here's the honest one-sentence version: an AI agent is a language model that can use tools, running in a loop until it finishes a job. That's it. Everything else is just details — and in this video I break all of those details down clearly, then show you how to build the same agent in four completely different ways.

Want to make real money with coding? I share high-signal insights on careers, monetization, and leverage in my free newsletter. Join here and get my guide How to Make Money With Coding instantly: https://techwithtim.net/newsletter

🚀 Tools I Use
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⏳ Timestamps ⏳
00:00 | Overview
00:52 | What Are Agents
01:53 | The Key Building Blocks
07:15 | The Landscape/Options
08:25 | DataCamp
09:52 | Tier 1 - No Code
12:36 | Tier 2 - Low Code
15:02 | Tier 3 - Agent Harness
17:44 | Tier 4 - Full Code
19:54 | How to Pick?

Hashtags
#AIAgents #ArtificialIntelligence #AITutorial

UAE Media License Number: 3635141

Get started with @GensparkProduct and get unlimited access to AI usage: https://bit.ly/4tBYC7v

New registered users can get free credits. What’s wild, Genspark is offering unlimited use of AI chat and AI image for all paid users in 2026. Unlimited subject to abuse guardrails.

Most people are still using AI like it's 2023 — open ChatGPT, ask a question, copy the answer. But there are actually four levels of AI usage, and the majority of people are stuck on level one without even knowing the other three exist. In this video, I'll walk through real demos of each level so you can see exactly how to apply them, including a live example of an AI agent that researches, builds, and deploys a full landing page — completely on its own.

Want to make real money with coding? I share high-signal insights on careers, monetization, and leverage in my free newsletter. Join here and get my guide How to Make Money With Coding instantly: https://techwithtim.net/newsletter

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⏳ Timestamps ⏳
00:00 | Overview
01:16 | AI Framework
04:20 | Level 1 - Chat
06:00 | Level 2 - Tools
09:15 | Level 3 - Workflows
14:15 | Level 4 - Claw/Agents
18:32 | When to Use Each
19:56 | Genspark Usage

Hashtags
#Genspark #AIAgents #WorkwithGenspark

UAE Media License Number: 3635141

Host Your AI Agent with Hostinger 👉 https://www.hostg.xyz/SHJwM

In this video, I explain how AI agents work, then show how to deploy Hermes on a Hostinger VPS, connect it to OpenRouter and Telegram, give it scheduled jobs, use persistent memory and learned skills, and keep the agent secure and inexpensive while it runs around the clock.

Grab The AI Agent Build Kit 👉 https://youri-van-hofwegen.kit.com/35ac6b24d1

for inquiries: roboverse at youripartnerships.com

#Roboverse

Want to dive deeper? This curriculum is covered in the following online courses:
- Agentic AI professional education course: https://stanford.io/4zOyjPN
- XCS329 graduate course: https://online.stanford.edu/co....urses/cs329a-self-im

A similar curriculum is covered in XCS329z https://online.stanford.edu/co....urses/cs329z-enginee

Follow along with the course schedule and syllabus: https://cs329a.stanford.edu/

View the course playlist: https://www.youtube.com/playli....st?list=PLangBM27OtE

Video Summary:
This first lecture videoof Stanford's CS329A, Self-Improving AI Agents, taught by Aakanksha Chowdhery and Azalia Mirhoseini on September 22, 2025, opens with an overview of scaling laws that link model parameters, training compute, and dataset size to lower test loss in large language models from GPT-2 through GPT-4. It covers few-shot and zero-shot learning, the emergence of chain-of-thought reasoning in larger models, and the role of instruction tuning and reinforcement learning from human feedback in the development of ChatGPT. The lecture introduces inference-time scaling through the Large Language Monkeys project, which repeatedly samples a model's outputs and selects correct answers with a verifier to improve performance without retraining. It then traces the shift from single-turn chatbots to agent workflows such as prompt chaining, routing, parallelization, and orchestrator-worker patterns, using Claude Code and deep research tools as examples. The session closes with logistics for the course.

Speaker Bios:
Aakanksha Chowdhery
Adjunct Professor of Computer Science, Stanford University

Dr. Aakanksha Chowdhery is pushing the frontier of agentic LLMs, focusing on recursive self-improvement and long-horizon agents that learn and deploy in the real world. She is one of the few researchers globally who has led frontier model training end-to-end, across both dense and mixture-of-experts (MoE) architectures. At Google, she led the 540B PaLM model, the largest densely trained language model in the world at the time. She subsequently drove pre-training and scaling of Gemini's MoE models across multiple generations, and contributed key components to PaLM-E, Med-PaLM, and the Pathways infrastructure underpinning Google's large-model efforts. She went on to build and lead pretraining teams for open intelligence efforts at Reflection and Meta. Earlier, she held research roles at Microsoft Research and Princeton. At Stanford, where she earned her PhD, she teaches CS329A (Self-Improving AI Agents) and serves as Program Chair for MLSys 2026.

Azalia Mirhoseini
Assistant Professor of Computer Science, Stanford University

Azalia Mirhoseini is a co-founder of Ricursive Intelligence, a frontier lab dedicated to recursive self-improvement through AI that designs the chips that fuel it. She is also an Assistant Professor of Computer Science at Stanford University where she directs Scaling Intelligence, a lab focused on developing scalable and self-improving AI systems and methodologies toward the goal of artificial general intelligence. Previously, she spent several years in industry AI labs, including Google Brain, Anthropic, and Google DeepMind, working on the development of Claude and Gemini. Her past work includes Mixture-of-Experts (MoE) neural architectures, now predominantly used in leading generative AI models; AlphaChip, a pioneering work on deep reinforcement learning for layout optimization used in the design of advanced chips like Google AI accelerators (TPUs) and data center CPUs; as well as pioneering research on LLM Test-Time Scaling. Her work has been recognized through the Okawa Research Grant, the Google ML and Systems Junior Faculty Award, MIT Technology Review's 35 Under 35 Award, the Best ECE Thesis Award at Rice University, publications in flagship venues such as Nature, and coverage by various media outlets, including WSJ, NYT, Forbes, MIT Technology Review, IEEE Spectrum, WIRED, and TechCrunch.

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💌 Contact Us: [email protected]
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» Thank you for watching! Have a nice day!
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