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02:32:22 Can ChatGPT Solve LeetCode Problems?
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04:29:45 Build a Game using Python and ChatGPT
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06:10:28 How to use ChatGPT for SEO?
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7:11:53 Explore 10 Mind-Blowing GPTs Inside ChatGPT - WebPilot Will Amaze You
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➡️ About Artificial Intelligence Engineer

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Today we're diving deep into one of the hottest topics in AI right now. Building actual agents in Python. Not just chat bots to respond to your queries, but autonomous systems with memory goals and the ability to take actions in the world.

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⏳ Timestamps ⏳
00:00 | AI Agents
00:37 | NVIDIA Free AI Courses
01:38 | AI Agent Building Blocks
05:30 | Python Frameworks
09:01 | Python Tools
09:40 | AI Agent Patterns
13:15 | Picking Your Stack

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Learn how to use AI tools to become more productive as a developer.

You will master AI pair programming and agentic terminal workflows using top-tier tools like GitHub Copilot, Anthropic's Claude Code, and the Gemini CLI. The course also covers open-source automation with OpenClaw, teaching you how to set up a highly customizable, locally hosted AI assistant for your development environment. Finally, you will learn how to maintain high code quality and streamline your team's workflow by integrating CodeRabbit for automated, AI-driven pull request analysis.

Some of this course is based on this article from Mrugesh Mohapatra: https://www.freecodecamp.org/n....ews/how-to-become-an

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Contents
- 0:00:00 Introduction to AI-Assisted Development
- 0:00:42 Core Fundamentals: Tokens, Context Windows, and Hallucinations
- 0:05:48 When to Use AI vs. When to Code Manually
- 0:06:57 Setting Up GitHub Copilot in VS Code
- 0:09:42 Copilot Pricing and Plans
- 0:10:18 First Steps: Ghost Text and Code Completions
- 0:13:40 Pro Tip: The Neighboring Tabs Trick
- 0:15:52 Practice Exercise: Building a To-Do App
- 0:17:15 Interaction Modes: Ask, Edit, and Agent Mode
- 0:21:44 Agent Mode: Building a Full REST API Autonomously
- 0:24:52 Repository Customization with Instruction Files
- 0:26:34 Using Participants and Slash Commands
- 0:28:34 Automated Code Reviews with CodeRabbit
- 0:30:57 Setting Up CodeRabbit for GitHub Repositories
- 0:32:03 Simulating Real-World PR Reviews and Security Fixes
- 0:37:33 Chatting with AI Directly in Pull Requests
- 0:41:12 Configuring CodeRabbit Behaviors (.yaml)
- 0:41:58 Local Reviews via the CodeRabbit CLI
- 0:48:13 Powerful Terminal AI: Claude Code vs. Gemini CLI
- 0:49:12 Claude Code: Reasoning, Thinking Modes, and Fixes
- 0:53:02 Gemini CLI: Million-Token Context and Multimodal Features
- 0:57:01 OpenClaw: Your Open-Source Personal AI Assistant
- 1:01:01 Automating Tasks with Cron Jobs and Desktop Actions
- 1:03:33 Orchestrating Your AI Workflow
- 1:08:27 Model Context Protocol (MCP): Giving AI Real-World Tools
- 1:12:40 AI Code Quality and Security Essentials
- 1:15:03 The Formula for Better Prompt Engineering
- 1:16:13 Course Recap and Final Resources

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Everyone seems to be using AI to code now, and in this video, I'm showing you how to actually code with AI - not vibe code. Because there's a huge difference between software developers who strategically guide AI, and vibe coders who just let it take the wheel.

0:00 Everyone’s Coding with AI
0:32 Stop Repeating Yourself
1:45 Prompting is Important
4:21 The Software Development Workflow
6:42 Pair Programming
7:57 Implement Multiple Agents
9:25 git worktree
9:57 Extra Stuff AI Can Help With
11:21 Pull the Plug
11:58 Always Remember This

This video is sponsored by Warp.

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What is Artificial Intelligence? by Simplilearn is an introduction to artificial intelligence that deals with the fundamentals of artificial intelligence and its critical technicalities. Artificial Intelligence In five Minutes tutorial video will help you enhance your knowledge of Artificial Intelligence. This video on AI Explained by Simplilearn covers the most Critical AI Concepts in the simplest ways.

Don't forget to take the quiz at 03:50.

00:00:00 Introduction to What is AI?
This video segment on what is AI? will cover the briefing on Artificial Intelligence.

00:00:30 What is AI?
This section of the What is AI? video will give the technical definition of AI.

00:00:57 Types of AI
This section of the What is AI? video will cover the types of AI available in the industry.

00:01:46 Machine learning and Deep learning
This section of the What is AI? video will cover the aspects of machine learning and deep learning in detail.

00:02:30 Deep Learning and NLP
This section of the What is AI? video will cover the aspects of Deep Learning and NLP in detail.

00:02:45 AI in different fields
This section of the What is AI? video will cover the various fields where AI is currently operational.

00:03:30 Salary of AI engineer
This section of the What is AI? video will cover the salary details of AI engineers.

00:03:48 Quiz on What is AI?
This section of the What is AI? video will have a quick and simple quiz question on AI.

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Find the full "AI Agents for Beginners" Course and code samples here ➡️ https://aka.ms/ai-agents-beginners

In this lesson:
00:00 Introduction
00:00 Lesson 1 What are AI agents?
5:55 Lesson 2 Which agent framework to use
14:14 Lesson 3 How to design good AI agents
19:35 Lesson 4 What is the Agent Tool Use Design Pattern?
25:47 Lesson 5 What is agentic RAG?
33:03 Lesson 6 How to build effective AI agents
38:54 Lesson 7: What is the AI Agent Planning Design Pattern?
44:20 Lesson 8 How to use a multi-AI agent system
51:22 Lesson 9 How can AI agents improve?
58:32 Lesson 10 How to deploy AI agents into production

🔥Advanced Certification in Agentic AI Engineering: https://www.edureka.co/agentic-ai-training-course
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In this video, we dive into the exciting developments and advancements that are set to shape the industry in 2025. We will learn what Agentic AI is and how it goes beyond traditional AI by acting with purpose and autonomy. You’ll discover 5 powerful secrets behind Agentic AI—from multi-agent collaboration to real-world tool integration and ethical guardrails. By the end, you’ll understand how Agentic AI is transforming industries and why it’s the future of intelligent systems.

Join us as we discuss the latest trends, innovations, and predictions for Agentic AI in 2025. Whether you're an AI enthusiast, a business leader, or simply curious about the future of technology, this video is for you.

00:00:00 Introduction
00:17:13 What is Agentic AI?
00:36:19 Agentic AI vs Generative AI
00:46:07 Agentic AI Roadmap
00:52:18 Introduction to Artificial Intelligence
00:58:37 Introduction to Deep Learning
02:28:29 Artificial Neural Network
03:08:09 Transformers Explained Using Generative AI
03:09:37 Transformers Neural Networks Explained
03:21:52 What are Large Language Models?
03:38:17 LLM vs SLM
03:41:59 What is LangChain?
03:59:35 What is RAG?
04:22:07 LLMOps: The Future of AI Development
04:29:57 Prompt Engineering
04:42:41 Natural Language Processing (NLP) & Text Mining using NLTK
05:22:21 KNN Algorithm using Python
05:45:23 Alibaba’s Qwen 2.5-Max Just Beat GPT-4 & DeepSeek?
05:49:47 DeepSeek vs OpenAI: Who Wins the AI Race?
06:02:06 Deep Learning Interview Questions

✅Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV
📝Feel free to share your comments below.📝
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- - - - - - - - - - - - - -

What is Agentic AI?
Agentic AI refers to artificial intelligence systems that can autonomously make decisions, take actions, and pursue goals with minimal human intervention. Unlike traditional AI, which adheres to predetermined rules, agentic AI may dynamically adapt to new situations. It is widely utilized in robotics, virtual assistants, self-driving cars, and sophisticated decision-making processes.

What are the prerequisites for this Agentic AI Training Course?
In order to complete this course successfully, participants need to have a basic understanding of the Python programming language, machine learning, deep learning, natural language processing, generative AI, and prompt engineering concepts.

Who should take this AI Agents Training Course?
The Agentic AI online Training Course is ideal for AI enthusiasts, developers, and professionals looking to build autonomous AI agents. It is best suited for LLM Engineers, Generative AI Engineers, AI Research scientists, AI/ML practitioners, and freshers who want to leverage Agentic AI for automation and decision-making.

For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: +18885487823(toll-free).

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How is a beginner supposed to get started learning machine learning? I'm going to describe a free 3 month curriculum to help you go from beginner to well-versed in modern machine learning. Transformers, Diffusers, Large Language Models, and dozens of new ML Ops tools have been released the past few years! The field of ML has changed so much and there are so many new tools to learn. Using the help of AI services, we can learn a lot faster than usual. This learning plan is something I'd create for myself if I were to get started today, but I'm going to open source it for you guys. This curriculum will cover all the math concepts, machine learning, deep learning, ML Ops, and tons of cool projects to get you up to speed with the field as fast as possible. If anyone asks how to best get started with machine learning, direct them to this video!

Curriculum from this video:
https://github.com/llSourcell/LearnML

Subscribe! And like. And comment. That's what keeps me going.

Want more education? Connect with me here:
Twitter: https://twitter.com/sirajraval
Instagram: https://www.instagram.com/sirajraval
Facebook: https://www.facebook.com/sirajology

Curriculum Links
-----------------------------------------------------
Month 1: Machine Learning

Week 1: Python Fundamentals - https://allendowney.github.io/DSIRP/index.html

Week 2: Math of Machine Learning -
http://www.xaktly.com/XMathMain.html

Week 3: Data Analysis -
https://www.kaggle.com/learn

Week 4: Machine Learning Techniques -
https://github.com/ipython-books/cookbook-2nd

Month 2: Deep Learning

Week 1: Neural Networks
http://d2l.ai/

Week 2: Transformers
https://huggingface.co/course/chapter1/1

Week 3: Diffusers
https://www.fast.ai/posts/part2-2022-preview.html

Week 4: Deep Reinforcement Learning
https://simoninithomas.github.io/deep-rl-course/

Month 3: Machine Learning Operations

Week 1: Design -
https://madewithml.com/

Week 2: Development -
https://fullstackdeeplearning.com/course/2022/

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🚀 Complete Agentic AI Course for Beginners in Telugu

Code files:
LangGraph code:
https://drive.google.com/file/....d/1l6N1T06ghWX-PpTSR

CrewAI code:
https://drive.google.com/file/....d/13QghlEBku_OCM5kYi

In this 3-hour full course, you will learn Agentic AI from scratch and understand how to build your own AI Agents step by step.

This course is explained in Simple Telugu and is perfect for beginners who want to learn modern AI Agent development.

What You Will Learn

✅ What is an AI Agent?
✅ AI Agent vs Chatbot
✅ How AI Agents think, plan, use tools and complete tasks
✅ LangGraph explained with practical workflow
✅ MCP — Model Context Protocol
✅ A2A — Agent-to-Agent Protocol
✅ CrewAI multi-agent systems
✅ OpenAI Agents SDK overview
✅ How to build AI Agents from scratch
✅ Real-world Agentic AI use cases

Topics Covered

🤖 AI Agents
🔷 LangGraph
🔗 MCP
🤝 A2A Protocol
👥 CrewAI
⚡ OpenAI Agents SDK
🧠 Agentic AI Architecture
💻 Practical AI Agent Projects

If you want to learn AI, Machine Learning, Generative AI and Agentic AI in Simple Telugu, subscribe to AI School of India.

Let’s make AI learning simple and practical 🚀

Hashtags

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Next: Your first AI Employee: https://youtu.be/jYbzKYwdy24

In this video, I break down what Agentic AI really is, beyond the hype and buzzwords. While most people think of AI as just chatbots like ChatGPT, the next evolution - Agentic AI - is quietly transforming how we interact with technology.

I explain in simple terms how Agentic AI systems can actually take initiative, complete complex tasks autonomously, and solve problems without constant human guidance. You'll understand the key differences between reactive AI tools and truly agentic systems that can plan, adapt, and achieve goals.

Whether you're a developer, business leader, or just curious about AI's future, this concise explanation gives you the foundation to understand why Agentic AI represents such a significant leap forward in artificial intelligence.

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In this video, we will build a Vision Language Model (VLM) from scratch, showing how a multimodal model combines computer vision and natural language processing for vision QA. GitHub below ↓

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GitHub Link of the Code
https://github.com/uygarkurt/VLM-PyTorch

Dataset
https://huggingface.co/dataset....s/uygarkurt/simple-i

Implement and Train ViT From Scratch for Image Recognition - PyTorch
https://youtu.be/Vonyoz6Yt9c

Implement Llama 3 From Scratch - PyTorch
https://youtu.be/lrWY4O5kUTY

Fine-Tune Visual Language Models (VLMs) - HuggingFace, PyTorch, LoRA, Quantization, TRL
https://youtu.be/3ypHZayanBI

ViT (Vision Transformer) - An Image Is Worth 16x16 Words (Paper Explained)
https://youtu.be/8phM16htKbU

What should I implement next? Let me know in the comments!

00:00 VLM Explanation
04:42 Downloading Dataset
06:39 Imports
10:09 Hyperparameters
12:37 Data Processing
16:03 ViT Definition
19:05 VLM Implementation
41:40 Sample Inference
45:20 Data Loaders
52:46 Training Loop
57:50 Inference

References
https://github.com/AviSoori1x/seemore

Buy me a coffee! ☕️
https://ko-fi.com/uygarkurt

In this comprehensive hands-on workshop, Jon Krohn and Ed Donner introduce AI agents, including multi-agent systems. All the essential theory of agentic AI is provided alongside hands-on code demos in Python that feature the critical agent frameworks: MCP, CrewAI and the OpenAI Agents SDK. From design considerations through to practical implementation tips, by completing all four modules in this video, you will have all the knowledge and skills needed to develop and deploy your own multi-agent teams.

The four modules are:
1. Defining Agents (with OpenAI Agents SDK)
2. Designing Agents (with CrewAI)
3. Deploying Agents (with MCP)
4. The Future of Agents

The accompanying slides and code are available in GitHub: github.com/ed-donner/action

Jon Krohn is co-founder and CEO of Y Carrot, a software firm that specializes in designing and implementing agentic AI systems for enterprises. He also serves as ML Practice Fellow at Lightning AI and is the host of SuperDataScience, the data science industry’s most listened-to podcast. He wrote the book Deep Learning Illustrated, an instant #1 bestseller that was translated into seven languages. He lectures at leading universities (Columbia, NYU), keynotes at top global conferences (Web Summit, Collision) and presents content on Bloomberg TV that’s viewed by millions. Jon holds a PhD from Oxford and has been publishing on machine learning in prominent academic journals since 2010.

Ed Donner is renowned for his teaching on LLMs and agentic AI via O'Reilly and Udemy. He’s the co-founder and CTO of Nebula.io, the platform that uses generative AI and other forms of machine learning to source, understand, engage and manage talent. Previously, Ed was the founder and CEO of AI startup untapt, an Accenture Fintech Innovation Lab company that was acquired in 2020. Before that, Ed was a Managing Director at JPMorgan Chase, leading a team of 300 software engineers in Risk Technology across three continents, the culmination of a 15-year technology career on Wall Street. Ed holds a patent for a deep learning matching engine issued in 2023 and a master's in physics from Oxford.

This high-quality footage was shot live during a packed house at the Open Data Science Conference (ODSC) East in Boston on May 14th, 2025. The director of photography was Lucie McCormick and media editor was Mario Pombo.

Two bonus goodies below:

Follow this link to get Ed's 17-hour "Complete Agentic AI Engineering Course" on Udemy at a discounted rate (as well as coupon links to all of his Udemy courses): https://edwarddonner.com/2025/....05/28/connecting-my-

Use this link to skip the waitlist for Lightning AI Studios and get 15 free GPU-compute credits every month: https://lightning.ai/?inviteCo....de=Agentic-AI-worksh