𝐄𝐝𝐮𝐫𝐞𝐤𝐚'𝐬 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐌𝐚𝐬𝐭𝐞𝐫𝐬 𝐩𝐫𝐨𝐠𝐫𝐚𝐦: https://www.edureka.co/masters....-program/data-scient
🔥Data Science with Python Certification Course: https://www.edureka.co/data-sc....ience-python-certifi
🔥Integrated MS+PGP Program in Data Science & AI:https://www.edureka.co/dual-ce....rtification-programs
This Edureka Data Science Full Course video will help you understand and learn Data Science Algorithms in detail. This Data Science Tutorial is ideal for both beginners as well as professionals who want to master Data Science Algorithms.
00:00:00 Introduction
00:01:31 What is Data Science?
00:03:26 Who is a Data Scientist?
00:35:36 Top 10 Reasons to Learn Data Science
00:40:12 Data Science Basics
01:33:28 Data Life Cycle
01:37:03 Statistics and Probability
03:09:33 Hypothesis Testing Statistics
04:02:26 What is Machine Learning?
04:19:21 Linear Regression
04:45:59 Logistic Regression
05:34:29 Decision Tree Algorithm
06:19:50 Random Forest
06:47:02 KNN Algorithm
07:19:27 Naive Bayes Classifier
07:39:45 Support Vector Machine
08:05:14 K- Means Clustering Algorithm
08:25:22 Apriori Algorithm Explained
08:55:04 Reinforcement Learning
09:16:30 What is Deep Learning?
09:40:24 Introduction to Keras
10:11:51 Data Science Roadmap
10:21:05 Data Science vs Data Analytics
10:31:05 Data Science for Non Programmers
10:59:00 Data Science Interview Questions

🔴 𝐋𝐞𝐚𝐫𝐧 𝐓𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐅𝐨𝐫 𝐅𝐫𝐞𝐞! 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐘𝐨𝐮𝐓𝐮𝐛𝐞 𝐂𝐡𝐚𝐧𝐧𝐞𝐥: https://edrk.in/DKQQ4Py
📝Feel free to share your comments below.📝

🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬

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🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐔𝐧𝐢𝐯𝐞𝐫𝐬𝐢𝐭𝐲 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐬

🔵 Post Graduate Program in DevOps with Purdue University: https://bit.ly/3Ov52lT

🌕 Advanced Certificate Program in Data Science with E&ICT Academy, IIT Guwahati: http://bit.ly/3V7ffrh

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🌕Advanced Certificate Program in Cybersecurity with E&ICT Academy, IIT Guwahati: https://bit.ly/3Pd2utG

📌𝐓𝐞𝐥𝐞𝐠𝐫𝐚𝐦: https://t.me/edurekaupdates
📌𝐓𝐰𝐢𝐭𝐭𝐞𝐫: https://twitter.com/edurekain
📌𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧: https://www.linkedin.com/company/edureka
📌𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦: https://www.instagram.com/edureka_learning/
📌𝐅𝐚𝐜𝐞𝐛𝐨𝐨𝐤: https://www.facebook.com/edurekaIN/
📌𝐒𝐥𝐢𝐝𝐞𝐒𝐡𝐚𝐫𝐞: https://www.slideshare.net/EdurekaIN
📌𝐂𝐚𝐬𝐭𝐛𝐨𝐱: https://castbox.fm/networks/505?country=IN
📌𝐌𝐞𝐞𝐭𝐮𝐩: https://www.meetup.com/edureka/
📌𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲: https://www.edureka.co/community/

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

️🔥 Professional Certificate in AI and Machine Learning - https://www.simplilearn.com/professional-aiml-program?utm_campaign=yMot_Z9yjZ8&utm_medium=DescriptionFirstFold&utm_source=Youtube
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The AI Agents Full Course 2026 by Simplilearn starts with an introduction to AI Agents, their environments, and the Agentic AI roadmap, before moving into hands-on learning like building AI agents without code. It then explores core technologies including Natural Language Processing, multimodal AI, RAG (Retrieval-Augmented Generation), and Agentic AI workflows. Learners practice through n8n, LangGraph, LangChain, Firebase Studio, MCP, and Claude Code tutorials, alongside comparisons like Gemini CLI vs Claude Code. The course also introduces voice agents, Generative AI agents, DeepSeek R1, Meta’s LLaMA 3.2, and Manus AI. Finally, it covers practical applications and strategies to make money with AI agents, preparing learners for both technical mastery and real-world opportunities.

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

00:00:00 - Introduction to AI Agents Full Course 2026
00:02:15 - AI Agents Tutorial
00:35:58 - Agentic AI Roadmap
00:51:07 - AI Agents and Environments
00:56:29 - How to Build AI Agents Without Code
01:13:47 - Natural Language Processing Tutorial
01:46:36 - n8n Tutorial
01:59:42 - Agentic AI Workflow
03:30:02 - Build AI Agents From Scratch
03:53:44 - Langraph Tutorial
04:08:44 - What Are Gen AI Agents
04:26:50 - How to Build AI Voice Agents
04:49:18 - Langchain Explained
04:59:54 - Multimodal AI
05:05:31 - Build Agentic Rag
06:04:10 - MCP Tutorial
06:26:54 - Claude Code Tutorial for Beginners
06:41:16 - Germini CLI vs Claude Code
06:57:25 - Firebase Studio
07:10:02 - Manus AI
07:22:24 - Introduction to LLM
07:28:24 - Deepseek R1
07:44:29 - Install Deepseek
07:55:42 - Meta New Lama 3.2
08:04:00 - Make Money With AI Agents

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

⏩ Check out the Artificial Intelligence training videos: https://youtube.com/playlist?l....ist=PLEiEAq2VkUULa5a

#generativeAI #AIAgents #AIAgentsTutorial #AI #GenAI #ArtificialIntelligence #simplilearn #2026

➡️ About Professional Certificate Program in Generative AI and Machine Learning

Dive into the future of AI with our Generative AI & Machine Learning course, in collaboration with E&ICT Academy, IIT Guwahati. Learn tools like ChatGPT, OpenAI, Hugging Face, Python, and more. Join masterclasses led by IITG faculty, engage in hands-on projects, and earn Executive Alumni Status.

Key Features:
✅ Program completion certificate from E&ICT Academy, IIT Guwahati
✅ Curriculum delivered in live virtual classes by seasoned industry experts
✅ Opportunity to earn an 'Executive Alumni Status' from E&ICT Academy, IIT Guwahati
✅ Eligibility for a campus immersion program organized at IIT Guwahati
✅ Exclusive hackathons and “ask-me-anything” sessions by IBM
✅ Certificates for IBM courses and industry masterclasses by IBM experts
✅ Practical learning through 25+ hands-on projects and 3 industry-oriented capstone projects
✅ Simplilearn's JobAssist helps you get noticed by top hiring companies

Skills Covered:
✅ Generative AI
✅ Prompt Engineering
✅ Chatbot Development
✅ Supervised and Unsupervised Learning
✅ Model Training and Optimization
✅ Model Evaluation and Validation
✅ Ensemble Methods
✅ Deep Learning
✅ Natural Language Processing
✅ Computer Vision
✅ Reinforcement Learning
✅ Machine Learning Algorithms
✅ Speech Recognition
✅ Statistics

Learning Path:
✅ Program Induction
✅ Programming Fundamentals
✅ Python for Data Science (IBM)
✅ Applied Data Science with Python
✅ Machine Learning
✅ Deep Learning with TensorFlow (IBM)
✅ Deep Learning Specialization
✅ Essentials of Generative AI, Prompt Engineering & ChatGPT
✅ Advanced Generative AI
✅ Capstone

👉 Learn More At: https://www.simplilearn.com/applied-generative-ai-course?utm_campaign=yMot_Z9yjZ8&utm_medium=Description&utm_source=Youtube

Start building AI apps for free with lovable and use code TINA20YT for a 20% discount at https://dub.link/tina-yt
🤖 Want to get ahead in your career using AI? Join the waitlist for my AI Agent Bootcamp: https://www.lonelyoctopus.com/ai-agent-bootcamp

🤝 Business Inquiries: https://tally.so/r/mRDV99

In this video I show you how to build your first AI agent in 26 minutes using no code and n8n.

🖱️Links mentioned in video
========================
n8n Metaprompt: hhttps://docs.google.com/docume....nt/d/1OoWDiwsr9zjCoo

🔗Affiliates
========================
My SQL for data science interviews course (10 full interviews):
https://365datascience.com/lea....rn-sql-for-data-scie

365 Data Science:
https://365datascience.pxf.io/WD0za3 (link for 57% discount for their complete data science training)

Check out StrataScratch for data science interview prep:
https://stratascratch.com/?via=tina

🎥 My filming setup
========================
📷 camera: https://amzn.to/3LHbi7N
🎤 mic: https://amzn.to/3LqoFJb
🔭 tripod: https://amzn.to/3DkjGHe
💡 lights: https://amzn.to/3LmOhqk

⏰Timestamps
========================
00:00 — Intro
00:24 — What is an AI Agent?
02:16 — Quiz 1
02:34 — Building An AI Research/Learning Agent in n8n
15:28 — Agent Guardrails & Error Handling
20:03 — Agent Orchestration
24:15 — Deploying the AI Agent
24:44 — Final Result
26:20 — Quiz 2

📲Socials
========================
instagram: https://www.instagram.com/hellotinah/
linkedin: https://www.linkedin.com/in/tinaw-h/
tiktok: https://www.tiktok.com/@hellotinahuang
discord: https://discord.gg/5mMAtprshX

🎥Other videos you might be interested in
========================
How I consistently study with a full time job:
https://www.youtube.com/watch?v=INymz5VwLmk

How I would learn to code (if I could start over):
https://www.youtube.com/watch?v=MHPGeQD8TvI&t=84s

🐈‍⬛🐈‍⬛About me
========================
Hi, my name is Tina and I'm an ex-Meta data scientist turned internet person!

📧Contact
========================
youtube: youtube comments are by far the best way to get a response from me!
linkedin: https://www.linkedin.com/in/tinaw-h/
email for business inquiries only: [email protected]

========================
Some links are affiliate links and I may receive a small portion of sales price at no cost to you. I really appreciate your support in helping improve this channel! :)

*Note: 1+ Years of Work Experience Recommended to Sign up for Below Programs⬇️
🔥 Purdue Post Graduate Program In AI And Machine Learning: https://www.simplilearn.com/pgp-ai-machine-learning-certification-training-course?utm_campaign=25August2023PythonProgrammingFullCourse2023&utm_medium=Descriptionff&utm_source=youtube
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Python, known for its simplicity and versatility, is a high-level programming language powering diverse applications. With clear syntax and a rich library ecosystem, it's a top choice for web development, data analysis, machine learning, and more. Its broad adoption and community support drive innovation across industries. Today we bring you python programming full course that covers all the important concepts of python. Well suitable for beginners to expert level, this course covers basic concepts to python projects.

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02:00 What is python?
21:57 Writing Python Program
30:47 Variables in Python
31:22 Object References
57:25 Data types in Python
01:29:15 Type conversions
02:02:50 Types of number data types
02:28:25 Python String
02:56:33 Operators in Python
03:22:38 Lists in Python
03:50:04 Methods
04:42:31 For loop syntax
05:12:22 Python Functions
05:51:02 Lambda Functions
06:17:27 Classes and Objects
06:36:17 Inheritance
07:00:39 Python iterator
07:29:32 Scope in Python
07:48:28 Exception Handling
08:18:08 Sorting in Python
09:02:19 Python Projects

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⏩ Check out the Python Programming Videos By Simplilearn: https://youtube.com/playlist?l....ist=PLEiEAq2VkUUJO27

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✅ About AI & Machine Learning Bootcamp (US Only)

This AI and Machine Learning Bootcamp in collaboration with Caltech, will help you advance your career as an AI and ML specialist. The AI and ML Bootcamp includes live classes delivered by industry experts, hands-on labs, Industry-relevant projects, and masterclasses by Caltech professors.

✅ Skills Covered

- Statistics
- Python
- Supervised Learning
- Unsupervised Learning
- Recommendation Systems
- NLP
- Neural Networks
- GANs
- Deep Learning
- Reinforcement Learning
- Speech Recognition
- Ensemble Learning
- Computer Vision

✅ Tools Covered
- Python
- Tensorflow
- Keras
- MatPlotlib
- NLKT
- Scikit Learn
- Django
- Flask
- Open CV

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Following are the topics covered in this tutorial on Agentic AI Course 2026 :

00:00:05 - Introduction to Agentic AI and Multi-Agent Systems
00:01:35 - Quiz Question
00:01:55 - What Is Agentic AI And How It Works
00:02:00 - Generative AI, LLMs, RAG, And AI Workflows
00:02:39 - Practical Projects And Hands-On Learning Approach
00:03:11 - Tools And Frameworks For Building AI Systems
00:03:37 - Career Opportunities And Who Should Learn Agentic AI
00:04:03 - Learning Support, Mentorship, And Career Assistance
00:04:23 - Latest Trends In Generative AI And Agentic AI
00:04:39 - Outro

This video on Agentic AI and Multi-Agent Systems by Simplilearn explains how modern AI systems are moving beyond simple question-answering tools to intelligent systems that can plan tasks, use tools, and complete workflows. In this video, you will learn the fundamentals of Agentic AI, AI agents, multi-agent systems, LLMs, RAG, and workflow automation. The session covers how AI agents are designed, how different AI components work together, and how technologies like LangChain, Crew AI, AutoGen, MCP, and AI automation tools are used. You will also explore practical applications, real-world projects, and enterprise use cases of agentic workflows. This video is useful for developers, product managers, technology professionals, AI enthusiasts, and beginners who want to understand the future of AI systems. By the end of this video, you will understand how intelligent AI workflows are created and how skills in Agentic AI can support future career opportunities.



Related Videos:
✅ 1. https://youtu.be/2R-niMsB0QY?si=uoG8eqArZkZ8M88i
✅ 2. https://youtu.be/2MHjpOHQNNQ?si=hBzT0uI-B6ESuEU4
✅ 3. https://youtu.be/uFTrtMw8cIM?si=2GB6EM9HxTSQGVUL
✅ 4. https://www.youtube.com/live/s....TzbTqjsmIw?si=7VwvBU
✅ 5. https://youtu.be/8ykgB9lKA3M?si=UtUcxTb5grCmo7Fp

There is so much being said about artificial intelligence these days. In order to understand this latest wave of AI, it is important to know how it works and why this moment is different from developments in the past.

WSJ science reporter Eric Niiler joins host Zoe Thomas for the first installment of Tech News Briefing’s special series Artificially Minded. New episodes drop every Monday in April.

0:00 AI basics
2:18 What is machine learning?
4:20 How generative AI like ChatGPT works
6:07 AI in our daily lives
7:45 How people on the street feel about AI
8:38 Why AI is a hot topic right now
9:50 Risks and rules surrounding AI

Tech News Briefing
WSJ’s tech podcast featuring breaking news, scoops and tips on tech innovations and policy debates, plus exclusive interviews with movers and shakers in the industry.

For more episodes of WSJ’s Tech News Briefing: https://link.chtbl.com/WSJTechNewsBriefing

#AI #ArtificialIntelligence #WSJ

Unpacking how large language models work under the hood
Early view of the next chapter for patrons: https://3b1b.co/early-attention
Special thanks to these supporters: https://3b1b.co/lessons/gpt#thanks

To contribute edits to the subtitles, visit https://translate.3blue1brown.com/

Other recommended resources on the topic.

Richard Turner's introduction is one of the best starting places:
https://arxiv.org/pdf/2304.10557.pdf

Coding a GPT with Andrej Karpathy
https://youtu.be/kCc8FmEb1nY

Introduction to self-attention by John Hewitt
https://web.stanford.edu/class..../cs224n/readings/cs2

History of language models by Brit Cruise:
https://youtu.be/OFS90-FX6pg

Paper about examples like the “woman - man” one presented here:
https://arxiv.org/pdf/1301.3781.pdf

------------------

Timestamps

0:00 - Predict, sample, repeat
3:03 - Inside a transformer
6:36 - Chapter layout
7:20 - The premise of Deep Learning
12:27 - Word embeddings
18:25 - Embeddings beyond words
20:22 - Unembedding
22:22 - Softmax with temperature
26:03 - Up next

------------------
These animations are largely made using a custom Python library, manim. See the FAQ comments here:
https://3b1b.co/faq#manim
https://github.com/3b1b/manim
https://github.com/ManimCommunity/manim/

All code for specific videos is visible here:
https://github.com/3b1b/videos/

The music is by Vincent Rubinetti.
https://www.vincentrubinetti.com
https://vincerubinetti.bandcam....p.com/album/the-musi
https://open.spotify.com/album..../1dVyjwS8FBqXhRunaG5

------------------

3blue1brown is a channel about animating math, in all senses of the word animate. If you're reading the bottom of a video description, I'm guessing you're more interested than the average viewer in lessons here. It would mean a lot to me if you chose to stay up to date on new ones, either by subscribing here on YouTube or otherwise following on whichever platform below you check most regularly.

Mailing list: https://3blue1brown.substack.com
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Facebook: https://www.facebook.com/3blue1brown
Patreon: https://patreon.com/3blue1brown
Website: https://www.3blue1brown.com

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.

Game development veteran, creator of libGDX, and 17-year open-source contributor Mario Zechner tells the story of how he ended up building pi, his own minimal, opinionated terminal coding agent.

It started in April 2025 when Peter Steinberger and Armin Ronacher (Flask, Sentry) dragged him into an overnight AI hackathon. Within weeks, Mario was hooked on Claude Code — until he wasn't. There was feature bloat, hidden context injection that changed daily, the infamous terminal flicker, and zero extensibility for power users.

He then surveyed the alternatives — Codex CLI, Amp, OpenCode... Eventually, he came across Terminus — an agent that gives the model nothing but a tmux session and raw keystrokes. If that's enough for the model to perform, what are all those extra features actually doing?

Mario's thesis: we're still in the "messing around and finding out" stage, and coding agents need to become more malleable so developers can experiment faster.

Pi is his answer: four tools (read, write, edit, bash), the shortest system prompt of any major agent, tree-structured sessions, full cost tracking, hot-reloading TypeScript extensions, and nothing injected behind your back. No MCP, no sub-agents, no plan mode — but all of it buildable in minutes through extensions.

The community has already shipped pi-annotate (visual frontend feedback), pi-messenger (a multi-agent chatroom), and someone even got Doom running. On TerminalBench, pi with Claude Opus 4.5 landed right behind Terminus — before it even had compaction.

🔗 LINKS & RESOURCES
pi coding agent: https://pi.dev
Mario Zechner: https://mariozechner.at
Peter Steinberger / OpenClaw: https://github.com/steipete
Armin Ronacher: https://lucumr.pocoo.org
Claude Code: https://docs.anthropic.com/en/docs/claude-code
Aider: https://aider.chat
OpenCode: https://github.com/anthropics/opencode
Amp (Sourcegraph): https://sourcegraph.com/amp
TerminalBench: https://terminalbench.com
Ghostty: https://ghostty.org
Vouch: https://github.com/mitchellh/vouch
libGDX: https://libgdx.com

AI Engineer London is a community meetup for engineers and founders building with AI, covering everything from agent frameworks and RAG pipelines to LLMs in production. Each event features technical talks, live demos, and hands-on networking. This talk was recorded at AI Engineer London #10, hosted by Tessl, in collaboration with AI Engineer London.

AI ENGINEER LONDON
📅 Events: https://lu.ma/aiengineerlondon
💼 LinkedIn: https://linkedin.com/company/a....i-engineer-london-me

📚 MASTRA RESOURCES
Mastra: https://mastra.ai
Learn Mastra in the world's first MCP-Based Course: https://mastra.ai/course
Principles of Building AI Agents (Book): https://mastra.ai/books/principles-of-building-ai-agents
Patterns for Building AI Agents (New Book): https://mastra.ai/books/patterns-of-building-ai-agents

MASTRA?
Mastra is an open-source TypeScript framework designed for building and shipping AI-powered applications and agents with minimal friction. It supports the full lifecycle of agent development—from prototype to production. You can integrate it with frontend and backend stacks (e.g., React, Next.js, Node) or run agents as standalone services. If you're a JavaScript or TypeScript developer looking to build an agentic or AI-powered product without starting from first principles, Mastra provides the scaffolding, tools, and integrations to accelerate that process.

📑 CHAPTERS
00:00 Intro
02:17 The history of coding agents: ChatGPT → Copilot → Aider → Claude Code
04:52 What Claude Code got right — and where it became a spaceship
06:04 Claude Code Drawbacks
09:39 Claude Code Alternatives
11:38 OpenCode's compaction problem and prompt cache busting
12:51 Why LSP feedback mid-edit is a terrible idea
14:26 OpenCode's architecture issues and security vulnerability
16:06 TerminalBench and Terminus
18:13 Mario's Two Theses
19:08 Introducing pi — strip everything, build a minimal extensible core
20:01 The system prompt
21:18 What's not in pi — and what you build instead
22:40 Extensions: custom tools, custom UI, hot reloading
24:00 Community extensions
24:59 Tree-structured sessions, cost tracking, HTML export
25:33 TerminalBench results
25:54 Open source under siege and human verification

Build an AI agent that learns alongside you

In this free, end‑to‑end course, you’ll build a powerful language‑learning AI agent from scratch using Python, LangGraph, OpenAI, Ollama, and MCP. Over this hands‑on tutorial, we’ll create a ReAct‑style agent that sources vocabulary, performs accurate translations, and automatically generates Anki flashcards.

You’ll learn how to:
• Build a ReAct agent using LangGraph
• Connect agents to external tools with MCP
• Combine proprietary and local LLMs (OpenAI & Ollama)
• Design real‑world AI agent workflows

No NLP background required — we explain both the how and the why. By the end of this course, you’ll go from an empty Python project to a fully functional AI assistant you can adapt for your own AI projects.

Download PyCharm for free, the only Python IDE you need to build data models and AI agents: https://jb.gg/ai-agents-course

The full code for this tutorial, as well as the resources used, can be found in this GitHub repo: https://github.com/t-redactyl/....language-learning-ag
You can connect with Jodie, as well as see more of her work, at http://t-redactyl.io

Resources:
https://github.com/eymenefealt....un/all-words-in-all-
https://www.kaggle.com/datasets
https://www.kaggle.com/datasets?tags=13204-NLP
https://archive.ics.uci.edu/datasets
https://huggingface.co/learn/agents-course/

Timestamps
00:00 - Course Intro: What We’ll Build, AI agent tech stack
00:43 - What You’ll Learn: Building an AI Language Learning Agent
01:41 – Who This AI Agent Tutorial Is For (Python Prerequisites)
02:42 – Instructor Introduction: NLP & Data Science Background
03:01 – Why Use PyCharm for AI Agents & Data Science

Environment & Project Setup
03:36 – Installing PyCharm and AI Assistant
05:45 – Creating a Python Project with Virtual Environments

Dataset Selection & Preparation
07:26 – Choosing a Multilingual Vocabulary Dataset
08:46 – Best NLP Datasets: Kaggle & UCI Repositories
10:02 – Cloning and Organizing NLP Datasets in PyCharm

Installing Python & AI Dependencies
13:06 – Installing NLP Libraries: pandas, SpaCy, wordfreq
15:09 – Installing LangChain, LangGraph & MCP Libraries

Data Exploration & Analysis
16:56 – Exploring NLP Data with Jupyter Notebooks
18:01 – Analyzing Vocabulary Size Across Languages
20:45 – Visualizing Word Counts with Pandas Charts
22:08 – Identifying Data Problems in Multilingual Word Lists
24:36 – Introduction to SpaCy for Natural Language Processing

Cleaning the Word Lists
29:02 – Inspecting and Debugging Raw Vocabulary Data
31:48 – Removing Noise: Basic Text Cleaning Techniques
33:06 – Lemmatizing Words with SpaCy Models
35:10 – Using Zipf’s Law Overview to Filter Rare Words
37:26 – Word Frequency Analysis with wordfreq and SpaCy
42:35 - Understand Word Frequencies with wordfreq

Final Dataset Creation
45:22 – Building a Complete NLP Cleaning Pipeline
49:01 – Validating Results with a Spanish Dataset
50:01 – Comparing Raw vs Cleaned NLP Data

ReAct Agent Basics
54:40 – From Clean Data to an AI Agent
55:45 – What Is an AI Agent? Core Concepts
56:19 – Thought-Action-Observation Loop Explained
58:11 – Types of AI Agents

Large Language Models (LLMs) Explained
1:00:56 – How Large Language Models Understand Language
1:01:01 – Word Embeddings & Word2Vec Explained
1:05:24 – Why Word Embeddings Fail Without Context
1:06:05 – Transformers & Self-Attention Explained
1:10:23 – GPT Models and Decoder-Only Architectures

Reasoning Models for AI Agents
1:11:52 – Why Reasoning Models Power AI Agents
1:12:08 – How Reasoning Models Are Trained (Chain-of-Thought)
1:17:04 – When Not to Use Reasoning Models

1:20:24 How to Build a ReAct Agent with LangGraph
1:21:31 Agent State, Memory & Tools Explained
1:23:34 Choosing Between GPT-4 and Open-Source Models
1:26:36 How to Manage OpenAI API Keys Securely

1:30:09 How to Build Custom Tools for LangGraph Agents
1:33:14 Auto-Generating Tool Docstrings with AI
1:35:01 Improving Agent Reliability with System Prompts

1:38:13 Building and Connecting a LangGraph Agent Graph
1:41:21 Running an AI Agent End-to-End
1:43:02 How to Debug AI Agents in PyCharm
1:44:37 Visualizing Agent Execution Graphs

1:47:36 How to Run AI Agents Locally with Ollama
1:49:24 Choosing the Best Open-Source Reasoning Model
1:51:04 Installing and Managing Ollama Models
1:53:19 GPT-4 vs Ollama: Model Comparison for Agents

1:58:12 Switching LangGraph Agents from OpenAI to Ollama
2:00:22 Testing a Fully Local AI Agent

2:11:31 Adding Difficulty-Aware Vocabulary Tools
2:14:01 Handling Ambiguous User Requests in AI Agents
2:19:56 Testing AI Agents with Natural Language Prompts

2:24:48 How to Translate Words Using an LLM Tool
2:27:34 Building a Translation Tool with Ollama
2:32:28 Parsing Structured Output from LLMs

2:37:02 Multi-Step Tool Use in ReAct Agents
2:39:34 Handling Errors and Non-Determinism in AI Agents

2:41:40 What Is MCP (Model Context Protocol)?
2:42:13 Connecting AI Agents to External Tools with MCP

🔹Download the free guide from HubSpot on Your 2026 Guide to
AI Agents: https://clickhubspot.com/70c662
🔹Code in this video (available inside Skool): https://www.skool.com/ai-agent....-builders/classroom/

No frameworks. No vibe coding. No third-party tools. In this beginner course I show you how to build a fully functioning AI agent from scratch in pure Python — so you actually understand what's happening under the hood, not just how to prompt a tool.

Want to LEARN how to build useful AI agents that actually get things done? Go here: https://aiagentbuilders.co/yt

🚀 Tools I Use
Get 10% off with code techwithtim
Openclaw setup: https://www.hostinger.com/techwithtim
VPS setup: https://www.hostinger.com/techwithtim10
Wispr Flow (Best AI Dictation): https://ref.wisprflow.ai/TechWithTim-aug26

🎞 Video Resources 🎞
HubSpot Free Resource - https://clickhubspot.com/70c662
Skool Lead Magnet: https://www.aiagentbuilders.co..../yt-resources/pure-p

⏳ Timestamps ⏳
00:00 | Overview
00:30 | What is an Agent
02:52 | The Three Steps
03:21 I Step 1 - Calling an API
11:50 | HubSpot Free Resource
13:15 | Step 2 - Conversation History
17:08 | Step 3 - Tool Calling
28:09 | Full Agent Build

Hashtags
#Python #AIAgents #BuildWithAI

UAE Media License Number: 3635141

E - mail = [email protected]
@[email protected] @Yellowy Coder @Codiscos @freeCodeCamp.org @Cody'sLab @xCodeh @Machine Learnia @DeepLearningAI @DeepLearning.TV @Deep Learning School @DeepLearning_by_PhDScholar @MrBeast @Coder Coder @노마드 코더 Nomad Coders @GitHub @GitHub Training & Guides @Machine Learning with Phil @Smitha Kolan - Machine Learning Engineer
@Iggy Azalea @Self Learner @Conoce conmigo@Code Logic




While there have been recent advances in few-shot image stylization, these methods fail to capture stylistic details that are obvious to humans. Details such as the shape of the eyes, the boldness of the lines, are especially difficult for a model to learn, especially so under a limited data setting. In this work, we aim to perform one-shot image stylization that gets the details right. Given a reference style image, we approximate paired real data using GAN inversion and finetune a pretrained using that approximate paired data. We then encourage the GAN to generalize so that the learned style can be applied to all other images.

Hands On Review of the Kinefinity Mavo Edge 8K Cinema Camera. In this in-depth Kinefinity Mavo Edge 8K Review I tell you everything you need to know before purchasing this budget-friendly cinema camera.

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In this Kinefinity Mavo Edge 8K review, I talk about how I used this cinema camera in a production situation while shooting a short film that I wrote and produced.


Chapters:
00:00 Welcome to my Hands on Review of the Kinefinity Mavo Edge 8K
01:15 Camera Specs
02:59 Footage + Color Science
03:48 Low Light Performance
06:25 Dynamic Range
07:26 Codecs
08:13 Internal ND
09:10 Build Quality
10:08 Lens Mounts
11:04 User Experience
11:56 Black Shading
12:31 Audio Inputs
13:22 Rear & Front IO
13:55 Accessories
16:05 Recording Media
17:50 Power Options
20:48 Pros & Cons
23:02 The Final Verdict


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DISCLAIMERS: Driven Films is a participant in the Amazon Services LLC Associates Program, an affiliate advertising program designed to provide a means for us to earn fees by linking to Amazon.com and affiliated sites. This video was not paid for by outside persons or manufacturers. The content of this video and my opinions were not reviewed or paid for by any outside persons.

Code link: https://github.com/krishnaik06..../Agentic-LanggraphCr
LangGraph is built for developers who want to build powerful, adaptable AI agents. Developers choose LangGraph for:

Reliability and controllability. Steer agent actions with moderation checks and human-in-the-loop approvals. LangGraph persists context for long-running workflows, keeping your agents on course.
Low-level and extensible. Build custom agents with fully descriptive, low-level primitives free from rigid abstractions that limit customization. Design scalable multi-agent systems, with each agent serving a specific role tailored to your use case.
First-class streaming support. With token-by-token streaming and streaming of intermediate steps, LangGraph gives users clear visibility into agent reasoning and actions as they unfold in real time.
Learn LangGraph basics¶
To get acquainted with LangGraph's key concepts and features, complete the following LangGraph basics tutorials series:

Build a basic chatbot
Add tools
Add memory
Add human-in-the-loop controls
Customize state
Time travel

Timestamp:

00:00:00 Introduction And Agenda
00:03:37 Langgraph Projects Structure
00:11:15 Building Blocks Of LAnggraph
00:23:47 Building a Basic Chatbot
00:49:05 Building Chatbot With Tools
01:18:37 ReACT Agent Architecture
01:26:22 Adding Memory In Langgraph
01:35:00 Streaming In Langgraph
01:44:58 Human Feedback In the loop
01:52:42 MCP Server Scratch Implementation

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In this video on "What is Agentic AI", we explain the meaning of Agentic AI through the simple story of Emma, a small online business owner. You will see how traditional AI can provide useful ideas and answers, while Agentic AI can understand a goal, create a plan, use tools, take actions, and improve results step by step.

Emma initially uses a normal AI chatbot to get ideas for increasing her weekend sales. Although the AI suggests discounts, social media promotions, and email campaigns, Emma still has to complete every task herself. With Agentic AI, the experience changes. The AI reviews sales information, studies customer queries, identifies popular products, creates campaign plans, writes email drafts, prepares social media captions, recommends posting times, and tracks campaign performance. Simplilearn’s AI certification programs can help learners understand modern AI tools, prompt engineering, AI automation, and the safe use of AI systems in real-world situations.

Watch the complete video to understand how Agentic AI helps people move from ideas to action.

Topics covered:
1. What is Agentic AI?
2. What are AI agents
3. Traditional AI vs Agentic AI
6. AI guardrails
7. Business automation
8. Generative AI and the future of work.

Related Videos:
✅ 1. https://youtu.be/2MHjpOHQNNQ?si=a8lofxiO2g8ONByK
✅ 2. https://youtu.be/DfFoMhfYNLI?si=gxT5Dy5rUZ7OXh1l
✅ 3. https://youtu.be/ia7M-lJiTMY?si=fu8XSxaAjcLIgIaH
✅ 4. https://youtu.be/phkxmDo0TlQ?si=z900jRlscbwFu6iA
✅ 5. https://youtu.be/2R-niMsB0QY?si=UaM46S85L7vVmVoU

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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
✅ Earn a course completion certificate hosted on the Microsoft Learn portal for Microsoft courses
✅ Experience flexible learning with session recordings
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✅ Gain 24/7 access to Simplilearn’s Learning Management System (LMS)

Skills Covered
✅ Prompt Engineering
✅ Agentic Frameworks
✅ AI Agents
✅ LangChain for Workflow Design
✅ Retrieval Augmented Generation RAG
✅ 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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