Tutorials – Page 229 – NulledCodeSolutions.online

May 21, 2026

AI Engineer Production Track: Deploy LLMs & Agents at Scale

Deploy AI to AWS, GCP, Azure, Vercel with MLOps, Bedrock, SageMaker, RAG, Agents, MCP: scalable, secure and observable..

What you’ll learn
Deploy SaaS LLM apps to production on Vercel, AWS, Azure, and GCP, using Clerk
Design cloud architectures with Lambda, S3, CloudFront, SQS, Route 53, App Runner and API Gateway
Integrate with Amazon Bedrock and SageMaker, and build with GPT-5, Claude 4, OSS, AWS Nova and HuggingFace
Rollout to Dev, Test and Prod automatically with Terraform and ship continuously via GitHub Actions
Deliver enterprise-grade AI solutions that are scalable, secure, monitored, explainable, observable, and controlled with guardrails.
Create Multi-Agent systems and Agentic Loops with Amazon Bedrock AgentCore and Stands Agents

Requirements
While it’s ideal if you can code in Python and have some experience working with LLMs, this course is designed for a very wide audience, regardless of background. I’ve included a whole folder of self-study labs that cover foundational technical and programming skills. If you’re new to coding, there’s only one requirement: plenty of patience!
The course runs best if you have a small budget for APIs and Cloud Providers of a few dollars. But we monitor expenses at every point, and it’s always a personal choice.

Description
This is the course that more of my students have asked for than any other course – put together.
One student called it
"The missing course in AI."
This course is for
Entrepreneurs
Enterprise engineers
.and everyone in between.
It’s not just about RAG – although we’ll work with RAG.
It’s not just about Agents – but there will be many Agents.
It’s not just about MCP – but yes, there will be plenty of MCP too.
This course is about
RAG, Agents, MCP, and so much more. deployed to production.
Live.
Enterprise-grade.
Scalable, resilient, secure, monitored – and explained.
You’ll ship real-world, production-grade AI with LLMs and agents across Vercel, AWS, GCP, and Azure, going deepest on AWS.
Across four weeks you’ll take four products to production
Week 1
You’ll launch a Next.js SaaS product on Vercel and AWS,
with AWS App Runner and Clerk for user management and subscriptions.
Week 2
You’ll become an AI platform engineer on AWS,
deploying serverless infrastructure using
Lambda, Bedrock, API Gateway, S3, CloudFront, Route 53
Write Infrastructure as Code with Terraform
Set up CI/CD pipelines with GitHub Actions
– for hands-free deployments and one-click promotions.
Week 3
You’ll gain broad industry skills for GenAI in production
Deploy a Cyber Security Analyst agent with MCP to Azure & GCP
Stand up SageMaker inference
Build data ingest to S3 vectors
Deploy a Researcher Agent using OpenAI OSS models on Bedrock + MCP
Week 4
You’ll go fully agentic in production
Architect multi-agent systems with
Aurora Serverless, Lambda, SQS
JWT-authenticated CloudFront frontends
LangFuse observability
Overview of AWS Agent Core
By the end, you’ll know how to
Pick the right architecture
Lock down security
Monitor costs
Deliver continuous updates
Everything needed to run scalable, reliable AI apps in production.
Course sections (Weeks & Projects)
Week 1
SaaS App Live in Production with Vercel, AWS, Next.js, Clerk, App Runner
Project: SaaS Healthcare App
Week 2
AI Platform Engineering on AWS with Bedrock, Lambda, API Gateway, Terraform, CI/CD
Project: Digital Twin Mk II
Week 3
Gen AI in Production with Azure, GCP, AWS SageMaker, S3 Vectors, MCP
Project: Cybersecurity Analyst
Week 4
Agentic AI in Production: Build and deploy a Multi-Agent System on AWS (Aurora Serverless, Lambda, SQS),
with LangFuse and Bedrock AgentCore
Capstone Project: SaaS Financial Planner

Who this course is for
Well, perhaps I’m biased, but I’d say: anyone and everyone! If you’re fascinated in the potential of Agents and hungry to have the skills to create powerful Agentic AI – then you’ve come to the right place. While it’s most suited to those with programming experience, I’ve designed the course to work for all backgrounds.

Welcome to – Check it Every Days
If you have any troubles with downloading, PM me

Happy Learning!!

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May 21, 2026

AI Engineer Production Track Deploy LLMs and Agents at Scale

12.84 GB | 11min 17s | mp4 | 1920X1080 | 16:9
Genre:eLearning |Language:English

Files Included :
1 Day 1 – Instant AI Deployment Your First Production App on Vercel in Minutes.mp4 (141.63 MB)
2 Day 1 – From Zero to Live Deploying Your First AI-Powered SaaS on Vercel.mp4 (40.31 MB)
3 Day 1 – From AI Concepts to Cloud Deployment Navigating the DevOps Landscape.mp4 (49.48 MB)
4 Day 1 – Course Overview Building Production AI Systems Across 4 Weeks.mp4 (54.9 MB)
5 Day 1 – Deploy Your First Live AI App with OpenAI and Vercel Integration.mp4 (77.22 MB)
6 Day 1 – Managing API Costs and Environment Setup for Production AI Systems.mp4 (85.9 MB)
7 Day 1 – Course Expectations and Community Support for Production AI.mp4 (94.23 MB)
8 Day 2 – Building Full-Stack AI Apps Frontend-Backend Architecture for LLMs.mp4 (53.73 MB)
9 Day 2 – Building Full-Stack AI Apps with React, FastAPI, and NextJS.mp4 (215.24 MB)
10 Day 2 – Building Your First Full-Stack AI SaaS with NextJS and FastAPI.mp4 (63.82 MB)
11 Day 2 – Building Your First FastAPI Backend for Production LLM Deployment.mp4 (94.72 MB)
13 Day 2 – Adding Real-Time Streaming and Professional UI to Your LLM App.mp4 (129.19 MB)
14 Day 3 – Adding User Authentication to Your Production AI Application.mp4 (206.15 MB)
15 Day 3 – Adding User Authentication to Production AI Apps with Clerk.mp4 (68.73 MB)
16 Day 3 – Adding Subscription Billing to Your Production AI SaaS Application.mp4 (148.56 MB)
17 Day 3 – Adding Authentication and Billing to Production AI Applications.mp4 (133.4 MB)
18 Day 4 – Building Your First Commercial AI App From Prototype to Business.mp4 (121.72 MB)
19 Day 4 – Building Healthcare AI Apps with FastAPI and Structured Prompts.mp4 (93.26 MB)
20 Day 4 – Deploying Your Complete AI Healthcare App to Production on Vercel.mp4 (26.38 MB)
21 Day 4 – Building a Production Healthcare AI SaaS with Streaming LLMs.mp4 (91.96 MB)
22 Day 5 – AWS Setup and IAM for Production AI Your First Cloud Deployment.mp4 (153.83 MB)
23 Day 5 – Setting Up AWS Cost Monitoring for Production AI Deployments.mp4 (223.99 MB)
24 Day 5 – Setting Up Secure IAM Users for Production AI Deployments on AWS.mp4 (140.81 MB)
25 Day 5 – Containerizing AI Apps with Docker for Cloud Deployment.mp4 (156.84 MB)
26 Day 5 – Migrating Your AI App from Vercel to AWS for Production Scale.mp4 (42.1 MB)
27 Day 5 – Containerizing Your AI App Docker Images for Production Deployment.mp4 (109.57 MB)
28 Day 5 – Deploying Dockerized AI Apps to AWS with ECR and App Runner.mp4 (148.91 MB)
29 Day 5 – Deploying Your AI App Live on AWS App Runner with Auto-Scaling.mp4 (38.31 MB)
30 Day 5 – From Vercel to AWS Deploying Production LLM Apps at Scale.mp4 (95.7 MB)
31 Day 1 – AWS Foundations for Production AI From Console to Infrastructure.mp4 (187.86 MB)
32 Day 1 – Cloud Deployment Architectures for Production AI Applications.mp4 (69.95 MB)
33 Day 1 – AWS Cloud Components for Production AI S3, Lambda, and Bedrock.mp4 (50.09 MB)
34 Day 1 – Building Your Digital Twin AWS Lambda + Bedrock Architecture Setup.mp4 (205.34 MB)
36 Day 1 – Building Your First Full-Stack AI App with FastAPI and React.mp4 (48.46 MB)
37 Day 1 – Building Conversational Memory for Production AI Chat Applications.mp4 (66.94 MB)
38 Day 2 – Building Production-Ready AI Agents with AWS Lambda and S3.mp4 (173.3 MB)
39 Day 2 – Migrating AI Chat Apps from Local Storage to AWS S3 and Lambda.mp4 (62.86 MB)
40 Day 2 – Deploying Your First Production LLM API on AWS Lambda.mp4 (70 MB)
41 Day 2 – Configuring AWS Lambda and S3 for Production LLM Memory Storage.mp4 (58.27 MB)
42 Day 2 – Setting Up S3 Buckets and API Gateway for Production AI Apps.mp4 (57.54 MB)
43 Day 2 – Deploying AI Frontend Through CloudFront for Global Distribution.mp4 (68.22 MB)
44 Day 2 – Testing Your Live AI Agent and Configuring CORS for Production.mp4 (76.12 MB)
45 Day 3 – Setting Up Amazon Bedrock for Production LLM Deployment on AWS.mp4 (145.05 MB)
47 Day 3 – Deploying Bedrock LLMs to AWS Lambda and Testing Production APIs.mp4 (63.43 MB)
48 Day 3 – Monitoring Production AI with CloudWatch and Bedrock Metrics.mp4 (107.82 MB)
49 Day 4 – Infrastructure as Code for AI Deploying LLM Apps with Terraform.mp4 (187.37 MB)
50 Day 4 – Infrastructure as Code Automating AI Deployments with Terraform.mp4 (143.52 MB)
51 Day 4 – Automating AI Deployments with Terraform and Shell Scripts.mp4 (192.86 MB)
52 Day 4 – Automating Full-Stack AI Deployment with Terraform and AWS.mp4 (50.78 MB)
53 Day 4 – Multi-Environment AI Deployments Dev, Test, and Production Setup.mp4 (182.26 MB)
54 Day 4 – Testing Production AI Deployments and Terraform Cleanup Workflows.mp4 (127.97 MB)
55 Day 5 – Automating AI Infrastructure Deployments with GitHub Actions CI CD.mp4 (160.19 MB)
56 Day 5 – Setting Up Git and GitHub Actions for AI Production Deployments.mp4 (152.07 MB)
57 Day 5 – Setting Up GitHub Actions for Automated AI Model Deployment.mp4 (111.55 MB)
58 Day 5 – Setting Up GitHub Actions for Automated AI Infrastructure Deployment.mp4 (108.67 MB)
59 Day 5 – Setting Up GitHub Actions for Automated AI Agent Deployments.mp4 (192.65 MB)
60 Day 5 – Live CI CD Pipeline Deploy From Git Push to Production AI Agent.mp4 (237.16 MB)
61 Day 5 – Automated CI CD Pipelines for Production AI Apps with Git Deploy.mp4 (163.44 MB)
62 Day 5 – Resource Management and Cost Control for Production AI Systems.mp4 (184.63 MB)
63 Day 1 – Multi-Cloud AI Deployment Azure, GCP & Cybersecurity Agent Setup.mp4 (289.25 MB)
64 Day 1 – Building AI Security Agents with MCP Servers and Semgrep Integration.mp4 (58.59 MB)
65 Day 1 – Containerizing AI Agents with Docker for Cloud Deployment.mp4 (75.91 MB)
67 Day 1 – Deploying AI Apps to Azure with Terraform Infrastructure as Code.mp4 (73.69 MB)
68 Day 1 – Deploying AI Agents with MCP Servers to Azure Container Apps.mp4 (73.4 MB)
69 Day 2 – Setting Up GCP Infrastructure for Production AI Agent Deployment.mp4 (134.35 MB)
70 Day 2 – Setting Up Google Cloud CLI for Production AI Container Deployment.mp4 (13.59 MB)
71 Day 2 – Deploying AI Agents to GCP Cloud Run with Terraform Infrastructure.mp4 (34.94 MB)
72 Day 2 – Deploying AI Agents Across GCP and Azure with Container Services.mp4 (50.81 MB)
73 Day 3 – Building ALEX Multi-Agent Financial AI System on AWS Infrastructure.mp4 (93.75 MB)
74 Day 3 – Setting Up AWS Permissions and SageMaker for Production AI Agents.mp4 (143.93 MB)
75 Day 3 – SageMaker vs Bedrock Deploying Custom AI Models in Production.mp4 (84.56 MB)
76 Day 3 – Deploying SageMaker Embedding Models for Production RAG Systems.mp4 (191.44 MB)
77 Day 3 – Exploring SageMaker AI’s Full Platform for Production ML Workflows.mp4 (36.67 MB)
78 Day 4 – Building Vector Data Pipelines with SageMaker and S3 for AI Memory.mp4 (145.7 MB)
79 Day 4 – Building Cost-Effective Vector Storage with S3 and Lambda Ingestion.mp4 (40.85 MB)
80 Day 4 – Setting Up Secure AI Ingestion Pipelines with Terraform and AWS.mp4 (47.9 MB)
81 Day 4 – Testing Your AWS Lambda Vector Ingest Pipeline End-to-End.mp4 (75.93 MB)
82 Day 5 – Building AI Research Agents with MCP Servers and Data Pipelines.mp4 (94.44 MB)
83 Day 5 – Building AI Research Agents with Bedrock and OpenAI SDK on AWS.mp4 (91.96 MB)
84 Day 5 – Deploying AI Research Agents with Docker, ECR, and App Runner.mp4 (75.52 MB)
85 Day 5 – Testing End-to-End AI Agent Workflows from Research to Vector Storage.mp4 (48.49 MB)
86 Day 5 – Automating AI Agent Workflows with AWS EventBridge Scheduling.mp4 (106.23 MB)
87 Day 5 – Week 3 Wrap-Up Assignment Options & Production AI Next Steps.mp4 (315.82 MB)
88 Day 1 – Multi-Agent vs Single-Agent Architectures for Production AI Systems.mp4 (124.15 MB)
89 Day 1 – Building Multi-Agent Financial AI Database Architecture & AWS Setup.mp4 (48.25 MB)
91 Day 1 – Setting Up Aurora Serverless Database for Multi-Agent AI Systems.mp4 (123.36 MB)
92 Day 1 – Setting Up Aurora Database Infrastructure for Production AI Apps.mp4 (47.64 MB)
93 Day 1 – Setting Up Production Database Architecture for AI Agent Systems.mp4 (68.78 MB)
94 Day 2 – Building Multi-Agent Financial AI Systems with Context Engineering.mp4 (112.85 MB)
95 Day 2 – Setting Up AWS Bedrock Models and Enterprise APIs for AI Agents.mp4 (92.9 MB)
96 Day 2 – Exploring Multi-Agent Architecture Tools and Structured Outputs.mp4 (38.9 MB)
97 Day 2 – Building Multi-Agent Financial Systems Code Review and Architecture.mp4 (98.68 MB)
98 Day 2 – Testing Multi-Agent Systems Locally Before Lambda Deployment.mp4 (81.39 MB)
99 Day 2 – Packaging and Deploying Multi-Agent AI Systems to AWS Lambda.mp4 (88.95 MB)
100 Day 2 – End-to-End Testing of Multi-Agent Systems on AWS Lambda.mp4 (76.56 MB)
101 Day 3 – Building the Frontend for Your Production AI Agent System.mp4 (127.9 MB)
102 Day 3 – Running Full-Stack AI Apps Locally Before Production Deployment.mp4 (52.95 MB)
103 Day 3 – When AI Code Generation Works vs Fails in Production Apps.mp4 (52.18 MB)
105 Day 3 – Testing Your Multi-Agent Financial AI System Live in Production.mp4 (111.42 MB)
106 Day 4 – Enterprise-Grade AI Monitoring, Security & Observability at Scale.mp4 (159.19 MB)
108 Day 4 – Monitoring AI Agents in Production with CloudWatch and Dashboards.mp4 (266.65 MB)
109 Day 4 – Monitoring AI Systems and Building Guardrails for Production Agents.mp4 (275.14 MB)
110 Day 4 – Advanced LLM Observability with Langfuse and Production Guardrails.mp4 (167.11 MB)
111 Day 4 – LLM-as-a-Judge Pattern with Langfuse Observability in Production.mp4 (197.12 MB)
112 Day 4 – Real-Time Agent Monitoring and the Security Risks of Production AI.mp4 (290.16 MB)
113 Day 4 – Securing AI Agents Against Prompt Injection in Production Systems.mp4 (44.51 MB)
114 Day 4 – Capstone Assignment Taking Your AI Financial Agent to Market.mp4 (118 MB)
115 Day 5 – Enterprise AI Guardrails and Wrapping Your Production Agent System.mp4 (113.88 MB)
116 Day 5 – Agent Platforms vs Custom Deployment When to Use Managed Solutions.mp4 (70.09 MB)
117 Day 5 – Building Production AI Agents with Amazon Bedrock AgentCore.mp4 (396.21 MB)
118 Day 5 – Setting Up AWS Bedrock Agent Core for Production AI Deployments.mp4 (164.32 MB)
119 Day 5 – Building and Deploying Your First AI Agent to AWS in Minutes.mp4 (47.9 MB)
120 Day 5 – Building Production AI Agents with Loop-Based Reasoning Systems.mp4 (57.28 MB)
121 Day 5 – Adding Code Execution Tools and Observability to AWS Bedrock Agents.mp4 (70.76 MB)
122 Day 5 – Course Wrap-Up From Zero to Production AI Expert in 4 Weeks.mp4 (158.64 MB)]
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May 21, 2026

AI Engineer Core Track: LLM Engineering, RAG, QLoRA, Agents

AI Engineer Core Track: LLM Engineering, RAG, QLoRA, Agents
2026-02-04
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Language: English (US) | Size: 24.53 GB | Duration: 33h 24m

Become an LLM Engineer in 8 weeks: Build and deploy 8 LLM apps, mastering Generative AI, RAG, LoRA and AI Agents.

What you’ll learn
Project 1: Make AI-powered brochure generator that scrapes and navigates company websites intelligently.
Project 2: Build Multi-modal customer support agent for an airline with UI and function-calling.
Project 3: Develop Tool that creates meeting minutes and action items from audio using both open- and closed-source models.
Project 4: Make AI that converts Python code to optimized C++, boosting performance by 60,000x!
Project 5: Build AI knowledge-worker using RAG to become an expert on all company-related matters.
Project 6: Capstone Part A – Predict product prices from short descriptions using Frontier models.
Project 7: Capstone Part B – Execute Fine-tuned open-source model to compete with Frontier in price prediction.
Project 8: Capstone Part C – Build Autonomous multi agent system collaborating with models to spot deals and notify you of special bargains.
Compare and contrast the latest techniques for improving the performance of your LLM solution, such as RAG, fine-tuning and agentic workflows
Weigh up the leading 10 frontier and 10 open-source LLMs, and be able to select the best choice for a given task

Requirements
Familiarity with Python. This course will not cover Python basics and is completed in Python.
A PC with an internet connection is required. Either Mac (Linux) or Windows.
We recommend that you allocate around $5 for API costs to work with frontier models. However, you can complete the course using open-source models if you prefer.

Description
Mastering Generative AI and LLMs: An 8-Week Hands-On JourneyAccelerate your career in AI with practical, real-world projects led by industry veteran Ed Donner. Build advanced Generative AI products, experiment with over 20 groundbreaking models, and master state-of-the-art techniques like RAG, QLoRA, and Agents.What you’ll learn Build advanced Generative AI products using cutting-edge models and frameworks. Experiment with over 20 groundbreaking AI models, including Frontier and Open-Source models. Develop proficiency with platforms like HuggingFace, LangChain, and Gradio. Implement state-of-the-art techniques such as RAG (Retrieval-Augmented Generation), QLoRA fine-tuning, and Agents. Create real-world AI applications, including: A multi-modal customer support assistant that interacts with text, sound, and images. An AI knowledge worker that can answer any question about a company based on its shared drive. An AI programmer that optimizes software, achieving performance improvements of over 60,000 times. An ecommerce application that accurately predicts prices of unseen products. Transition from inference to training, fine-tuning both Frontier and Open-Source models. Deploy AI products to production with polished user interfaces and advanced capabilities. Level up your AI and LLM engineering skills to be at the forefront of the industry.About the InstructorI’m Ed Donner, an entrepreneur and leader in AI and technology with over 20 years of experience. I’ve co-founded and sold my own AI startup, started a second one, and led teams in top-tier financial institutions and startups around the world. I’m passionate about bringing others into this exciting field and helping them become experts at the forefront of the industry.Projectsroject 1: AI-powered brochure generator that scrapes and navigates company websites intelligently.Project 2: Multi-modal customer support agent for an airline with UI and function-calling.Project 3: Tool that creates meeting minutes and action items from audio using both open- and closed-source models.Project 4: AI that converts Python code to optimized C++, boosting performance by 60,000x!Project 5: AI knowledge-worker using RAG to become an expert on all company-related matters.Project 6: Capstone Part A – Predict product prices from short descriptions using Frontier models.Project 7: Capstone Part B – Fine-tuned open-source model to compete with Frontier in price prediction.Project 8: Capstone Part C – Autonomous agent system collaborating with models to spot deals and notify you of special bargains.Why This Course? Hands-On Learning: The best way to learn is by doing. You’ll engage in practical exercises, building real-world AI applications that deliver stunning results. Cutting-Edge Techniques: Stay ahead of the curve by learning the latest frameworks and techniques, including RAG, QLoRA, and Agents. Accessible Content: Designed for learners at all levels. Step-by-step instructions, practical exercises, cheat sheets, and plenty of resources are provided. No Advanced Math Required: The course focuses on practical application. No calculus or linear algebra is needed to master LLM engineering.Course StructureWeek 1: Foundations and First Projects Dive into the fundamentals of Transformers. Experiment with six leading Frontier Models. Build your first business Gen AI product that scrapes the web, makes decisions, and creates formatted sales brochures.Week 2: Frontier APIs and Customer Service Chatbots Explore Frontier APIs and interact with three leading models. Develop a customer service chatbot with a sharp UI that can interact with text, images, audio, and utilize tools or agents.Week 3: Embracing Open-Source Models Discover the world of Open-Source models using HuggingFace. Tackle 10 common Gen AI use cases, from translation to image generation. Build a product to generate meeting minutes and action items from recordings.Week 4: LLM Selection and Code Generation Understand the differences between LLMs and how to select the best one for your business tasks. Use LLMs to generate code and build a product that translates code from Python to C++, achieving performance improvements of over 60,000 times.Week 5: Retrieval-Augmented Generation (RAG) Master RAG to improve the accuracy of your solutions. Become proficient with vector embeddings and explore vectors in popular open-source vector datastores. Build a full business solution similar to real products on the market today.Week 6: Transitioning to Training Move from inference to training. Fine-tune a Frontier model to solve a real business problem. Build your own specialized model, marking a significant milestone in your AI journey.Week 7: Advanced Training Techniques Dive into advanced training techniques like QLoRA fine-tuning. Train an open-source model to outperform Frontier models for specific tasks. Tackle challenging projects that push your skills to the next level.Week 8: Deployment and Finalization Deploy your commercial product to production with a polished UI. Enhance capabilities using Agents. Deliver your first productionized, agentized, fine-tuned LLM model. Celebrate your mastery of AI and LLM engineering, ready for a new phase in your career.

Who this course is for:
Aspiring AI engineers and data scientists eager to break into the field of Generative AI and LLMs., Professionals looking to upskill and stay competitive in the rapidly evolving AI landscape., Developers interested in building advanced AI applications with practical, hands-on experience., Individuals seeking a career transition or aiming to enhance productivity through LLM-built frameworks.

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May 21, 2026

AI Engineer Core Track LLM Engineering RAG QLoRA Agents 2026

24.41 GB | 14min 8s | mkv | 1920X1080 | 16:9
Genre:eLearning |Language:English

Files Included :
1-Day 1 Running Your First LLM Locally with Ollama and Open Source Models.mkv (169.39 MB)
2-Day 1 Spanish Tutor Demo with Open Source Models Course Overview.mkv (257.43 MB)
3-Day 1 Setting Up Your LLM Development Environment with Cursor and UV.mkv (153.5 MB)
4-Day 1 Setting Up Your PC Development Environment with Git and Cursor.mkv (145.46 MB)
5-Day 1 Mac Setup Installing Git Cloning the Repo and Cursor IDE.mkv (162.96 MB)
6-Day 1 Installing UV and Setting Up Your Cursor Development Environment.mkv (153.13 MB)
7-Day 1 Setting Up Your OpenAI API Key and Environment Variables.mkv (153.35 MB)
8-Day 1 Installing Cursor Extensions and Setting Up Your Jupyter Notebook.mkv (122.53 MB)
9-Day 1 Running Your First OpenAI API Call and System vs User Prompts.mkv (151.2 MB)
10-Day 1 Building a Website Summarizer with OpenAI Chat Completions API.mkv (140.02 MB)
11-Day 1 Hands On Exercise Building Your First OpenAI API Call from Scratch.mkv (112.89 MB)
12-Day 2 LLM Engineering Building Blocks Models Tools Techniques.mkv (162.18 MB)
13-Day 2 Your 8 Week Journey From Chat Completions API to LLM Engineer.mkv (14.76 MB)
14-Day 2 Frontier Models OpenAI GPT Claude Gemini Grok Compared.mkv (35.83 MB)
15-Day 2 Open Source LLMs LLaMA Mistral DeepSeek and Ollama.mkv (53.8 MB)
16-Day 2 Chat Completions API HTTP Endpoints vs OpenAI Python Client.mkv (77.59 MB)
17-Day 2 Using the OpenAI Python Client with Multiple LLM Providers.mkv (93.69 MB)
18-Day 2 Running Ollama Locally with OpenAI Compatible Endpoints.mkv (78.73 MB)
19-Day 3 Base Chat and Reasoning Models Understanding LLM Types.mkv (102.05 MB)
20-Day 3 Frontier Models GPT Claude Gemini Their Strengths and Pitfalls.mkv (53.01 MB)
21-Day 3 Testing ChatGPT 5 and Frontier LLMs Through the Web UI.mkv (104.1 MB)
22-Day 3 Testing Claude Gemini Grok DeepSeek with ChatGPT Deep Research.mkv (57.15 MB)
23-Day 3 Agentic AI in Action Deep Research Claude Code and Agent Mode.mkv (186.37 MB)
24-Day 3 Frontier Models Showdown Building an LLM Competition Game.mkv (153.99 MB)
25-Day 4 Understanding Transformers The Architecture Behind GPT and LLMs.mkv (233.53 MB)
26-Day 4 From LSTMs to Transformers Attention Emergent Intelligence Agentic A.mkv (77.95 MB)
27-Day 4 Parameters From Millions to Trillions in GPT LLaMA DeepSeek.mkv (23.39 MB)
28-Day 4 What Are Tokens From Characters to GPT s Tokenizer.mkv (20.96 MB)
29-Day 4 Understanding Tokenization How GPT Breaks Down Text into Tokens.mkv (28.52 MB)
30-Day 4 Tokenizing with tiktoken and Understanding the Illusion of Memory.mkv (80.85 MB)
31-Day 4 Context Windows API Costs and Token Limits in LLMs.mkv (185.01 MB)
32-Day 5 Building a Sales Brochure Generator with OpenAI Chat Completions API.mkv (120.92 MB)
33-Day 5 Building JSON Prompts and Using OpenAI s Chat Completions API.mkv (142.19 MB)
34-Day 5 Chaining GPT Calls Building an AI Company Brochure Generator.mkv (73.3 MB)
35-Day 5 Building a Brochure Generator with GPT 4 and Streaming Results.mkv (150.07 MB)
36-Day 5 Business Applications Challenges Building Your AI Tutor.mkv (144.68 MB)
37-Day 1 Connecting to Multiple Frontier Models with APIs OpenAI Claude Gemini.mkv (158.11 MB)
38-Day 1 Testing GPT 5 Models with Reasoning Effort and Scaling Puzzles.mkv (88.84 MB)
39-Day 1 Testing Claude GPT 5 Gemini DeepSeek on Brain Teasers.mkv (64.39 MB)
40-Day 1 Local Models with Ollama Native APIs and OpenRouter Integration.mkv (58.28 MB)
41-Day 1 LangChain vs LiteLLM Choosing the Right LLM Framework.mkv (154.79 MB)
42-Day 1 LLM vs LLM Building Multi Model Conversations with OpenAI Claude.mkv (151.29 MB)
43-Day 2 Building Data Science UIs with Gradio No Front End Skills Required.mkv (156.99 MB)
44-Day 2 Building Your First Gradio Interface with Callbacks and Sharing.mkv (154.51 MB)
45-Day 2 Building Gradio Interfaces with Authentication and GPT Integration.mkv (103.1 MB)
46-Day 2 Markdown Responses and Streaming with Gradio and OpenAI.mkv (129.22 MB)
47-Day 2 Building Multi Model Gradio UIs with GPT and Claude Streaming.mkv (69.28 MB)
48-Day 3 Building Chat UIs with Gradio Your First Conversational AI Assistant.mkv (63.18 MB)
49-Day 3 Building a Streaming Chatbot with Gradio and OpenAI API.mkv (42.58 MB)
50-Day 3 System Prompts Multi Shot Prompting and Your First Look at RAG.mkv (128.59 MB)
51-Day 4 How LLM Tool Calling Really Works No Magic Just Prompts.mkv (65.78 MB)
52-Day 4 Common Use Cases for LLM Tools and Agentic AI Workflows.mkv (16.11 MB)
53-Day 4 Building an Airline AI Assistant with Tool Calling in OpenAI and Gradio.mkv (139.25 MB)
54-Day 4 Handling Multiple Tool Calls with OpenAI and Gradio.mkv (117.8 MB)
55-Day 4 Building Tool Calling with SQLite Database Integration.mkv (149.41 MB)
56-Day 5 Introduction to Agentic AI and Building Multi Tool Workflows.mkv (115.09 MB)
57-Day 5 How Gradio Works Building Web UIs from Python Code.mkv (164.35 MB)
58-Day 5 Building Multi Modal Apps with DALL E 3 Text to Speech and Gradio Bloc.mkv (234.21 MB)
59-Day 5 Running Your Multimodal AI Assistant with Gradio and Tools.mkv (110.42 MB)
60-Day 5 Extra Compare Frontier LLMs with OpenRouter Generate SVG Art in Python.mkv (64.11 MB)
61-Day 1 Introduction to Hugging Face Platform Models Datasets and Spaces.mkv (173.11 MB)
62-Day 1 HuggingFace Libraries Transformers Datasets and Hub Explained.mkv (21.14 MB)
63-Day 1 Introduction to Google Colab and Cloud GPUs for AI Development.mkv (59.53 MB)
64-Day 1 Getting Started with Google Colab Setup Runtime and Free GPU Access.mkv (91.52 MB)
65-Day 1 Setting Up Google Colab with Hugging Face and Running Your First Model.mkv (119.34 MB)
66-Day 1 Running Stable Diffusion and FLUX on Google Colab GPUs.mkv (325.94 MB)
67-Day 2 Introduction to Hugging Face Pipelines for Quick AI Inference.mkv (96.68 MB)
68-Day 2 HuggingFace Pipelines API for Sentiment Analysis on Colab T4 GPU.mkv (142.39 MB)
69-Day 2 Named Entity Recognition Q A and Hugging Face Pipeline Tasks.mkv (234.3 MB)
70-Day 2 Hugging Face Pipelines Image Audio Diffusion Models in Colab.mkv (165.79 MB)
71-Day 3 Tokenizers How LLMs Convert Text to Numbers.mkv (97.99 MB)
72-Day 3 Tokenizers in Action Encoding and Decoding with Llama 3 1.mkv (134.11 MB)
73-Day 3 How Chat Templates Work LLaMA Tokenizers and Special Tokens.mkv (49.75 MB)
74-Day 3 Comparing Tokenizers Phi 4 DeepSeek and QWENCoder in Action.mkv (66.96 MB)
75-Day 4 Deep Dive into Transformers Quantization and Neural Networks.mkv (47.87 MB)
76-Day 4 Working with Hugging Face Transformers Low Level API and Quantization.mkv (212.54 MB)
77-Day 4 Inside LLaMA PyTorch Model Architecture and Token Embeddings.mkv (108.56 MB)
78-Day 4 Inside LLaMA Decoder Layers Attention and Why Non Linearity Matters.mkv (90.29 MB)
79-Day 4 Running Open Source LLMs Phi Gemma Qwen DeepSeek with Hugging Face.mkv (235.64 MB)
80-Day 5 Visualizing Token by Token Inference in GPT Models.mkv (129.22 MB)
81-Day 5 Building Meeting Minutes from Audio with Whisper and Google Colab.mkv (106.5 MB)
82-Day 5 Building Meeting Minutes with OpenAI Whisper and LLaMA 3 2.mkv (212.12 MB)
83-Day 5 Week 3 Wrap Up Build a Synthetic Data Generator with Open Source Models.mkv (66.94 MB)
84-Day 1 Choosing the Right LLM Model Selection Strategy and Basics.mkv (214.23 MB)
85-Day 1 The Chinchilla Scaling Law Parameters Training Data and Why It Matters.mkv (22.55 MB)
86-Day 1 Understanding AI Model Benchmarks GPQA MMLU Pro and HLE.mkv (30 MB)
87-Day 1 Limitations of AI Benchmarks Data Contamination and Overfitting.mkv (48.54 MB)
88-Day 1 Build a Connect Four Leaderboard Reasoning Benchmark.mkv (169.26 MB)
89-Day 2 Navigating AI Leaderboards Artificial Analysis HuggingFace More.mkv (176.15 MB)
90-Day 2 Artificial Analysis Deep Dive Model Intelligence vs Cost Comparison.mkv (185.58 MB)
91-Day 2 Vellum SEAL and LiveBench Essential AI Model Leaderboards.mkv (208.13 MB)
92-Day 2 LM Arena Blind Testing AI Models with Community Elo Ratings.mkv (38.08 MB)
93-Day 2 Commercial Use Cases Automation Augmentation Agentic AI.mkv (148.22 MB)
94-Day 3 Selecting LLMs for Code Generation Python to C with Cursor.mkv (114.71 MB)
95-Day 3 Selecting Frontier Models GPT 5 Claude Grok Gemini for C Code Gen.mkv (196.71 MB)
96-Day 3 Porting Python to C with GPT 5 230x Performance Speedup.mkv (89.6 MB)
97-Day 3 AI Coding Showdown GPT 5 vs Claude vs Gemini vs Groq Performance.mkv (79.77 MB)
98-Day 4 Open Source Models for Code Generation Qwen DeepSeek Ollama.mkv (170.86 MB)
99-Day 4 Building a Gradio UI to Test Python to C Code Conversion Models.mkv (37.71 MB)
100-Day 4 Qwen 3 Coder vs GPT OSS OpenRouter Model Performance Showdown.mkv (91.65 MB)
101-Day 5 Model Evaluation Technical Metrics vs Business Outcomes.mkv (76.34 MB)
102-Day 5 Python to Rust Code Translation Testing Gemini 2 5 Pro with Cursor.mkv (130.46 MB)
103-Day 5 Porting Python to Rust Testing GPT Claude and Qwen Models.mkv (127.53 MB)
104-Day 5 Open Source Model Wins Rust Code Generation Speed Challenge.mkv (324.69 MB)
105-Day 1 Introduction to RAG Retrieval Augmented Generation Fundamentals.mkv (150.54 MB)
106-Day 1 Building a Simple RAG Knowledge Assistant with GPT 4 1 Nano.mkv (145.88 MB)
107-Day 1 Building a Simple RAG System Dictionary Lookup and Context Retrieval.mkv (109.8 MB)
108-Day 1 Vector Embeddings and Encoder LLMs The Foundation of RAG.mkv (75.72 MB)
109-Day 1 How Vector Embeddings Represent Meaning From word2vec to Encoders.mkv (31.97 MB)
110-Day 1 Understanding the Big Idea Behind RAG and Vector Data Stores.mkv (105.43 MB)
111-Day 2 Vectors for RAG Introduction to LangChain and Vector Databases.mkv (115.38 MB)
112-Day 2 Breaking Documents into Chunks with LangChain Text Splitters.mkv (94.67 MB)
113-Day 2 Encoder Models vs Vector Databases OpenAI BERT Chroma FAISS.mkv (41.61 MB)
115-Day 2 3D Vector Visualizations and Comparing Embedding Models.mkv (167.46 MB)
116-Day 3 Building a Complete RAG Pipeline with LangChain and Chroma.mkv (97.05 MB)
117-Day 3 Building a RAG Pipeline with LangChain LLM Retriever Setup.mkv (116.91 MB)
118-Day 3 Building RAG with LangChain Retriever and LLM Integration.mkv (133.99 MB)
119-Day 3 Building Production RAG with Python Modules and Gradio UI.mkv (90.7 MB)
120-Day 3 RAG with Conversation History Building a Gradio UI and Debugging Chunki.mkv (86.91 MB)
121-Day 4 RAG Evaluations Measuring Performance and Iterating on Your Pipeline.mkv (176.85 MB)
122-Day 4 Evaluating RAG Systems Retrieval Metrics LLM as Judge and Golden Data.mkv (63.05 MB)
123-Day 4 Evaluating RAG Systems MRR NDCG and Test Data with Pydantic.mkv (194.3 MB)
124-Day 4 LLM as a Judge Evaluating RAG Answers with Structured Outputs.mkv (123 MB)
125-Day 4 Running RAG Evaluations with Gradio MRR nDCG and Test Results.mkv (100.97 MB)
126-Day 4 Experimenting with Chunking Strategies and Embedding Models in RAG.mkv (61.44 MB)
127-Day 4 Testing OpenAI Embeddings and Evaluating RAG Performance Gains.mkv (46.49 MB)
128-Day 5 Advanced RAG Techniques Pre processing Re ranking Evals.mkv (165.46 MB)
129-Day 5 Advanced RAG Techniques Chunking Encoders and Query Rewriting.mkv (38.31 MB)
130-Day 5 Advanced RAG Techniques Query Expansion Re ranking GraphRAG.mkv (62.97 MB)
131-Day 5 Building Advanced RAG Without LangChain Semantic Chunking with LLMs.mkv (283.21 MB)
132-Day 5 Creating Embeddings with Chroma Visualizing with t SNE and Re ranking.mkv (246.69 MB)
133-Day 5 Building RAG Without LangChain Re ranking and Query Rewriting.mkv (151.51 MB)
134-Day 5 Building Production RAG with Query Expansion and Multiprocessing.mkv (156.72 MB)
135-Day 5 Advanced RAG Evaluation From 0 73 to 0 91 MRR with GPT 4o.mkv (133.62 MB)
136-Day 5 RAG Challenge Beat My Results Build Your Knowledge Worker.mkv (174.65 MB)
137-Day 1 Training Datasets and Generalization Your Capstone Begins.mkv (200.72 MB)
138-Day 1 Finetuning LLMs The Price is Right Capstone Project Intro.mkv (159.38 MB)
139-Day 1 Curating Datasets Finding Data Sources and Building Training Sets.mkv (52.38 MB)
140-Day 1 Curating Amazon Data with Hugging Face for Price Prediction.mkv (161.43 MB)
141-Day 1 Exploring Amazon Dataset Distribution and Removing Duplicates.mkv (135.47 MB)
142-Day 1 Weighted Sampling with NumPy and Uploading Datasets to Hugging Face.mkv (261.69 MB)
143-Day 2 Five Step Strategy for Selecting and Applying LLMs to Business Problems.mkv (133.02 MB)
144-Day 2 The Five Step AI Process Productionizing with MLOps.mkv (102.89 MB)
145-Day 2 Data Pre processing with LLMs and Groq Batch Mode.mkv (203.01 MB)
146-Day 2 Batch Processing with Groq API and JSONL Files for LLM Workflows.mkv (225.28 MB)
147-Day 2 Batch Processing with Groq Running 22K LLM Requests for Under 1.mkv (179.37 MB)
148-Day 3 Building Baseline Models with Traditional ML and XGBoost.mkv (152.85 MB)
149-Day 3 Building Your First Baseline with Random Pricer and Scikit learn.mkv (92.47 MB)
150-Day 3 Baseline Models and Linear Regression with Scikit Learn.mkv (47.85 MB)
151-Day 3 Bag of Words and CountVectorizer for Linear Regression NLP.mkv (49.77 MB)
152-Day 3 Random Forest and XGBoost Ensemble Models in Scikit Learn.mkv (137.36 MB)
153-Day 4 Training Your First Neural Network and Testing Frontier Models.mkv (177.31 MB)
154-Day 4 Human Baseline Performance vs Machine Learning Models in PyTorch.mkv (103.88 MB)
155-Day 4 Building Your First Neural Network with PyTorch.mkv (132.33 MB)
156-Day 4 Testing GPT 4o mini and Claude Opus Against Neural Networks.mkv (86.8 MB)
157-Day 4 Testing Gemini 3 GPT 5 1 Claude 4 5 Grok on Price Prediction.mkv (82.03 MB)
158-Day 5 Fine Tuning OpenAI Frontier Models with Supervised Fine Tuning.mkv (172.3 MB)
159-Day 5 Fine Tuning GPT 4o Nano with OpenAI s API for Custom Models.mkv (200.71 MB)
160-Day 5 Fine Tuning GPT 4o mini nano Running Jobs and Monitoring Training.mkv (112.35 MB)
161-Day 5 Fine Tuning Results When GPT 4o mini Gets Worse Not Better.mkv (121.29 MB)
162-Day 5 When Fine Tuning Frontier Models Fails Building Deep Neural Networks.mkv (94.8 MB)
163-Day 5 Deep Neural Network Redemption 289M Parameters vs Frontier Models.mkv (69.68 MB)
164-Day 1 Introduction to QLoRA for Fine Tuning Open Source Models.mkv (142.81 MB)
165-Day 1 LoRA Training LLaMA 3 2 with Low Rank Adapters.mkv (29.24 MB)
166-Day 1 LoRA Hyperparameters and QLoRA Quantization Explained.mkv (73.97 MB)
167-Day 1 Setting Up Google Colab and Exploring LLaMA 3 2 Model Architecture.mkv (168.08 MB)
168-Day 1 Loading Models with 8 bit and 4 bit Quantization Using QLoRA.mkv (135.43 MB)
169-Day 1 LoRA Parameter Calculations and Model Size on Hugging Face.mkv (119.78 MB)
170-Day 2 Preparing Your Dataset for Fine Tuning with Token Limits.mkv (133.98 MB)
171-Day 2 Fine Tuning Data Prep Rounding Prices and Token Length Optimization.mkv (153.31 MB)
172-Day 2 Preparing Hugging Face Datasets and Testing Base LLaMA 3 2 Model.mkv (216.68 MB)
173-Day 2 Base Models vs Chat Models Understanding LLaMA Fine Tuning.mkv (145.05 MB)
174-Day 3 Fine Tuning Hyperparameters QLoRA Settings and Training Config.mkv (134.29 MB)
175-Day 3 Learning Rate Optimizers and Training Hyperparameters for LoRA.mkv (35.49 MB)
176-Day 3 Setting Up Training Hyperparameters qLoRA Config Weights Biases.mkv (105.76 MB)
177-Day 3 Setting Up Weights Biases and the HuggingFace SFT Trainer.mkv (77.31 MB)
178-Day 3 Running Fine Tuning with TRL and Monitoring Training in Weights Biases.mkv (123.51 MB)
179-Day 4 Monitoring Your Fine Tuning Run with Weights Biases.mkv (143.53 MB)
180-Day 4 Full Dataset Training on Google Colab A100 with 800K Data Points.mkv (128.93 MB)
181-Day 4 Monitoring Training Loss and Learning Rate in Weights Biases.mkv (118.21 MB)
182-Day 4 Analyzing Weights Biases Results and Catching Overfitting.mkv (214.81 MB)
183-Day 4 Managing Runs in Weights Biases and Selecting Best Model Checkpoints.mkv (80.07 MB)
184-Day 5 Results Day Running Inference on Fine Tuned Models Loss Calculation D.mkv (156.56 MB)
185-Day 5 Cross Entropy Loss How LLMs Calculate Probability Distributions.mkv (46.49 MB)
186-Day 5 Testing Our Fine Tuned LoRA Model Against GPT 4o Nano.mkv (77.55 MB)
187-Day 5 Fine Tuned LLaMA 3 2 Crushes GPT 5 1 and Frontier Models.mkv (79.9 MB)
188-Day 1 Intro to Agentic AI Serverless Deployment on Modal.mkv (154.96 MB)
189-Day 1 Designing Agent Architectures Modal Platform Setup.mkv (53.56 MB)
190-Day 1 Running Python Locally and in the Cloud with Modal Remote Execution.mkv (79.67 MB)
191-Day 1 Setting Up Modal Secrets and Deploying LLaMA Models to the Cloud.mkv (79.13 MB)
192-Day 1 Deploying Fine Tuned Models to Modal Cloud with Persistent Storage.mkv (182.07 MB)
193-Day 1 Building Your First Agent with Modal Serverless AI.mkv (129.98 MB)
194-Day 2 Building Advanced RAG with ChromaDB and Vector Stores No LangChain.mkv (116.18 MB)
195-Day 2 Visualizing Chroma Vectors with t SNE and Building a RAG Pipeline.mkv (234.72 MB)
196-Day 2 RAG with GPT 4o vs Fine Tuned Models Building an Ensemble.mkv (72.27 MB)
197-Day 2 Ensemble Model Success Combining RAG Neural Networks Modal.mkv (84.35 MB)
198-Day 2 Building and Testing an Ensemble Agent with Multiple LLM Calls.mkv (58.96 MB)
199-Day 3 Structured Outputs with Pydantic and Constrained Decoding.mkv (170.71 MB)
200-Day 3 Building a Deal Scanner with Structured Outputs and Pydantic.mkv (119.9 MB)
201-Day 3 Structured Outputs for Parsing Building a Pushover Notification Agent.mkv (159.26 MB)
202-Day 4 Building Agentic AI Planning Agents with Tool Orchestration.mkv (48.76 MB)
203-Day 4 Building an Autonomous Planner Agent with Tool Calling and GPT 4.mkv (66.2 MB)
204-Day 4 Building an Autonomous Multi Agent System with Tool Calling and Agent Lo.mkv (133.22 MB)
205-Day 4 Building a Multi Model AI Platform 34 Calls Across GPT 5 Claude Open.mkv (26.51 MB)
206-Day 5 Finalizing Your Agentic Workflow and Becoming an AI Engineer.mkv (44.15 MB)
207-Day 5 Building the Price Is Right Agent UI with Gradio and DealAgentFramework.mkv (81.07 MB)
208-Day 5 Course Wrap Up Your Journey to AI Engineer.mkv (127.62 MB)]
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May 21, 2026

AI Engineer Agentic Track: The Complete Agent & MCP Course

AI Engineer Agentic Track: The Complete Agent & MCP Course
2025-11-03
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Language: English (US) | Size: 14.11 GB | Duration: 16h 48m

Master AI Agents in 30 days: build 8 real-world projects with OpenAI Agents SDK, CrewAI, LangGraph, AutoGen and MCP.

What you’ll learn
Project 1: Career Digital Twin. Build and deploy your own Agent to represent you to potential future employers.
Project 2: SDR Agent. An instant business application: create Sales Representatives that craft and send professional emails .
Project 3: Deep Research. Make your own version of the essential Agentic use case: a team of Agents that carry out extensive research on any topic you choose.
Project 4: Build a Stock Picker Agent in minutes with CrewAI-automate your search for investment gems!
Project 5: Deploy your own 4-Agent Engineering Team-manage, build, and test software apps with CrewAI and Coder Agents in Docker!
Project 6: Build your own version of OpenAI’s Operator Agent-your Sidekick works with you inside your browser via LangGraph!
Project 7: Agent Creator-an Agent that builds and launches new Agents using AutoGen, unlocking endless AI possibilities!
Project 8: Capstone-build a Trading Floor with 4 Agents making autonomous trades, powered by 6 MCP servers and 44 tools!

Requirements
While it’s ideal if you can code in Python and have some experience working with LLMs, this course is designed for a very wide audience, regardless of background. I’ve included a whole folder of self-study labs that cover foundational technical and programming skills. If you’re new to coding, there’s only one requirement: plenty of patience!
The course runs best if you have a small budget for APIs, but it’s totally your choice. You can complete the entire course with no API spend. If you do wish to use frontier models, the typical spend would be under $5. You can choose to access more capabilities if you’re comfortable spending a little more.

Description
2025 is the year that Agents enter the workforce. This is nothing short of a watershed moment for Artificial Intelligence. It has never been more important to be an expert with Agentic AI. And that is precisely the goal of this course: to equip you with the skills and expertise to design, build and deploy Autonomous AI Agents, opening up new career and commercial opportunities.This is an intensive 6-week program to master Agentic AI. We start by building foundational expertise, connecting LLMs using proven design patterns. Then, each week, we upskill with new frameworks: OpenAI Agents SDK, CrewAI, LangGraph and Autogen. The course culminates with a full week on the remarkable opportunities opened up by MCP.Above all, this is a hands-on course. I’m a big believer that the best way to learn is by DOING. So please prepare to roll up your sleeves! We’ll build 8 real-world projects; some are astonishing, some are intriguing, and some are quite surreal. But one thing’s for sure: all are powerful demonstrations of Agentic AI’s potential to utterly transform the business landscape.So come join me on this comprehensive 6-week journey. By the end, you will have mastered Agentic AI. You will have expertise in all the major frameworks. You’ll be well-versed in the strengths and traps of Agentic AI. You’ll confidently unleash Autonomous Agents to solve real-world commercial problems. And along the way, you’ll have had a whole lot of fun with the astounding, groundbreaking technology that is Agentic AI.

Who this course is for:
Well, perhaps I’m biased, but I’d say: anyone and everyone! If you’re fascinated in the potential of Agents and hungry to have the skills to create powerful Agentic AI – then you’ve come to the right place. While it’s most suited to those with programming experience, I’ve designed the course to work for all backgrounds.

For More Courses Visit & Bookmark Your Preferred Language Blog
From Here:——–

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May 21, 2026

AI Driven Cybersecurity Automation

4.96 GB | 9min 52s | mp4 | 1920X1080 | 16:9
Genre:eLearning |Language:English

Files Included :
1 11 The Problem with Manual Cyber Defense.mp4 (107.79 MB)
2 12 Why Rule-Based Automation Breaks.mp4 (75.02 MB)
3 13 AI as a Decision Engine (Not Just Detection).mp4 (90.73 MB)
4 14 Security Automation vs Orchestration.mp4 (74.5 MB)
5 15 Where Humans Should Stay in the Loop.mp4 (64.51 MB)
6 16 Automation Failure Case Studies.mp4 (57.81 MB)
7 17 Mapping Security Tasks to Automation Potential.mp4 (56.58 MB)
8 21 How AI Makes Decisions.mp4 (62.6 MB)
9 22 Patterns vs Rules vs Learning.mp4 (50.02 MB)
10 23 Supervised vs Unsupervised AI for Automation.mp4 (55.08 MB)
11 24 Confidence, Thresholds & Risk Scores.mp4 (44.33 MB)
12 25 Training AI for Operational Environments.mp4 (52.57 MB)
13 26 Why Perfect Accuracy Is Dangerous.mp4 (55.51 MB)
14 27 Practical Exercise Automation Decision Mapping.mp4 (52.84 MB)
15 31 Traditional Detection Pipelines.mp4 (51.17 MB)
16 32 AI-Driven Detection Pipelines.mp4 (48.84 MB)
17 33 Event Correlation Using AI.mp4 (43.83 MB)
18 34 Alert Deduplication & Suppression.mp4 (61.41 MB)
19 35 AI-Based Severity Scoring.mp4 (57.29 MB)
20 36 False Positive Reduction Strategies.mp4 (54.06 MB)
21 37 Practical Lab Automated Alert Prioritization.mp4 (51.93 MB)
22 41 Why Monitoring Doesn’t Scale.mp4 (70.07 MB)
23 42 AI-Driven Anomaly Detection.mp4 (54.63 MB)
24 43 Behavioral Baselines & Drift.mp4 (57.27 MB)
25 44 Continuous Monitoring with AI.mp4 (64.62 MB)
26 45 Context-Aware Monitoring.mp4 (62.32 MB)
27 46 Alert Escalation Automation.mp4 (56.01 MB)
28 47 Lab Self-Adjusting Monitoring Rules.mp4 (51.91 MB)
29 51 Anatomy of a Modern SOC.mp4 (61.1 MB)
30 52 Where SOC Analysts Lose Time.mp4 (57.75 MB)
31 53 AI for Alert Triage.mp4 (55.3 MB)
32 54 Automated Case Creation & Enrichment.mp4 (51.3 MB)
33 55 Analyst Assistants vs Autonomous Agents.mp4 (66.7 MB)
34 56 Measuring SOC Automation Success.mp4 (53.5 MB)
35 57 Lab AI-Driven SOC Workflow.mp4 (60.52 MB)
36 61 Incident Response Bottlenecks.mp4 (56 MB)
37 62 AI-Assisted Incident Classification.mp4 (44.96 MB)
38 63 Automated Evidence Collection.mp4 (54.45 MB)
39 64 Decision Trees vs AI Decisions.mp4 (53.37 MB)
40 65 Automated Containment Actions.mp4 (41.95 MB)
41 66 Rollback & Human Override.mp4 (59.72 MB)
42 67 Practical Simulation Automated IR Flow.mp4 (49.46 MB)
43 71 From Detection to Defense.mp4 (58.22 MB)
44 72 Automated Blocking & Isolation.mp4 (97.8 MB)
45 73 Adaptive Firewall & Network Rules.mp4 (77.33 MB)
46 74 Endpoint Containment Automation.mp4 (77.57 MB)
47 75 Confidence-Based Defense Actions.mp4 (83.07 MB)
48 76 Avoiding Self-Inflicted Outages.mp4 (87.58 MB)
49 77 Lab Controlled Autonomous Defense.mp4 (90.37 MB)
50 81 Why Cloud Security Must Be Automated.mp4 (119.26 MB)
51 82 AI for IAM Abuse Detection.mp4 (97.12 MB)
52 83 Automated Permission Risk Scoring.mp4 (79.16 MB)
53 84 Cloud Misconfiguration Auto-Fixing.mp4 (124.85 MB)
54 85 AI-Driven Access Revocation.mp4 (111.91 MB)
55 86 Continuous Cloud Defense Loops.mp4 (97.72 MB)
56 87 Lab AI-Based Cloud Security Automation.mp4 (118.04 MB)
57 91 Network Security Automation Basics.mp4 (103.68 MB)
58 92 AI-Driven IDS & IPS.mp4 (91.24 MB)
59 93 Automated Lateral Movement Detection.mp4 (83.87 MB)
60 94 Endpoint Behavior Automation.mp4 (81.62 MB)
61 95 Deception & Moving Target Defense.mp4 (84.68 MB)
62 96 Automated Threat Containment.mp4 (66.31 MB)
63 97 Lab Network + Endpoint Automation.mp4 (63.42 MB)
64 101 When Automation Goes Wrong.mp4 (63.94 MB)
65 102 Automation Blind Spots.mp4 (71.49 MB)
66 103 Feedback Loops & Cascade Failures.mp4 (62.75 MB)
67 104 Adversarial Abuse of Automation.mp4 (54.09 MB)
68 105 Human-in-the-Loop Design.mp4 (57.15 MB)
69 106 Kill Switches & Rollback Systems.mp4 (59.63 MB)
70 111 Attacks Against AI Security Systems.mp4 (74.77 MB)
71 112 Data Poisoning in Automation Pipelines.mp4 (50.12 MB)
72 113 Model Evasion Techniques.mp4 (50 MB)
73 114 Securing Training Data.mp4 (61.44 MB)
74 115 Monitoring AI Decisions.mp4 (60.47 MB)
75 116 Trust & Explainability in Defense AI.mp4 (67.46 MB)]
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May 21, 2026

AI Coder From Vibe Coder to Agentic Engineer in 3 weeks

19.69 GB | 13min 4s | mp4 | 1920X1080 | 16:9
Genre:eLearning |Language:English

Files Included :
1 Day 1 – Welcome to the Course Building a 3D Game with Cursor AI.mp4 (195.98 MB)
2 Day 1 – Building a First-Person Shooter Game with Cursor AI Agent.mp4 (157 MB)
3 Day 1 – The Missing Manual for Agentic AI Coding.mp4 (174.9 MB)
4 Day 1 – Meet Your Instructor & the 3-Week AI Coder Course Roadmap.mp4 (293.78 MB)
5 Day 1 – Vibe Coding, Agentic Coders & Coding Agents Like Claude Code.mp4 (226.66 MB)
6 Day 1 – The 8 Stages of AI Coding From ChatGPT to Agent Orchestration.mp4 (280.57 MB)
7 Day 1 – Wrapping Up Your Unique Agentic Coding Journey Ahead.mp4 (106.31 MB)
8 Day 2 – How LLMs Work Tokens, Memory, and Reasoning Explained.mp4 (308.39 MB)
9 Day 2 – Tools, Loops, and the Definition of AI Agents.mp4 (228.48 MB)
10 Day 2 – Context Engineering System Prompts, Context Windows & agents md.mp4 (332.82 MB)
11 Day 2 – Mastering agents md Context Window Strategy for Coding Agents.mp4 (290.02 MB)
12 Day 2 – The Evolution of AI Coding Workflows From YOLO to Ralph Loops.mp4 (371.63 MB)
13 Day 2 – Beyond the Hype Comparing LLMs on Artificial Analysis.mp4 (159.37 MB)
14 Day 3 – Hands-On with Cursor, Copilot, Codex & Agentic Vibe Coding.mp4 (197.86 MB)
15 Day 3 – Exploring the agents md File and Cursor Settings for Vibe Coding.mp4 (161.57 MB)
16 Day 3 – Building a Kanban App with the Cursor AI Agent in YOLO Mode.mp4 (192.17 MB)
17 Day 3 – Building a Kanban Board with GitHub Copilot in VS Code.mp4 (275.21 MB)
18 Day 3 – OpenAI Codex VS Code Extension Zero-Shot Kanban App Build.mp4 (166.91 MB)
19 Day 3 – Building a Kanban App with Antigravity IDE and Gemini 3 Pro.mp4 (182.03 MB)
20 Day 3 – Cursor vs Copilot vs Codex vs Antigravity Final Verdict.mp4 (97.96 MB)
21 Day 4 – YOLO Mode Choosing the Right LLM for Agentic Coding in IDEs.mp4 (244.74 MB)
22 Day 4 – Five Principles for Successful Vibe Coding Be the Boss.mp4 (277.76 MB)
23 Day 4 – Responsible YOLO Coding Setting Up OpenRouter for AI Projects.mp4 (261.49 MB)
24 Day 4 – YOLO Mode Building a Next js Website with GPT Codex in Cursor.mp4 (195.7 MB)
25 Day 4 – Adding an AI Digital Twin Chat with OpenRouter & Vibe Coding.mp4 (166.16 MB)
26 Day 4 – Tutorials, Code Reviews with Opus, and Cross-Model Collaboration.mp4 (186.42 MB)
27 Day 5 – Karpathy on Vibe Coding & Rules for Building a Commercial MVP.mp4 (271.62 MB)
28 Day 5 – Web Apps 101 Front-End, Back-End, APIs & Docker Setup.mp4 (292 MB)
29 Day 5 – Setting Up a Full-Stack Project with GitHub Copilot & FastAPI.mp4 (193.98 MB)
30 Day 5 – Building Step-by-Step with AI Copilot Planning & Scaffolding.mp4 (205.32 MB)
31 Day 5 – Building a Kanban App with GitHub Copilot, Docker & FastAPI.mp4 (216.58 MB)
32 Day 5 – Building a Kanban App with Codex Debugging Drag and Drop.mp4 (188.51 MB)
33 Day 5 – AI Assistant Kanban App Complete Codex, OpenRouter & Week 1 Wrap.mp4 (233.9 MB)
34 Day 1 – Welcome to Pro Week From Vibe Coding to Vibe Engineering.mp4 (203.52 MB)
35 Day 1 – The Rise of Claude Code History, Setup & Installation in VS Code.mp4 (248.73 MB)
36 Day 1 – Getting Started with Claude Code CLI Init, Context & Testing.mp4 (174.81 MB)
37 Day 1 – Claude Code Review Fixing Hallucinations & Refactoring Code.mp4 (268.3 MB)
38 Day 1 – OpenCode Use Free Models Like GLM 4 7 as a Claude Code Alternative.mp4 (184.93 MB)
39 Day 1 – AMP Code, OpenCode & Claude Code with OpenRouter and Ollama.mp4 (245.61 MB)
40 Day 2 – Claude Code Commands, Shortcuts & Configuration Deep Dive.mp4 (199.25 MB)
41 Day 2 – Sessions, Checkpoints & Git Managing Your Claude Code Workflow.mp4 (247.21 MB)
42 Day 2 – Claude Code Checkpoints, Rewind, and YOLO Mode Explained.mp4 (100.93 MB)
43 Day 2 – Claude Code YOLO Mode Bypass Permissions for Autonomous Coding.mp4 (104.55 MB)
44 Day 2 – Ralph Loops in Claude Code Autonomous Agent Coding on Autopilot.mp4 (225.76 MB)
45 Day 3 – MCP, Skills & Plugins The Big Three Building Blocks of Claude Code.mp4 (288.55 MB)
46 Day 3 – MCP Servers Explained Hosts, Clients, and Discovery for Claude Code.mp4 (309.92 MB)
47 Day 3 – Adding MCP Servers to Claude Code Context7 & Polygon IO.mp4 (187.76 MB)
48 Day 3 – Skills vs MCP Claude Code’s Simpler Way to Add Abilities.mp4 (370.11 MB)
49 Day 3 – Skills Marketplaces Installing Agent Browser in Claude Code.mp4 (218.56 MB)
50 Day 3 – Claude Code Plugins Marketplace, Installation & Best Practices.mp4 (204.95 MB)
51 Day 3 – MCP vs Skills vs Plugins Choosing the Right Claude Code Setup.mp4 (201.37 MB)
52 Day 4 – Building with Claude Code, Jira, MCP & Plugins Workflow.mp4 (217.08 MB)
53 Day 4 – Connecting Claude Code to Jira MCP Server & GitHub Repository.mp4 (159.97 MB)
54 Day 4 – Setting Up GitHub MCP Server & Featured Dev Plugin in Claude Code.mp4 (183.22 MB)
55 Day 4 – Claude Code Autonomy From Jira Issue to PR via MCP Server.mp4 (178.87 MB)
56 Day 4 – Claude Code Builds a Full Next js App from a Jira Ticket.mp4 (165.87 MB)
57 Day 4 – Debugging Strategies for Claude Code A Disciplined Approach.mp4 (313.64 MB)
58 Day 5 – Building a SaaS Platform Claude Code Skills & Claude md Setup.mp4 (217.96 MB)
59 Day 5 – Writing claude md and Creating Custom Skills for Cerebras.mp4 (158.01 MB)
60 Day 5 – Setting Up Jira Tickets for Claude Code to Build Our V1 Product.mp4 (114.55 MB)
61 Day 5 – Building Features with Claude Code, Jira, and FastAPI.mp4 (198.6 MB)
62 Day 5 – Testing the AI Legal Doc Generator with Cerebras and Claude Code.mp4 (188.07 MB)
63 Day 5 – Final PR Merge, Full SaaS Demo & Week 2 Victory in Claude Code.mp4 (269.12 MB)
64 Day 1 – Claude Code Pro Sub-Agents, Hooks, Swarms & Controlled Chaos.mp4 (278.36 MB)
65 Day 1 – Setting Up the FiNALLY Project Our Multi-Agent Trading App.mp4 (166.67 MB)
66 Day 1 – Creating Custom Slash Commands in Claude Code.mp4 (204.59 MB)
67 Day 1 – Building Agents & Sub-Agents with Claude Code and Codex CLI.mp4 (210.14 MB)
68 Day 1 – Subagents vs Agent Teams in Claude Code with Codex Review.mp4 (166.22 MB)
69 Day 1 – Claude Code Hooks Auto-Trigger Reviews with Events & Commands.mp4 (166.23 MB)
70 Day 1 – Building Custom Claude Code Plugins and Marketplaces.mp4 (268.69 MB)
71 Day 2 – Claude Code Sandboxing and Cloud Execution Deep Dive.mp4 (227.23 MB)
72 Day 2 – Remote Execution & Cloud Sandboxes with Claude Code on the Web.mp4 (280.96 MB)
73 Day 2 – Setting Up Claude Code Sandbox & GitHub Integration.mp4 (234.49 MB)
74 Day 2 – 5 Ways to Run Claude Code Remotely Cloud, Web, Mobile & GitHub.mp4 (182.51 MB)
75 Day 2 – Third-Party Cloud Sandboxes Running Claude Code on Sprites dev.mp4 (189.81 MB)
76 Day 2 – Cloud Sandbox Recap YOLO Mode with Sprites dev & GitHub PRs.mp4 (158.07 MB)
77 Day 3 – Large Codebases with Claude Code, Codex & Sprites dev.mp4 (229.77 MB)
78 Day 3 – Best Practices for Using Claude Code on Large Team Codebases.mp4 (289.76 MB)
79 Day 3 – Driving Claude Code Programmatically with Claude Agent SDK.mp4 (145.47 MB)
80 Day 3 – Claude Cowork Agentic AI for Everyday Tasks Demo.mp4 (106.28 MB)
81 Day 3 – OpenClaw Your Personal AI Sidekick via Telegram & WhatsApp.mp4 (216.89 MB)
82 Day 4 – Claude Code Agent Teams Swarms and Orchestration.mp4 (322.55 MB)
83 Day 4 – Setting Up Claude Code Agent Teams for Full-Stack Development.mp4 (153.04 MB)
84 Day 4 – Multi-Agent Team Build Live Trading Dashboard with Claude Opus.mp4 (283.16 MB)
85 Day 4 – GSD Spec-Driven Design Meets Multi-Agent Orchestration.mp4 (225.16 MB)
86 Day 4 – Building a Trading Platform with Claude GSD A 5-Hour Deep Dive.mp4 (167.08 MB)
87 Day 4 – GSD vs Claude Agent Teams Side-by-Side UI Comparison & Wrap-Up.mp4 (196.13 MB)
88 Day 5 – Gastown Orchestrating Swarms of Claude Code Agents.mp4 (242.5 MB)
89 Day 5 – Gastown’s Parallel Polecats Claude Code Agents Build in Chaos.mp4 (204.68 MB)
90 Day 5 – Gastown vs Claude Agent Teams vs GSD Multi-Agent Orchestrators.mp4 (229.48 MB)
91 Day 5 – Codex Wins Final Trader Workstation with Live Market Data.mp4 (335.26 MB)
92 Day 5 – Final Deployment, Course Wrap-Up & Coding Agent Best Practices.mp4 (256.26 MB)
93 Day 5 – Course Recap From Vibe Coding to Agentic Engineer.mp4 (141.26 MB)]
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May 21, 2026

Ai Automation: Build Llm Apps & Ai-Agents With N8N & Apis

Published 3/2025 MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz Language: English | Size: 12.00 GB | Duration: 13h 13m

Automate Everything: n8n, LLMs, OpenAI API, Deepseek, Ollama & RAG! Business & Private AI Agents; More than just ChatGPT

What you’ll learn
Fundamentals of Automation, AI Agents & LLMs (ChatGPT, Claude, Gemini, Deepseek, Llama, Mistral & more)
Introduction to automation & key tools (n8n, Make, Zapier, LangChain, LangGraph, Flowise)
Understanding and utilizing APIs for automation
OpenAI API: Pricing structure, compliant usage & project setup
Function calling with LLMs: Using calendars, emails, web search, webhooks, Airtable, Google Sheets & more
Everything about vector databases, embedding models & Retrieval-Augmented Generation (RAG)
n8n Basics & Automation Applications
Basics of n8n: Installation, importing, exporting & selling workflows
Automations with Airtable, Google Sheets & Google Cloud
Using simple JavaScript variables in automation
Expanding AI automation with LLMs: Email automation, sentiment analysis, databases
Integrating open-source LLMs (Deepseek R1, Llama, Mistral) into automation
Using external LLM APIs in n8n (Deepseek API, Groq API & more)
AI Agents & RAG Chatbots in Workflows
Integrating AI agents & RAG chatbots into workflows
Automated vector database updates with Google Drive
RAG chatbot with AI agent node, embeddings & retrieval techniques
AI-powered email agents for automated summaries & responses
Prompt engineering: Principles, best practices & avoiding errors
Hosting, Social Media & Advanced Automations
n8n self-hosting with Render & other options
Using AI agents in WhatsApp, Telegram & social media
Web scraping & automation with sub-workflows & webhooks
Debugging strategies for error-free n8n automation
Connecting Flowise AI agents with webhooks & Google Sheets
Extending n8n with Flowise & JavaScript custom tools
Business & Market Aspects of AI Automation
AI automation as a business: Selling automation & AI agents
Creating market-ready RAG bots for lead generation & website integration
Marketing strategies for successfully selling AI solutions
Optimizing RAG chatbots: Chunk size, overlap & data quality
LlamaIndex & LlamaParse for data preprocessing in Google Colab
Using Firecrawl for web data extraction in Markdown format
Security, privacy & ethical concerns: Jailbreaks, prompt injections & data poisoning
Copyright & data protection for AI-generated data
Legal frameworks: EU AI Act & more

Requirements
No prior knowledge required, everything is shown step by step.

Description
AI Automation is the Future!But how does it really work? And how can AI optimize business processes-on a whole new level, far beyond ChatGPT? The answer: AI Agents.This course guides you through both essential and advanced concepts in automation using AI automation, AI agents, LLMs, vector databases, Retrieval-Augmented Generation (RAG), and n8n. You’ll learn how to create powerful automations, build intelligent AI agents, and seamlessly integrate them into your workflows to enhance both business and personal projects.Additionally, you’ll receive 29 downloadable JSON workflows to accelerate your learning and implementation.What You’ll Learn in This Course:Fundamentals of Automation, AI Agents & LLMsDive into the world of AI automation:Introduction to automation, AI agents & essential tools (n8n, Make, Zapier, LangChain, LangGraph, Flowise).Understanding APIs and their role in automation.LLMs explained: ChatGPT, Claude, Gemini, Deepseek, Llama, Mistral & more.OpenAI API: Pricing structure, GDPR-compliant usage & project setup.Function calling with LLMs: How AI agents use tools like calendars, emails, web search, webhooks, Airtable, Google Sheets, and more.RAG (Retrieval-Augmented Generation): Vector databases & embeddings explained.n8n Basics: Installation & First WorkflowsMaster the fundamentals of n8n, the key to intelligent automation:Local installation with Node.js & using the web version without installation.Importing, exporting, and selling workflows.Setting up automations with Airtable, Google Sheets & Google Cloud.Using simple JavaScript variables in automation.Expanding AI Automation with LLMsBuild advanced AI-powered automations:Email automation with OpenAI API, Gmail, and Airtable.Real-time sentiment analysis & database storage.Integrating open-source LLMs (Deepseek R1, Llama, Mistral) into automation.Using any LLM API in n8n (Deepseek API, Groq API & more).Integrating AI Agents & RAG Chatbots into WorkflowsAutomate customer communication & data processing:RAG Agent: Automatically updating vector databases with Google Drive.RAG Chatbot using AI agent nodes, embeddings & retrieval techniques.AI-powered email agents for automated summaries & responses.Prompt Engineering for AI AgentsOptimize your prompts for better AI responsesrinciples & best practices for effective prompt engineering.Avoiding errors & precisely controlling AI outputs.Hosting, Social Media & Advanced AutomationsExpand your automations with self-hosting & real-time integrations:n8n self-hosting with Render & other options.Using AI agents in WhatsApp & Telegram.Social media automation with sub-workflows, webhooks & web scraping.Debugging & Optimizing API IntegrationsEnhance performance & error handling in n8n workflowsebugging strategies for error-free n8n automations.Connecting Flowise AI agents with webhooks & Google Sheets.Extending n8n with Flowise & JavaScript custom tools.Building a Business with AI Automation & AI AgentsLeverage your skills to create a profitable AI automation business:Selling automations & AI agents as services.Developing market-ready RAG bots for lead generation & website integration.Marketing strategies for successfully selling AI solutions.Optimizing RAG Chatbots: Data Quality & ChunkingImprove AI responses with optimized data strategies:Chunk size, overlap & data quality for better chatbot performance.Using Firecrawl for web data extraction in Markdown format.LlamaIndex & LlamaParse for data preprocessing in Google Colab.Security, Privacy & Ethical ConsiderationsProtect your AI agents & ensure GDPR compliance:Understanding & preventing jailbreaks, prompt injections & data poisoning.Ensuring copyright & data protection for AI-generated content.Key legal frameworks: EU AI Act & moreAdditionally, you’ll gain access to 29 ready-to-use JSON workflows, available for download to streamline your learning experience and accelerate implementation.Become an Expert in AI Agents & Automation!After this course, you will have a deep understanding of AI automation, n8n, LLMs & RAG and be able to develop, optimize, and deploy powerful AI agents for business applications.Sign up now and step into the future of AI automation.

Overview
Section 1: Introduction

Lecture 1 Welcome!

Lecture 2 Course Overview

Lecture 3 Important Tips for the Course

Lecture 4 Explanation of the Links that you need in the Course

Lecture 5 Important Links

Section 2: Basics – Automation, LLMs, Function Calling, Vector Databases & RAG Explained

Lecture 6 What to Expect in This Section

Lecture 7 What Are Automations, AI Automation, and AI Agents?

Lecture 8 What Is an API (Client and Server)

Lecture 9 Tools for Automation & AI Agents: n8n, Make, Zapier, LangChain, Flowise & More

Lecture 10 What are LLMs like ChatGPT, Claude, Gemini, Llama, Deepseek, Grok etc.

Lecture 11 OpenAI API Explained: Pricing, Project Setup, Management & Compliance

Lecture 12 Test Time Compute (TTS) Explained: Thinking Models like Deepseek R1 & OpenAI o3

Lecture 13 What is Function Calling in LLMs for AI Agents and AI Automations

Lecture 14 Vector Databases, Embedding Models & Retrieval-Augmented Generation (RAG)

Lecture 15 Key Takeaways

Section 3: n8n Basics – Installation, Interface & First Simple Workflows

Lecture 16 What This Section Covers

Lecture 17 Local Installation of n8n with Node.js & Interface Overview

Lecture 18 Managing Node Versions (Fixing Errors in n8n Installation)

Lecture 19 Updating n8n Locally via Node.js

Lecture 20 Testing n8n for Free Without Local Installation

Lecture 21 First Automation: Automatically Save Bookings from On Form Submit in Airtable

Lecture 22 Importing, Exporting, and Selling Workflows as JSON

Lecture 23 Automatically Backing Up Airtable Data Locally

Lecture 24 Connecting Google Sheets with n8n (Google Cloud Platform Console)

Lecture 25 Recap

Section 4: Expanding Automations with LLMs & AI

Lecture 26 Overview of This Section

Lecture 27 Email Automation for Customer Bookings with OpenAI (ChatGPT) Gmail & Airtable

Lecture 28 Sentiment Analysis with LLMs & Storing Data in Airtable: OpenAI API

Lecture 29 Using Open-Source LLMs with Ollama: Deepseek R1, Llama, Mistral & More

Lecture 30 Integrating Any LLM into n8n via APIs: Deepseek API, Groq, Gemini, Claude & More

Lecture 31 Recap of Automations with LLMs in n8n

Section 5: AI Agents & RAG Chatbots in Your Automations & Email Automation

Lecture 32 What to Expect in This Section

Lecture 33 RAG Agent (Part 1): Automatic Vector Database Updates with Google Drive

Lecture 34 RAG Chatbot (Part 2): AI Agent Node, Vector Database, Embeddings & More

Lecture 35 Email Agent with Sub-Workflows, Vector Database, Google Sheets & More

Lecture 36 The Fastest Way to Build an Email Agent!

Lecture 37 Automatically Summarizing All New Emails of the Day with LLMs at 7AM

Lecture 38 AI-Powered Email Automation: Filtering Messages & Auto-Replying

Lecture 39 Recap & Practical Task

Section 6: Prompt Engineering for AI Agents & AI Automations

Lecture 40 Prompt Engineering for AI Agents & AI Automations (Systemprompts)

Lecture 41 Key Principles of Prompt Engineering for AI Agents & Automations (Atricle)

Section 7: Hosting & Tool Integration: Telegram, WhatsApp, Calendar, Scraping & More

Lecture 42 Hosting n8n: Self-Hosting with Render & Other Options

Lecture 43 Integrating AI Agents & Automations into WhatsApp

Lecture 44 Code Snippet (Dynamic Expression) for WhatsApp as Download

Lecture 45 Using AI Agents & Sub-Workflows with Telegram Trigger Node

Lecture 46 Telegram Agent: Automating Emails, Calendars & More via Voice & Text

Lecture 47 Push the Boundaries: Big AI-Agent that can talk and automate everything

Lecture 48 More Practical Examples. Social Media Automation, Scraping, Crawling & More

Lecture 49 My BEST Tip for Building and Prompting AI-Agents

Lecture 50 Recap

Section 8: Debugging Workflows & Integrating Other Apps/APIs with HTTP Requests & Webhooks

Lecture 51 What to Expect: Debugging & Controlling n8n from Other Apps with Webhooks

Lecture 52 Finding Errors in n8n Workflows with This Automation (Debugging n8n)

Lecture 53 Flowise AI Agent & n8n Webhook: Integrate Sheets with JavaScript & HTTP request

Lecture 54 JavaScript Code for the Flowise Custom Tool as Download (fetch for HTTP-Request)

Lecture 55 One more example for Webhooks and HTTPS request

Lecture 56 Recap of Webhooks, HTTP Requests and Error Trigger Nodes

Section 9: Integrate Apps in Websites and Build a Business with AI Automation & AI Agents

Lecture 57 Overview of This Section: AI Automation as a Business

Lecture 58 What AI Automations & Agents Can Be Sold?

Lecture 59 Market-Ready RAG Bot for Lead Generation (n8n, Pinecone & Google Sheets)

Lecture 60 RAG Lead Bot as a Standalone App with a Published URL

Lecture 61 Integrating RAG Bots into Websites: HTML, WordPress & Custom CSS Branding

Lecture 62 Selling Automations & AI Agents: Marketing, Offers, Price, Sales & More

Lecture 63 Web Scraping with Software – Quickly Find Many Leads

Lecture 64 Summary & Additional Tips

Section 10: Optimizing RAG Chatbots – Data Quality, Chunk Size, Overlap, Embeddings & More

Lecture 65 Optimizing RAG Chatbots: Data Quality, Chunk Size, Overlap, Embeddings & More

Lecture 66 Scraping Webpages and Converting HTML & PDFs to Markdown for Better RAG

Lecture 67 Efficient RAG with LlamaIndex & LlamaParse: Data Preparation for PDFs & CSVs

Lecture 68 Chunk Size and Chunk Overlap for your Embeddings (Better RAG Application)

Lecture 69 Recap: Data Quality, Chunk Size, Overlap, Embeddings for betther RAG

Section 11: Problems, Security & Compliance – Copyright, Data Protection, GDPR & EU AI Act

Lecture 70 First Problems and what will we Learn in this Section?

Lecture 71 Jailbreaks: A Method to Hack LLMs and AI-Agents & Automations with Prompts

Lecture 72 Prompt Injections: Another Security Vulnerability of LLMs, Agents & Automations

Lecture 73 Data Poisoning and Backdoor Attacks

Lecture 74 Copyrights & Intellectual Property of Generated Data from AI Agents

Lecture 75 Privacy & Protection for your own and Client Data

Lecture 76 Censorship, Alignment & Bias in LLMs: Deepseek, ChatGPT, Claude, Gemini, Dolphin

Lecture 77 License of n8n: Can you sell AI agents, AI Automations or the Codebase from n8n?

Lecture 78 EU & US Compliance: GDPR (DSGVO), CCPA/CPRA & the EU AI Act

Lecture 79 Overview of the EU AI Act for ChatBots & AI Agents + CCPA/CPRA (Deep Research)

Lecture 80 DSGVO (GDPR) Compliance for Chatbots & AI Agents + CCPA/CPRA (Deep Research)

Lecture 81 Recap Important Points to Remember

Section 12: What’s Next?

Lecture 82 Recap, Thank You & Next Steps

For entrepreneurs who want to become more efficient, save money, or build an AI business.,For anyone eager to learn something new and gain deep insights into AI automation.,For individuals interested in AI and automation who want to build their own agents.,For developers and data scientists who want to stay on top of GenAI, automation, AI agents, and frameworks.,For anyone looking to automate tasks.

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May 21, 2026

AI & Python Development Megaclass by Vivian Aranha [2025, ENG + Sub]

Year of release : September 2025
Manufacturer : Packt Publishing via O’Reilly Learning
Manufacturer’s website : Code:Copy to clipboard

https://learning.oreilly.com/course/ai-python/9781806679652/

Author : Vivian Aranha
Duration : 57h 57m
Type of distributed material : Video lesson
Language : En + subtitles

Description:
In this 58-hour course, you will gain a comprehensive pathway in AI and Python development, covering programming, data science, machine learning, deep learning, NLP, computer vision, and reinforcement learning. Through hands-on exercises, mini-projects, and real-world deployments, you’ll build scalable AI systems while applying best practices in ethics and governance.
What I will be able to do after this course
Write Pythonic code and manage data using NumPy, Pandas, and Matplotlib
Design supervised, unsupervised, and ensemble machine learning algorithms
Construct deep learning models with TensorFlow, Keras, and PyTorch
Apply CNNs, RNNs, LSTMs, and Transformers to NLP and computer vision tasks
Generate data and solutions using GANs and reinforcement learning frameworks
Course Instructor(s)
Vivian Aranha is a technology professional with nearly two decades of AI expertise, specializing in machine learning, deep learning, and intelligent systems. He mentors aspiring AI developers and simplifies complex concepts into practical, hands-on learning.
Who is it for?
This course is designed for aspiring AI developers, data scientists, and software engineers seeking an end-to-end AI pathway. It is suitable for learners with basic programming knowledge aiming to master advanced AI techniques, deployment practices, and ethical AI principles.
Content
Chapter 01 Week 01 – Python Programming Basics for Artificial Intelligence
Chapter 02 Week 02 – Data Science Essentials for Artificial Intelligence
Chapter 03 Week 03 – Mathematics for Machine Learning and Artificial Intelligence
Chapter 04 Week 04 – Probability and Statistics for Machine Learning and AI
Chapter 05 Week 05 – Introduction to Machine Learning
Chapter 06 Week 06 – Feature Engineering and Model Evaluation
Chapter 07 Week 07 – Advanced Machine Learning Algorithms
Chapter 08 Week 08 – Model Tuning and Optimization
Chapter 09 Week 09 – Neural Networks and Deep Learning Fundamentals
Chapter 10 Week 10 – Convolutional Neural Networks (CNNs)
Chapter 11 Week 11 – Recurrent Neural Networks (RNNs) and Sequence Modeling
Chapter 12 Week 12 – Transformers and Attention Mechanisms
Chapter 13 Week 13 – Transfer Learning and Fine-Tuning
Chapter 14 Week 01 – Python Basics
Chapter 15 Week 02 – Intermediate Python
Chapter 16 Week 03 – Working with Data
Chapter 17 Week 04 – Object-Oriented Programming
Chapter 18 Week 05 – GUI Programming
Chapter 19 Week 06 – Web Development with Python
Chapter 20 Week 07 – Data Science Basics
Chapter 21 Days 50_60 – Intermediate Projects
Chapter 22 Days 61_70 – Advanced Intermediate Projects
Chapter 23 Days 71_80 – AI & Machine Learning Projects
Chapter 24 Machine Learning Algorithms and Implementation in Python
Example files : none
Video format : MP4
Video : AVC, 1920×1080, 16:9, 30,000 fps, 3,000 kbps (0.017 bit/pixel)
Аудио: AAC, 44.1 KHz, 2 channels, 128 kb/s, CBR

⋆🕷- – – – -☽───⛧ ⤝❖⤞ ⛧───☾ – – – -🕷⋆

AI & Python Development Megaclass (2025) (13.88 GB)
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May 21, 2026

Agile Transformation: Organizational Agility

Agile Transformation: Organizational Agility
Duration: 1h 6m | .MP4 1280×720 30 fps(r) | AAC, 48000 Hz, 2ch | 164.29 MB
Genre: eLearning | Language: English

Getting your enterprise to be more Agile is a worthwhile challenge. This course will teach you how to adapt your organization to be more Agile.

More Info:
Agile Transformation: Organizational Agility

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