Category: Tutorials

Last updated 3/2026 MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz, 2 Ch Language: English | Duration: 10h 44m | Size: 15.95 GB​

Learn AI Automation with n8n fast, step by step and effective. From the simple fundamentals to building real AI Systems

What you’ll learn
How to understand n8n from the ground up and confidently build workflows from scratch.
How to set up n8n in the cloud and in a self hosted environment step by step.
How to design logical automation workflows that solve real business problems.
How to integrate AI and large language models into n8n workflows in a practical way.
How to write effective prompts so AI behaves reliably inside automations.
How to connect n8n with external tools and services using APIs.
How to think like an automation builder and turn ideas into working systems.
How to install and setup Anthropic’s Claude Code so it can build n8n workflows for you.
How to test, improve, and stabilize workflows for real world usage.
How to build production ready AI automations that go beyond simple examples.
How to use AI automation skills to support your own business or offer services to clients.

Requirements
You DON’T need a special computer as we do everything in cloud solutions.
I walk you through everything else we need step by step.

Description
Do you want to truly understand AI automation with n8n and build real and useful workflows from scratch, even if you have no coding or technical background?

Then this n8n Masterclass is a great fit for you.

In this course, I teach AI automation from the ground up using n8n as the core tool. Step by step, you learn how to build powerful workflows that automate real business processes. The focus is on practical systems that are actually used in real projects, without theory overload or unnecessary complexity.

My name is Dominik, and I did not come from a traditional developer or IT background. I learned n8n by building AI automations for real companies, and this course is based entirely on that experience. You will not only learn how to build workflows, but also how to think like an automation builder.

We start simple and increase complexity gradually. You learn what n8n is, how it works, and how to set it up in the cloud or in a self hosted environment. From there, we move into practical examples that teach all core n8n concepts in a clear and logical order, so the learning process stays easy to follow.

At the end off the course I added an entire dedicated chapter about Claude Code and how to use it to build n8n workflows. Claude Code is insanely powerful and within that course I give you the entire setup so it can even build n8n workflows for you.

A major focus of this course is working with AI and large language models. You learn how to write effective prompts, how to integrate AI into n8n workflows, and how to combine AI with APIs and external tools to create powerful automations.

The course also teaches how to design automation processes, test and improve workflows, and turn them into stable systems. I also share my personal approach to getting first paying AI automation clients and how to handle early conversations.

By the end of this course, you will understand how to build your own AI automations from scratch and how to apply these skills in real business scenarios.

Who this course is for
The course is for anyone who wants to learn AI automation from scratch using n8n without needing a technical background.
The course is for business owners and freelancers who want to automate processes and save time using AI.
The course is for creators and builders who want to understand how automation workflows actually work behind the scenes.
The course is for anyone who wants to design and build their own AI powered automations instead of copying templates.
The course is for beginners who want a structured and easy to follow introduction to n8n and AI automation.
The course is also for more advanced users who want a clear framework for building better and more reliable automations.
The course is for anyone who wants to turn automation skills into a business opportunity.
The course is for anyone who wants to get an introduction to building n8n workflows with Claude Code.

Must-Have Tools for IT Support: Remote Access, AI, and Troubleshooting
.MP4, AVC, 1280×720, 30 fps | English, AAC, 2 Ch | 1h 1m | 134 MB
Instructor: Jason Gooley​

In today’s fast-paced digital world, IT support professionals need both strong technical knowledge and the right tools to deliver fast, secure, and effective support from anywhere. This comprehensive course teaches how to use essential IT support and remote desktop tools to manage devices remotely, solve complex issues, and provide real-time support. It covers how to use popular remote access tools such as RDP (Remote Desktop Protocol), Cisco Webex, TeamViewer, Virtual Network Computing (VNC), and pcAnywhere.

Discover how each tool fits different support scenarios. Learn how to use AI-powered support tools, technical forums, and search engines to research and resolve IT problems more efficiently, exploring how narrow AI, predictive AI, and generative AI can transform help-desk operations. Additionally, review security, privacy, and ethical factors to consider when using public data sources and remote access tools. Whether you are just starting in IT support or looking to enhance your skills with modern tools and best practices, this course provides techniques you can apply right away.

Learning objectives

[]Describe the differences and similarities of leading remote access and troubleshooting tools.
[]Explain the process involved to set up and use common remote access tools for technical support tasks.
[]Initiate and manage a remote session to help troubleshoot and resolve user issues.
[]Apply search strategies using knowledge base articles, technical forums, and search engines to diagnose and fix IT problems.
[]Describe the role of artificial intelligence in IT support, including the use of narrow, predictive, and generative AI tools.
[]Evaluate the security, privacy, and ethical risks associated with using public data sources and AI for IT troubleshooting.

Homepage

Moonvalley Ai Text-To-Video: Full Masterclass, Make Ai Video
Published 12/2023
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.50 GB | Duration: 1h 9m​

Moonvalley AI Text to Video Full Masterclass | AI Text to Video Mastery: Moonvalley | Text to Viral AI Video Made Easy

What you’ll learn

Become a master of AI-powered storytelling: Gain the skills and confidence to create stunning videos that captivate your audience and leave a lasting impression

Craft narratives that come alive: Master the art of weaving words that transform into captivating on-screen stories, using powerful techniques to hook and hold

Structure your masterpiece: Learn to build engaging video journeys, wielding story structure, pacing, and transitions like a pro to ensure your message resonate

Unlock the power of animation: Explore techniques for animating static scenes and characters, adding dynamic movement and visual interest to your videos.

Leverage video marketing strategies: Learn how to use your AI-powered videos to reach a wider audience, grow your online presence, and achieve your marketing go

Storytelling for different platforms: Adapt your narratives and video styles to different platforms like social media, YouTube, or online courses, maximizing yo

Requirements

PC/Laptop

Internet Connection

Description

Moonvalley AI Text-to-Video: Full Masterclass – Your Gateway to AI-Powered StorytellingStep into a world where your words paint the picture, and cutting-edge AI brings your vision to life. Immerse yourself in the Moonvalley AI Text-to-Video: Full Masterclass, and unlock the power to create stunning videos from the ground up, fueled by the limitless canvas of your imagination.Forget the constraints of traditional filmmaking. With Moonvalley AI, your narrative becomes the blueprint, your vision the director, and the AI engine your tireless collaborator. This comprehensive masterclass unveils the secrets within, empowering you to:Craft captivating video narratives: Master the art of weaving words that leap off the screen, using powerful storytelling techniques to captivate your audience.Unleash the power of AI-powered visuals: Explore a vast library of pre-rendered scenes, characters, and effects, and learn to manipulate them to bring your story to life with breathtaking visuals.Fine-tune every detail: Take control over every aspect of your video, from camera angles and lighting to music and sound effects, ensuring your creation is a true masterpiece.Go beyond the basics: Dive deeper into advanced techniques, incorporating custom assets, fine-tuning AI outputs, and even adding your own voice narration.But this masterclass transcends mere technical know-how. It’s a journey of creative exploration, where you’ll discover the magic of transforming your ideas into captivating visual experiences. You’ll learn to harness the power of AI to amplify your storytelling, evoke emotions, and leave a lasting impression on your viewers.Whether you’re a seasoned filmmaker, an aspiring entrepreneur, or simply a passionate storyteller, Moonvalley AI Text-to-Video opens a door to a world of limitless possibilities. This full masterclass equips you with the knowledge and skills to unlock your creative potential and become a master of AI-powered video creation.Ready to unleash the power of your imagination and bring your stories to life? Dive into the Moonvalley AI Text-to-Video: Full Masterclass today and embark on a transformative journey of creative empowerment.Remember, the focus title remains "Moonvalley AI Text-to-Video: Full Masterclass." Use it strategically throughout your description to keep the reader engaged and focused on the core topic.

Overview

Section 1: How to Launch Moonvalley AI ?

Lecture 1 How to Launch Moonvalley AI ?

Section 2: How to use Moonvalley AI ?

Lecture 2 How to use Moonvalley ?

Section 3: How to Use Any Image as Reference to Get Consistent result ?

Lecture 3 How to Use Any Image as Reference to Get Consistent result ?

Section 4: How to Create AI Voice for Our Videos ?

Lecture 4 How to use ElevenLabs to Generate AI Voice ? ( Complete Guide )

Lecture 5 Lets Type Our Script and Generate Voice For Our Project

Section 5: How to Edit AI Videos ?

Lecture 6 How to Edit AI videos To get Viral ?

The Aspiring Filmmaker: Crave to bring your narratives to life on screen but lack the traditional filmmaking resources? Moonvalley AI equips you with all the tools to craft stunning videos, even without a camera crew,The Budding Entrepreneur: Looking to elevate your brand story and engage your audience with captivating visuals? Master AI-powered video creation to capture attention, build trust, and promote your business in a whole new way.,The Creative Soul: Do you have a story burning inside you, begging to be shared with the world? Moonvalley AI gives you the power to transform your words into stunning visuals, regardless of your artistic background or technical skills.,The Time-Crunched Creator: Short on time but big on ideas? Moonvalley AI’s intuitive interface and pre-rendered assets allow you to create professional-quality videos efficiently, saving you precious time and resources.,The Passionate Educator: Want to make learning exciting and interactive? Explore how AI-powered videos can bring textbooks to life, engage students in new ways, and make even the most complex concepts clear and captivating.

Monitoring, Observability And Alerting With Prometheus
Published 2/2024
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.94 GB | Duration: 5h 4m​

Infrastructure Monitoring,Observability and Alerting with Prometheus, Prometheus Exporters, PromQL and Grafana

What you’ll learn

Infrastructure Monitoring and Observabilitity overview

What is Prometheus, Prometheus Architecture and its components

Prometheus Configuration files, Scrape Config ,Alerting Rules and Alert Manager

PromQL Tutorial- Vectors, data types, selectors and matchers, Aggregrators, Functions

Prometheus Metrics Types – Counter, Histogram, Gauge

Monitoring Linux, Windows, Docker, Jenkins and Kubernetes using Prometheus and Grafana

Requirements

Basic Linux Knowledge

Description

Infrastructure Monitoring, Observability, and Alerting with Prometheus, Prometheus Exporters, Prometheus Metrics, PromQL, and GrafanaSection 1: Introduction to Monitoring and ObservabilityWhat is Infrastructure MonitoringWhat is Observability Difference Between Monitoring & ObservabilitySection 2: What is Prometheus and Prometheus Architecture and its componentsPrometheus ArchitecturePrometheus Architecture ComponentsSection 3: Prometheus InstallationHow to Install Prometheus on UbuntuInstall and Configure the latest Prometheus on Ubuntu 22.04 LTSHow to Install Prometheus using Docker ContainerHow to Install Prometheus on Amazon Linux 2Section 4: Prometheus Configuration FilesPrometheus Configuration Files with ExamplesPrometheus Scrape Configuration with ExamplesPrometheus Recording Rules with ExamplesAlerting Rules in Prometheus with ExamplesAlerting with Alertmanager in Prometheus with ExamplesPush Gateway in Prometheus with ExamplesSection 5: Prometheus ExportersWhat are Exporters in Prometheus Node Exporter in Prometheus with ExamplesWMI Exporter in Prometheus with ExamplesSection 6: PromQL TutorialVectors in Prometheus with ExamplesPromQL Data TypesBinary Operators in PrometheusSectors and matchers in PrometheusSection 7: Metrics in Prometheus and its TypesWhat are metrics in Prometheus and TypesPrometheus Metrics Types – Counter with ExamplesPrometheus Metrics Types – Gauge with ExamplesPrometheus Metrics Types – Histogram with ExamplesPrometheus Metrics Types – Summary with ExamplesSection 8: Adding Prometheus as a Data Source in GrafanaHow to add Prometheus Data Source in GrafanaSection 9: Monitoring Windows and Linux Servers using Prometheus and GrafanaMonitor Linux server Prometheus, Node Exporter, and GrafanaMonitoring Linux and Windows using Prometheus and GrafanaMonitoring Windows server with Prometheus and Grafana with WMI exporterSection 10: Monitoring Docker Containers with Prometheus and GrafanaMonitor Docker Containers with Prometheus and GrafanaSection 11: Monitoring Jenkins Jobs with Prometheus, Grafana, and Node ExporterMonitoring Jenkins Jobs with Prometheus, Grafana, and Node ExporterSection 12: Monitoring Kubernetes Cluster with Prometheus and Grafana

Overview

Section 1: Prometheus Architecture and Prometheus Architecture Components

Lecture 1 Prometheus Architecture

Lecture 2 Prometheus Architecture Components Explained

Section 2: Prometheus Installation

Lecture 3 What is Prometheus and How to Install Prometheus on Amazon Linux 2

Lecture 4 Install and Configure the latest Prometheus on Ubuntu 22.04 LTS

Lecture 5 How to Install Prometheus using Docker Container

Section 3: Prometheus Configuration Files

Lecture 6 Prometheus Configuration Files

Lecture 7 Recording Rules in Prometheus with Example

Lecture 8 Alerting Rules in Prometheus with Example

Lecture 9 Alert Manager in Prometheus and How to Send Alert to Gmail from Prometheus

Section 4: Prometheus Exporters

Lecture 10 What are Prometheus Exporters and How does Exporters works in Prometheus

Lecture 11 Node Exporters in Prometheus and How to Install Node Exporter on Amazon Linux 2

Lecture 12 WMI Exporters in Prometheus, How it works and Install WMI Exporter on Windows

Section 5: PromQL(Prometheus Query Language) Tutorial

Lecture 13 What is PromQL and Data Types in PromQL

Lecture 14 Binary Operators in Prometheus with Examples

Lecture 15 Selectors and Matchers in Prometheus with Examples

Lecture 16 Aggregation Operators in Prometheus with Examples

Lecture 17 changes, deriv, and predict_linear functions in Prometheus with Example

Section 6: Monitoring Windows and Linux Servers using Prometheus and Grafana

Lecture 18 Monitoring Linux Server with Prometheus, Node Exporter and Grafana- Ubuntu 22.04

Lecture 19 Monitoring Windows Server with Prometheus and Grafana with WMI Exporter

Lecture 20 Monitoring Linux and Windows Server using Prometheus and Grafana

Section 7: Monitoring Jenkins Jobs with Prometheus, Grafana and Node Exporter

Lecture 21 Monitoring Jenkins Jobs with Prometheus, Grafana and Node Exporter

DevOps, SRE, Platform Engineering, IT Admin, Application Engineer

Year of release : 1/2025
Manufacturer : UDEMY, Maximilian Schwarzmüller
Author : Maximilian Schwarzmüller
Duration : 17:31:50
Type of material given : Video lesson
Language : En
Subtitles : En
Description:
What you’ll learn
Learn what document databases are and how data is organized with MongoDB

Learn how to perform CRUD operations with MongoDB

Don’t stop at the basics – learn all about writing complex MongoDB queries, in-depth and with practical examples!

Write efficient and well-performing queries to fetch data in the format you need it

Use all features MongoDB offers you to work with data efficiently
Requirements
NO prior knowledge on databases (of any kind) is required

General web development or mobile development knowledge will help you but is not a must-have

You can use any operating system – Windows, macOS, Linux, it’ll all work!
Description
Join this bestselling MongoDB course to learn all about this extremely popular database and query language from the ground up, in great detail and with many practical examples!

MongoDB is one of the most important NoSQL databases you can work with. It’s extremely popular and MongoDB developers are in high demand!

No matter if you’re building web applications, mobile applications or any other kind of application or if you’re a data scientist – you’ll need to work with data. Storing data, querying it efficiently and minimizing complexities whilst optimizing performance are crucial tasks.

MongoDB makes working with data simple – it’s built on a philosophy that prioritizes performance and efficiency.

In this course, you learn all about MongoDB from scratch. No prior MongoDB or database experience is required!
In detail, you’ll learn:
How to install and use MongoDB locally and in the cloud (MongoDB Atlas)

How to perform CRUD (Create, Read, Update, Delete) operations on MongoDB databases

How to filter for data efficiently

How to work with both the Mongo Shell and drivers (e.g. Node.js driver)

How to increase performance by using indexes (and how to use the right indexes!)

How to use the amazing "Aggregation Framework" that’s built into MongoDB

What replica sets and sharding are

How to use MongoDB Atlas – the cloud solution offered by MongoDB

How to use the serverless platform (Stitch) offered by MongoDB

And much more!
This course is a hands-on course – you’ll learn by writing code/ commands. We’ll work on a wide variety of example data and use-cases and by the end of the course, you’ll have all the knowledge you need to work with MongoDB in your next project!
This course is for you, no matter which programming language you plan on using, you’ll learn a uniform way of interacting with MongoDB that can be easily applied to any language.
This course is for you:
if you’re brand-new to MongoDB and databases in general

if you got some basic database or even MongoDB experience – in this course, there are different entry points you can choose from!

if you are a web or mobile app (or desktop app) developer who considers using MongoDB

if you’re working in a team that considers using MongoDB (or already does use it)

if you are primarily using SQL-based databases so far and you want to explore the most popular NoSQL alternative
This course is NOT for you:
if you’re looking for a guide on administrating MongoDB servers => This course focuses on the commands/ queries you write, it’s NOT an administration course. I will show (in detail) how to deploy a ready-to-use cloud MongoDB solution that follows best practices though.
Who this course is for:
Developers or data scientists who plan on (or are already) working with MongoDB

Everyone who’s interested in NoSQL databases

Both beginner and advanced MongoDB users who want to explore all the core features
Format Video : mp4
Video : avc, 1280×720, 16: 9, 30000k / s, 400 kb / s
Audio : AAC, 48.0KGC, 62.7KB / s, 2 audio

Udemy – MongoDB – The Complete Developer’s Guide 2025 (2025-1) (3.63 GB)

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Modern SQL Mastery: Beginner to Expert with PostgreSQL
Published 1/2026
Duration: 3h 36m | .MP4 1920×1080 30fps(r) | AAC, 44100Hz, 2ch | 4.18 GB
Genre: eLearning | Language: English​

Master SQL queries, joins, CTEs, window functions, and build real database projects, joins, CTEs, Hands-on SQL training

What you’ll learn

• Understand database fundamentals including tables, schemas, rows, and columns
• Learn all SQL command types such as DDL, DML, DCL, and TCL
• Perform data analysis using aggregate functions and GROUP BY with HAVING
• Write efficient queries using SELECT, WHERE, ORDER BY, LIMIT, and FETCH
• Master all types of SQL joins including INNER, LEFT, RIGHT, FULL, and CROSS
• Use subqueries, Common Table Expressions (CTEs), and window functions
• Create views, materialized views, stored procedures, and functions
• Build an end-to-end SQL project using real retail and e-commerce datasets

Requirements

• No prior SQL or database knowledge is required
• A basic understanding of computers and files is enough
• Willingness to install PostgreSQL and DBeaver (guided step-by-step)
• No programming background is required
• Beginner-friendly explanations with practical examples

Description
This course is a complete and practical guide to masteringModern SQL using PostgreSQL, designed to take you from absolute beginner to advanced level with confidence.

Unlike traditional SQL tutorials that focus only on syntax, this course emphasizeshands-on learning. You will build real databases, write real queries, and understand how SQL is used in real-world data analytics and engineering scenarios.

By the end of this course, you will be able to confidently work with SQL in professional environments and solve real business problems using data.

What You Will Learn

Understand databases, schemas, tables, rows, and columns

Learn SQL command categories including DDL, DML, DCL, and TCL

Write powerful queries using SELECT, WHERE, ORDER BY, LIMIT, and FETCH

Perform data analysis using aggregate functions like COUNT, SUM, AVG, MIN, and MAX

Work with GROUP BY and HAVING for analytical queries

Master all types of JOINs including INNER, LEFT, RIGHT, FULL, and CROSS

Write efficient subqueries and Common Table Expressions (CTEs)

Use window functions for advanced analytics

Create and manage views and materialized views

Understand stored procedures, functions, and exception handling

Build an end-to-end SQL project using retail and e-commerce datasets

Why This Course Is Different

UsesPostgreSQL, one of the most powerful and widely used open-source databases

Focuses onreal-world SQL usage, not just theory

Includes a complete SQL project to simulate real industry scenarios

Ideal for learners who want practical, job-ready SQL skills

Who this course is for:

• Beginners who want to learn SQL from scratch
• Students preparing for data analyst or data engineer roles
• Professionals who want to improve their SQL skills for work
• Data analysts looking to strengthen real-world query writing
• Data engineers working with PostgreSQL databases
• Developers who want better database and SQL understanding
• Anyone preparing for SQL interviews or technical assessments
• Learners who prefer hands-on, project-based SQL learning

More Info

Year of release : 2022
Manufacturer : Udemy
Manufacturer’s website : Code:Copy to clipboard

https://www.udemy.com

Автор : And Pipi Rahayu Study Program
Duration : 11h
Type of material distributed : Video lesson
Language : En

Description : Please do not leave the distribution, I will not be able to maintain the distribution forever.
Share the freebie with other people, don’t leave the distribution.
Encourage other people to switch to roottracker.
Course in En. Added En subtitles using speech to text for Adobe Premier Pro.
Modern JavaScript is really a wide and moving target. You may have learned JavaScript five years ago and some of those techniques may be considered old by today’s standards, just as you may know JavaScript techniques from 20+ years ago that are still considered modern best practices. My intent is to take you on a guide through JavaScript’s various ins and outs to learn modern JavaScript as it’s known today.
In this course I will explain:
The Steps to Becoming a JavaScript Developer
the tools and the knowledge needed
JavaScript basics
What is JavaScript
Script loading strategies
Variables
Constants
Operators
Conditionals
Events
Strings
Functions
Strings and arrays
Interaction with HTML pages
Use JavaScript to interact with a form
Manage the events in input elements in a form with JavaScript
Use JavaScript to change the CSS style of HTML elements
The document Object
Troubleshooting JavaScript
JavaScript building blocks
Conditionals
Looping code
Functions
More on events
Event listeners
Event objects
Event properties and methods
JavaScript objects
Object properties and methods
Objects within objects
Set and modify object members
Create objects
JavaScript object prototypes
The prototype chain
Shadowing properties
Setting prototypes
Own properties of prototypes
Prototypes and inheritance
Object-oriented programming in JavaScript
Classes and instances
Inheritance
Encapsulation
OOP in JavaScript
Working with JSON data
Asynchronous JavaScript: traditional
Why Asynchronous JavaScript
Event handlers with Asynchronous JavaScript
Asynchronous JavaScript and Callbacks
Asynchronous JavaScript: modern
Promises
The fetch() API
Promise chaining
Handle errors with promises
Promise Terminologies
Combination of multiple promises
async and await
Workers
Client-side web APIs
What are APIs?
Common browser APIs and common third-party APIs
How do APIs work?
Document Object Model (DOM) APIs
APIs for fetching data from a server
Third party APIs
At the end we will see some applications to verify what we have learned in this course.

Content
Chapter 1-Introduction
Chapter 2-Steps to Becoming a JavaScript Developer
Chapter 3-JavaScript Basics
Chapter 4-Strings and Arrays
Chapter 5-Interaction with HTML Pages
Chapter 6-Troubleshooting JavaScript
Chapter 7-JavaScript building Blocks
Chapter 8-More on events
Chapter 9-JavaScript Objects
Chapter 10-JavaScript Object Prototypes
Chapter 11-Object oriented Programming in JavaScript
Chapter 12-Working with JSON data
Chapter 13-Asynchronous JavaScript traditional
Chapter 14-Asynchronous JavaScript modern
Chapter 15-Client side Web APIs
Chapter 16-Applications
Example files : present
Video format : MP4
Video : H265 1280×720 16:9 30k/sec 300 kbit/sec
Audio : AAC 48 kHz 128 kbps 2 channels

Modern JavaScript and Software Front-End Development (2 GB)
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https://rapidgator.net/file/3fd4783b59fbc9bc0978c047e9a5d2ab/Modern.JavaScript.and.Software.Front-End.Development.part2.rar

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Release year : January 2025
Manufacturer : Published by Pearson Via O’Reilly Learning
Author : Sinan Ozdemir
Duration : 5h 37m
Type of material given : Video lesson
Language : En + subtitles
Description:
Get started with automated AI agents
Overview
Modern Automated AI Agents introduces you to the concept of automated agents. It then helps you build a solid understanding of how to design, build, and optimize AI agents to tackle real-world challenges.
About the Instructor
Sinan Ozdemir is the founder and CTO of LoopGenius, where he uses state-of-the-art AI to help people create and run their businesses. Sinan is a former lecturer of Data Science at Johns Hopkins University and the author of multiple textbooks and videos on data science and machine learning. Additionally, he is the founder of the recently acquired Kylie.ai, an enterprise-grade conversational AI platform with RPA capabilities. He holds a master’s degree in Pure Mathematics from Johns Hopkins University and is based in San Francisco, California.
Learn How To
Build & use AI agents
Evaluate AI agent frameworks
Start to use CrewAI
Design multi-step workflows with LangGraph
Use large language models (LLMs)
Integrate existing and custom tools
Use thought, action, observation, and response components
Test and evaluate agents, their responses, backstories, definitions, and rules
Add planning and reflection to agents to bolster performance
Who Should Take This Course
Developers, data scientists, and engineers who are interested in building intelligent, autonomous AI agents capable of solving complex problems and adapting to dynamic environments
Course Requirements
Python 3 proficiency with some experience working in interactive Python environments including Notebooks (Jupyter/Google Colab/Kaggle Kernels)
Comfortable using the Pandas library and either Tensorflow or PyTorch
Understanding of ML/deep learning fundamentals including train/test splits, loss/cost functions, and gradient descent
Content
Introduction
Lesson 1 Introduction to AI Agents
Lesson 2 Under the Hood of AI Agents
Lesson 3 Building an AI Agent
Lesson 4 Testing and Evaluating Agents
Lesson 5 Expanding on ReAct with Planning and Reflection
Lesson 6 Advanced Applications and Future Directions
Summary
Example files : no
Format Video : mp4
Video : AVC, 1280 x 720, 16: 9, 30.000 FPS, 3,000 KB/S (0.017 Bit/Pixel)
Аудио: AAC, 44.1 KHz, 2 channels, 128 kb/s, CBR

Modern Automated AI Agents Building Agentic AI to Perform Complex Tasks (1.7 GB)

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Last updated 7/2024 Created by Manifold AI Learning ® MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + srt | Duration: 246 Lectures ( 39h 42m ) | Size: 13.8 GB​

What you’ll learn:
Develop a solid foundation in Python, tailored for MLOps applications.
Streamline Machine Learning processes using Python’s powerful capabilities.
Leverage Python for effective data manipulation and analysis in Data Science.
Understand how Python enhances the entire data science lifecycle.
Master version control using Git for collaborative development.
Learn to manage and track changes efficiently within MLOps projects.
Dive into the art of packaging Machine Learning models for easy deployment.
Ensure models are reproducible and deployable in diverse environments.
Effectively manage and track Machine Learning experiments using MLflow.
Utilize MLflow for enhanced experiment tracking and management.
Acquire essential skills in YAML for MLOps configuration and deployment.
Gain practical experience in writing and interpreting YAML files.
Explore Docker and its role in containerizing Machine Learning applications.
Understand the advantages of containerization for efficient MLOps.
Develop Machine Learning applications with FastAPI for efficient and scalable deployments.
Explore Streamlit and Flask for creating interactive web applications.
Implement Continuous Integration and Continuous Deployment pipelines for MLOps.
Automate development, testing, and deployment of ML models.
Gain a solid understanding of the Linux operating system.
Explore how Linux is essential for both DevOps and Data Scientists in MLOps.
Dive into Jenkins, an open-source automation server.
Learn to set up and configure Jenkins for automating MLOps workflows.
Develop insights into effective monitoring and debugging strategies for MLOps.
Utilize tools and techniques to identify and address issues in ML systems.
Set up continuous monitoring for MLOps using Prometheus and Grafana
Enhance observability in Machine Learning applications.
Extend Docker skills by mastering Docker Compose.
Learn to deploy multi-container applications seamlessly.
Explore tools and strategies for ongoing performance monitoring in MLOps.
Proactively address issues in production ML systems.
Utilize WhyLogs for efficient monitoring and logging of ML data.
Enhance the observability and traceability of ML systems.
Understand crucial steps for maintaining and updating ML models in a production environment.
Implement best practices for ensuring the long-term success of deployed ML systems.

Requirements:
Familiarity with programming concepts is preferred, but we cover in our course as well
Some knowledge of data manipulation and analysis will be beneficial.
Basic understanding of version control concepts, preferably with Git – will be beneficial
Enthusiasm for the intersection of Machine Learning and DevOps practices.
Participants should have access to a computer with a stable internet connection for viewing video content and engaging in practical exercises.

Description:
Welcome to our extensive MLOps Bootcamp (AI Ops Bootcamp), a transformative learning journey designed to equip you with the skills and knowledge essential for success in the dynamic field of Machine Learning Operations (MLOps). This comprehensive program covers a diverse range of topics, from Python and Data Science fundamentals to advanced Machine Learning workflows, Git essentials, Docker for Machine Learning, CI/CD pipelines, and beyond.Curriculum Overview:1. Python for MLOpsive into the fundamentals of Python tailored specifically for MLOps.Explore Python’s role in streamlining and enhancing Machine Learning processes.Develop proficiency in leveraging Python for effective MLOps practices.2. Python for Data Science:Uncover the power of Python in the context of Data Science.Learn essential data manipulation and analysis techniques using Python.Understand how Python enhances the entire data science lifecycle.3. Git and GitHub Fundamentals:Master the essentials of version control with Git.Understand how GitHub facilitates collaborative development in MLOps.Learn to manage and track changes effectively within MLOps projects.4. Packaging the ML Modelselve into the art of packaging Machine Learning models.Explore different packaging techniques and their implications.Ensure your ML models are easily deployable and reproducible.5. MLflow – Manage ML Experiments:Learn to effectively manage and track Machine Learning experiments.Understand the features and benefits of MLflow for experiment tracking and management.Implement MLflow in your MLOps projects for enhanced experimentation.6. Crash Course on YAML:Acquire a solid foundation in YAML, a key configuration language.Learn how YAML is used in MLOps for configuration and deployment.Gain practical skills in writing and interpreting YAML files.7. Docker for Machine Learning:Explore Docker and its role in containerizing Machine Learning applications.Understand the advantages of containerization for MLOps.Learn to build and deploy Docker containers for Machine Learning projects.8. Build MLApps using FastAPI: Dive into FastAPI, a modern, fast web framework for building APIs.Learn to develop ML applications using FastAPI for efficient and scalable deployments. Implement best practices for building robust MLApps.9. Build MLApps using Streamlit: Explore Streamlit, a powerful framework for creating interactive web applications. Develop hands-on experience in building MLApps with Streamlit. Understand how Streamlit enhances the user interface for Machine Learning applications.10. Build MLApps using Flask:Gain proficiency in Flask, a popular web framework for Python. Learn to build and deploy Machine Learning applications using Flask. Understand the integration of Flask with MLOps workflows.11. CI/CD for Machine Learning: Explore Continuous Integration and Continuous Deployment (CI/CD) pipelines in the context of MLOps. Implement automation to streamline the development, testing, and deployment of ML models. Learn to build robust CI/CD workflows for Machine Learning projects.12. Linux Operating System for DevOps and Data Scientists: Understand the fundamentals of the Linux operating system. Explore how Linux is essential for both DevOps and Data Scientists in MLOps. Gain practical skills in working with Linux for MLOps tasks.13. Working with Jenkins: Dive into Jenkins, an open-source automation server.Learn to set up and configure Jenkins for automating MLOps workflows. Understand how Jenkins enhances the efficiency of continuous integration and deployment in MLOps.14. Monitoring and Debugging of ML System: Gain insights into effective monitoring and debugging strategies for MLOps. Learn tools and techniques to identify and address issues in Machine Learning systems.Implement best practices for maintaining the health and performance of ML systems.15. Continuous Monitoring with Prometheus: Explore Prometheus, an open-source monitoring and alerting toolkit. Learn to set up continuous monitoring for MLOps using Prometheus. Understand how Prometheus enhances observability in Machine Learning applications.16. Deploy Applications with Docker Compose: Extend your Docker skills by mastering Docker Compose. Learn to deploy multi-container applications seamlessly using Docker Compose.Understand how Docker Compose enhances the deployment of complex MLOps architectures.17. Continuous Monitoring of Machine Learning Application: Dive into continuous monitoring practices specifically tailored for Machine Learning applications. Explore tools and strategies to ensure ongoing performance monitoring in MLOps.Implement solutions for proactively addressing issues in production ML systems.18. Monitor the ML System with WhyLogs: Explore WhyLogs, a data logging library for Machine Learning.Learn how WhyLogs facilitates efficient monitoring and logging of ML data.Implement WhyLogs to enhance the observability and traceability of your ML system.19. Post Productionizing ML Models: Understand the crucial steps involved in post-productionizing Machine Learning models. Explore strategies for maintaining and updating ML models in a production environment. Gain insights into best practices for ensuring the long-term success of deployed ML systems.Conclusion: Embark on this comprehensive MLOps Bootcamp to transform your skills and elevate your proficiency in the dynamic and ever-evolving field of Machine Learning Operations. Whether you are a seasoned professional or just starting your journey in MLOps, this program provides the knowledge, tools, and practical experience needed to succeed in implementing robust and efficient Machine Learning workflows. Join us and become a master of MLOps, ready to tackle the challenges of the modern AI landscape with confidence and expertise.

Who this course is for:
Data scientists seeking to extend their skills into the operational aspects of deploying and maintaining machine learning models.
Software developers interested in mastering the tools and practices for integrating machine learning into real-world applications.
DevOps professionals aiming to specialize in MLOps and enhance their proficiency in deploying and managing machine learning systems.
Data engineers looking to broaden their skill set by incorporating MLOps practices into data pipelines.
IT professionals wanting to understand the integration of machine learning models within operational workflows.
Individuals passionate about the latest advancements in technology and eager to explore the practical aspects of MLOps.
Entrepreneurs and business professionals seeking to understand how MLOps can drive innovation and competitive advantage in their organizations.
Students and researchers in the fields of computer science, data science, and related disciplines looking to expand their knowledge in MLOps.
Individuals transitioning into roles that involve machine learning operations and deployment.
Enthusiasts who are keen to explore the convergence of machine learning and operations, regardless of their current role or background.

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