Advanced Model Deployment Learning Path

Statistical Learning

State-of-the-Art Machine Learning Papers Implementation

Learn Transformers, explained: Understand the model behind GPT, BERT, and T5

Machine Learning Model Deployment

Welcome to the era of Artificial Intelligence, where everything is rapidly evolving. In this dynamic era, it's crucial to enhance your skills by acquiring the most essential, cutting-edge knowledge that is currently in high demand in the market: Artificial Intelligence. This course takes you on a comprehensive learning journey, delving into the most advanced concepts in AI, such as Computer Vision Generative A.IRNN Variational Autoencoder PyTorch With Python and C++Numpy and Pandas And lot of more things..There are numerous cutting-edge concepts in high demand at the moment. I am formerly engaged in the Trustline security limited organization, where we harness real-world experience to create resilient AI solutions. I leverage this experience to instruct you on crafting advanced, industry-ready, robust A.I.In this course, we embark on a journey to develop AI across various domains, including stock market analysis, human face generation, image classification, and more. This course not only reinforces your programming and mathematical fundamentals but also equips you to build AI solutions in two distinct languages: Python and C++. This proficiency in both languages is a rare and valuable asset in the deep learning space.Furthermore, we explore best practices that enable the systematic creation of AI solutions. We delve into the theory of MLOPS (Machine Learning Operations), enhancing your capabilities and making your talents shine brightly in the competitive AI market.We also explore how Chat GPT LLM can enhance and expedite our AI development in the realm of Data Science. This section is particularly engaging, as Chat GPT serves as a valuable assistant in addressing repetitive and logic-free tasks, making our AI journey even more exciting and efficient.At the

Bienvenido a este curso 100% práctico y aplicado en el que podrás aprender de forma intuitiva, guiada y paso-a-paso el despliegue de modelos Deep Learning para ambientes de Desarrollo y principalmente para Producción en escenarios de alto desempeño usando la librería TensorFlow 2.0 y la creación de servicios REST.Estructura temática:¿Por qué desplegar modelos Deep Learning?Despliegue en Desarrollo vs Producción Servidores de despliegue en la Nube: Google Cloud Platform (GCP) y CentOS Despliegue de modelos en la nube como Servicio Web REST desde cero (AP Is)Gestor de contenedores Docker para despliegues en Producción (Docker Swarm, TensorFlow Serving) Implementación de llamadas al Servicio Web desde cero Consideraciones técnicas para el despliegue de modelos Deep Learning Dev Ops y Machine Learning / ML Ops | IA Ops | XX Ops Interoperabilidad de modelos: ONNX.Despliegue Customizado vs Plataformas100% práctico:El curso prioriza el desarrollo de algoritmos en sesiones de laboratorio y actividades de programación 100% hands-on con los que podrás reproducir cada una de las líneas de código con explicaciones muy bien detalladas, sin descuidar los fundamentos teóricos de cada uno de los conceptos descritos.Herramientas:Todas las herramientas necesarias para el curso se podrán configurar directamente en la nube de Google; por tanto, no será necesario invertir tiempo en instalaciones de herramienta de forma local.El curso se desarrolla con las herramientas más populares y de alta madurez del ecosistema de Python 3.0 como:TensorFlow 2.0Tensor Flow Serving Flask

The course "AI Agents for Everyone and Artificial Intelligence Bootcamp" is designed to demystify the world of intelligent systems, making it accessible to learners of all levels. Whether you're a curious beginner or an aspiring AI developer, this course provides a comprehensive foundation in the development, deployment, and application of AI agents across various domains. With a strong emphasis on hands-on learning, participants will explore state-of-the-art technologies such as machine learning, natural language processing (NLP), and advanced frameworks like AutoGPT, IBM Bee, Lang Graph, and CrewAI.Throughout the course, learners will gain a deep understanding of how AI agents function, from basic reflex agents to advanced collaborative systems. You'll learn about the core principles that govern intelligent agents, including decision-making, adaptability, and autonomy. By understanding these foundations, you will be equipped to create AI agents that can perceive their environment, make informed decisions, and perform complex tasks. The course also delves into the critical technologies that power AI agents, such as machine learning algorithms for predictive insights, NLP techniques for conversational AI, and robotics integration for automation.One of the course’s unique aspects is its focus on practical application. You will work on hands-on projects to develop and deploy AI agents in real-world scenarios. From creating collaborative systems with CrewAI to implementing stateful interactions using Lang Graph, you’ll get valuable experience with cutting-edge tools and frameworks. Additionally, the course explores the transformative potential of AI agents in industries such as healthcare, finance, business operations, entertainment, and IoT, providing actionable insights into their role in shaping the future.Ethics and societal impact are integral to this learning experience. The course examines the ethical considerations and regulatory challenges surrounding AI agents,

This Complete Prompt Engineering Bootcamp 2025 is your definitive guide to mastering the art and science of working with large language models. Whether you're a developer, content creator, business professional, or AI enthusiast, this course will transform how you interact with AI systems.What Makes This Course Different?Unlike surface-level tutorials, this bootcamp combines real-world AI engineering experience with proven frameworks used in production environments. You'll learn the exact techniques that professional AI engineers use to build reliable, high-performance AI systems.What You'll Master:Foundation to Advanced Prompting: Start with core principles and progress to expert-level techniques including prompt optimization strategies that deliver measurable results.Production Best Practices: Learn debugging techniques, testing frameworks, and how to deploy AI solutions that scale. Discover how to measure success with concrete metrics.Real-World Projects: Apply your skills through hands-on projects that mirror actual industry use cases. Build portfolio pieces that demonstrate your expertise to employers.By the end of this bootcamp, you'll be able to:Design and implement sophisticated prompt strategies for any use case Build and deploy AI agents that automate complex workflows Create RAG systems that enhance LLM capabilities with custom knowledge Optimize AI systems for performance, accuracy, and cost-efficiency Debug and troubleshoot AI applications like a professional engineer Who This Course Is For:Developers wanting to add AI engineering skills to their toolkitAI enthusiasts ready to move beyond basic ChatGPT usage Product managers who need to understand AI capabilities deeply Content creat

In this course you will learn how to deploy Machine Learning Deep Learning Models using various techniques.&nbsp; This course takes you beyond model development and explains how the model can be consumed by different applications with hands-on examples Course Structure:Creating a Classification Model using Scikit-Learn Saving the Model and the standard Scaler Exporting the Model to another environment - Local and Google Colab Creating a REST API using Python Flask and using it locally Creating a Machine Learning REST API on a Cloud virtual server Creating a Serverless Machine Learning REST API using Cloud Functions Building and Deploying TensorFlow and Keras models using TensorFlow Serving Building and Deploying&nbsp; PyTorch Models Converting a PyTorch model to TensorFlow format using ONNX Creating REST API for PyTorch and TensorFlow Models Deploying tf-idf and text classifier models for Twitter sentiment analysis Deploying models using TensorFlow.js and Java Script Tracking Model training experiments and deployment with MLF Low Running ML Flow on Colab and Databricks Appendix - Generative AI - Miscellaneous Topics.OpenAI and the history of GPT models Creating an OpenAI account and invoking a text-to-speech model from Python code Invoking OpenAI Chat Completion, Text Generation, Image Generation models from Python code Creating a Chatbot with OpenAI API and ChatGPT Model using Python on Google Colab ChatGPT, Large Language Models (LLM) and prompt engineering New Section : Agent-Mode Model Building and Deployment with Git Hub Copilot Vibe Coding: Model Development with Git Hub Copilot Using a Single Prompt<li

Large Language Models like GPT-4, Llama, and Mistral are no longer science fiction; they are the new frontier of technology, powering everything from advanced chatbots to revolutionary scientific discovery. But to most, they remain a "black box." While many can use an API, very few possess the rare and valuable skill of understanding how these incredible models work from the inside out.What if you could peel back the curtain? What if you could build a powerful, modern Large Language Model, not just by tweaking a few lines of code, but by writing it from the ground up, line by line?This course is not another high-level overview. It's a deep, hands-on engineering journey to code a complete LLM—specifically, the highly efficient and powerful Mistral 7B architecture—from scratch in PyTorch. We bridge the gap between abstract theory and practical, production-grade code. You won't just learn what Grouped-Query Attention is; you'll implement it. You won't just read about the KV Cache; you'll build it to accelerate your model's inference.We believe the best way to achieve true mastery is by building. Starting with the foundational concepts that led to the transformer revolution, we will guide you step-by-step through every critical component. Finally, you'll take your custom-built model and learn to deploy it for real-world use with the industry-standard, high-performance vLLM Inference Engine on Runpod.After completing this course, you will have moved from an LLM user to an LLM architect. You will possess the first-principles knowledge that separates the experts from the crowd and empowers you to build, debug, and innovate at the cutting edge of AI.You will learn to build and understand:The Origins of LL Ms: The evolution from RNNs to the Attention mechanism that started it all.The Transformer

Most LangChain and Lang Graph courses are Python-first. This one is built from the ground up for Java Script &amp; Type Script engineers who want real, shippable agentic systems—not disconnected demos.You’ll build a sequence of end-to-end projects that mirror how modern teams ship AI features: clean Type Script code, clear AP Is, JSON contracts, Lang Graph orchestration, RAG, proper vector stores, and real Next.js frontends wired to real agents.By the end, you’ll know exactly how to go from idea → design → implementation → observability → deployment in the JS ecosystem.Here’s what we’ll cover in Phase 1:Intro &amp; Mindset How this course works, what it is / isn’t, and how to follow.Choosing models (OpenAI / Gemini / Groq / local) smartly for cost, speed &amp; reliability.How all projects connect into a reusable “agent platform” you can extend.Foundations: LangChain, Agents &amp; Flow Modern AI app architecture: UI → orchestration → models → tools → storage.Simple, honest definition of AI agents and real-world use cases.Chains vs agents: when a chain is enough, when an agent is worth it.Where LangChain.js fits, where Lang Graph.js fits, and how they work together.JSON-first mindset teaser: why strings lie and schemas save you.Orientation &amp; “Hello Agent” ProjectTS/Node project setup, tsconfig, env patterns, scripts.Multi-provider setup: OpenAI, Gemini, Groq via a single provider factory.First “Hello Agent” function that runs like a clean backend primitive, not a toy script.LLM Fundamentals: JS

Machine Learning Real value comes from actually deploying a machine learning solution into production and the necessary monitoring and optimization work that comes after it.Most of the problems nowadays as I have made a machine-learning model but what next.How it is available to the end-user, the answer is through API, but how it works?How you can understand where the Docker stands and how to monitor the build we created.This course has been designed to keep these areas under consideration. The combination of industry-standard build pipeline with some of the most common and important tools.This course has been designed into Following sections:1) Configure and a quick walkthrough of each of the tools and technologies we used in this course.2) Building our NLP Machine Learning model and tune the hyperparameters.3) Creating flask API and running the WebAPI in our Browser.4) Creating the Docker file, build our image and running our ML Model in Docker container.5) Configure Git Lab and push your code in Git Lab.6) Configure Jenkins and write Jenkins's file and run end-to-end Integration.This course is perfect for you to have a taste of industry-standard Data Science and deploying in the local server. Hope you enjoy the course as I enjoyed making it.

Willkommen zum ChatGPT-Kurs, der Ihnen alles beibringt, was Sie über die effektive Nutzung von ChatGPT wissen müssen! Dieser Kurs wurde entwickelt, um Ihnen die notwendigen Fähigkeiten zu vermitteln, um ChatGPT optimal zu nutzen, sei es für persönliche Projekte, berufliche Anwendungen oder kreative Ideen.Kursübersicht:Einführung in ChatGPT Verständnis der Grundlagen: Wie funktioniert ChatGPT?Überblick über die Anwendungsbereiche von ChatGPT in verschiedenen Branchen.Praktische Anwendungsfälle Schreiben von Texten: Tipps und Tricks zur Verbesserung der Textqualität.Kreative Anwendungen: Generierung von Ideen, Geschichten und mehr.Anpassung und Feinabstimmung Personalisierung von ChatGPT für Ihre speziellen Bedürfnisse.Feinabstimmung von Modellen für branchenspezifische Anforderungen.Integration von ChatGPT in Projekte Einbindung von ChatGPT in Ihre Arbeitswelt.Best Practices für die nahtlose Anwendung.Effiziente Kommunikation mit ChatGPT Richtiges Formulieren von Anfragen: Maximierung der Antwortqualität.Umgang mit Einschränkungen und Herausforderungen.Ethik und Verantwortung in der Nutzung von ChatGPT Sensibilisierung für mögliche Bias-Probleme.Verantwortungsbewusster Einsatz von KI-Technologien.Aktuelle Entwicklungen und Zukunftsaussichten Einblick in die neuesten Updates und Funktionen von ChatGPT.Ausblick auf zukünftige Entwicklungen in der Welt der KI-gesteuerten Kommunikation.Zielgruppe:Entwickler Content-Ersteller

This course is diving into Generative AI State-Of-Art Scientific Challenges. It helps to uncover ongoing problems and develop or customize your Own Large Models Applications. Course mainly is suitable for any candidates(students, engineers,experts) that have great motivation to Large Language Models with Todays-Ongoing Challenges as well as their&nbsp; deeployment with Python Based and Javascript Web Applications, as well as with C/C++ Programming Languages. Candidates will have deep knowledge on&nbsp; TensorFlow , PyTorch,&nbsp; Keras models, Hugging Face with Docker Service. In addition, one will be able to optimize and quantize TensorRT frameworks for deployment in variety of sectors. Moreover, They will learn deployment of LLM quantized model to Web Pages developed with React, Javascript and FLASK Here you will also learn how&nbsp; to integrate Reinforcement Learning(PPO) to Large Language Model, in order to fine them with Human Feedback based.&nbsp; Candidates will learn how to code and debug in C/C++ Programming languages at least in intermediate level.LLM Models used: The Falcon, LLAMA2, BLOOM, MPT, Vicuna,FLAN-T5, GPT2/GPT3, GPT&nbsp;NEOXBERT 101, Distil BERTFINE-Tuning Small Models under supervision of BIG Models Image Generation :LLAMA models Gemini Dall-E OpenAI Hugging Face Models Learning and Installation of Docker from scratch Knowledge of Javscript, HTML ,CSS, Bootstrap React Hook,&nbsp;DOM and Javacscript Web Development Deep Dive on