Intermediate Generative AI

Generative AI for Content Creation & Marketing

LLM Evaluation and Testing

Streamlit for LLM Applications

Generative AI and ChatGPT: Complete Course

AI Image Generation with Stable Diffusion and DALL-E

A course focused on building Generative AI SaaS applications with tools like ChatGPT, MongoDB, Firebase, and Stripe, with a claim of no coding skills required. It covers building and testing mobile apps using Expo Snack.

A comprehensive course on building, deploying, and optimizing AI models using LangChain and Hugging Face. It covers everything from the basics of Generative AI to advanced concepts like Retrieval-Augmented Generation (RAG) pipelines.

This bootcamp on Udemy focuses on the operational aspects of deploying large language models. It covers CI/CD, Docker, Kubernetes, and monitoring for production LLM deployment, which are essential skills for managing and optimizing the cost and performance of LL Ms at scale.

This course focuses on the foundational concepts of LL Ms, specifically tokenization and word embedding models. It includes practical, hands-on exercises for building and training these models using PyTorch.

Learn ChatGPT Complete Guide: Learn Midjourney, ChatGPT 4 & More

Generative AI with Large Language Models

A comprehensive guide to over 50 generative AI tools to enhance business productivity and creativity, with a focus on ChatGPT and prompt engineering.

A bestselling bootcamp that teaches practical skills for working professionally with AI, including GPT-4, Midjourney, and Git Hub Copilot. It covers the 'Five Principles of Prompting' and other professional-grade tips and tricks.

Understand ChatGPT, GPT, LL Ms, Transformer Models and Generative AI concepts. Learn about prompt engineering.

Bienvenidos a este Curso Completo de Data Science en Python Desde Cero. En este curso, aprenderemos cómo usar Python para Data Science. Aprenderemos cómo recopilar datos, limpiar datos, hacer visualizaciones y construir un modelo de machine learning usando Python.El objetivo principal de este curso es llevar tus habilidades analíticas y de programación al siguiente nivel para desarrollar tu carrera como data scientist. Para lograr este objetivo, vamos a resolver +100  ejercicios y varios proyectos que te ayudarán a poner en práctica todos los conceptos de programación utilizados en Data Science.Aprenderemos las principales librerías de Python utilizadas en data science, como Pandas, Numpy y Scikit-Learn, y las usaremos para hacer tareas que data scientist realizan a diario (limpieza de datos, visualización de datos, recopilación de datos y creación de modelos).Este curso cubre 4 secciones.1. Curso básico de Python para Data Science: En la primera sección, aprenderemos todos los conceptos básicos de Python que necesita saber para data science. Aprenderemos a usar variables, listas, diccionarios y más.2. Python para análisis de datos: Aprenderemos las librerías de Python que se utilizan para el análisis de datos, como Pandas y Numpy. Ambas son excelentes herramientas para explorar y trabajar con datos. Usaremos Pandas y Numpy para realizar tareas de data science como limpiar y preparar datos.3. Python para visualización de datos: En la tercera sección, aprenderemos como hacer visualizaciones estáticas e interactivas con Pandas. Además, te mostraré algunas técnicas para realizar correctamente la visualización de datos.4. Machine Learning con Python: En la cuarta sección, aprenderemos Scikit-Learn resolviendo un problema de clasificación de texto en Python. Esta es la librería de machine learning más popular en Python y no solo aprenderemos como implementar algoritmos de machine learning en Python, sino que también aprenderemos conceptos básicos detrás de los a

Prompting Playbook &amp; Frameworks — Write Prompts That Deliver Business-Ready Results (ChatGPT, Gemini, Copilot, Perplexity)Short, practical, web-based.Learn the exact frameworks that turn vague asks into clear, reliable outputs across ChatGPT, Google Gemini, Microsoft Copilot (Web), and Perplexity.No agents. No coding. Just prompts that work.Why this course now?AI is already part of everyday work.Meeting notes, status updates, proposals, personas, job descriptions, variance commentary—tasks that used to take hours can now take minutes.But there’s a catch.A basic prompt will give you random, inconsistent responses.A well-structured, well-scoped prompt produces accurate, predictable, decision-ready results.This course gives you the playbook for writing those prompts—fast.What you’ll learn How to structure any request with R-C-I-E-S so the model understands exactly what to do.How to iterate once or twice with the Draft → Critique → Revise loop and a simple rubric to lift quality without wasting time.How to shape prompts with TCR (Task–Context–Rules), plus variables and checklists that you can reuse anywhere.How to decompose work into a Chain of Tasks when a single prompt is too vague—no agents required.How to ask for bullet caps, word caps, evidence lines, and tables so your outputs are tidy, scannable, and executive-ready.How to apply verification habits (no new numbers, assumptions to verify, objections to test) so stakeholders trust the output.No coding. No installs. Web LL Ms only.<p

KI Prompt Engineering Techniken für LL Ms: Tipps &amp; Tricks Entfessele das volle Potenzial von KI-Sprachmodellen!Keine lästigen Notizen während des Kurses!Konzentriere dich ganz auf das Lernen und profitiere von den bereits vorbereiteten Notizen und wissenschaftlichen Quellen im Begleitmaterial.Dieser Kurs ist ideal für:Einsteiger und Entdecker, die KI-Sprachmodelle wie ChatGPT 4, Google Gemini und Bing Chat im Alltag nutzen möchten Berufstätige, die ihre Ergebnisse mit KI-Tools verbessern möchten Alle, die mit den Standard-Ergebnissen von KI-Sprachmodellen nicht zufrieden sind Lerninhalte:Grundlagen des Prompt Engineering: So steuerst du KI-Sprachmodelle präzise und effektiv Einführung in Large Language Models (LL Ms): Funktionsweise, Stärken und Schwächen Verschiedene Prompting-Techniken: Rollenzuweisung, Chain-of-Thought, Tree-of-Thought und mehr Erweiterte Prompting-Strategien: Kontextualisierung, Kombinieren von Techniken und Nutzung von Prompt Generatoren Praktische Anwendungen: Fallbeispiele aus verschiedenen Branchen Tipps &amp; Tricks: Vermeiden von Fehlern und Optimierung Ihrer Prompts Erhalte:Umfassendes Wissen über Prompt Engineering und LL Ms Praktische Fähigkeiten zum Erstellen effektiver Prompts Zugang zu einer Community von Gleichgesinnten Exklusives Begleitmaterial:Fertig geschriebene, aufgearbeitete Notizen zu allen theoretischen Kapiteln<p

Join our interactive course, "Complete Guide to ChatGPT &amp; Copilot for Python &amp; R Projects", designed to give you hands-on experience in solving real data science problems using AI, Copilot, and ChatGPT. With Udemy's 30-day money-back guarantee in place, there's no need to worry if the class doesn't meet your expectations.The course is taught both in Python and R with RStudio. A complete installation guide on how to install and configure Python and R, was added in April 2024. It also explains how to connect RStudio to Python and use it as Python's IDE.Each lesson in this course stands alone, focusing on a different data science challenge.You'll learn how to apply AI tools like Copilot and ChatGPT to navigate through these challenges efficiently.Incorporating AI tools like Copilot and ChatGPT into your data science workflow can significantly enhance your speed and efficiency, often doubling (X2) or even increasing productivity tenfold (X10), depending on the task at hand.Here's what we'll cover, using practical, project-based learning:Data Clean-up and Tidy: Learn how to organize your data neatly, making it ready for analysis. This is a key step in data science to ensure your data is accurate and easy to work with. Using Pandas with Python and dplyr with R.Load Files of Different Formats: Discover how to bring in data from different kinds of files. This skill is important in data science because it lets you work with all sorts of data in tools like Copilot and ChatGPT.Data Visualization with Graphs: Find out how to use graphs to show your data in a clear and interesting way. Graphs help you see patterns and important points, which is a big part of data science.</

Discover the power of building AI-powered applications entirely in Kotlin with our comprehensive course on Jetpack Compose, Langchain4j, and Ollama (Local LLM). Whether you're a seasoned developer or a newcomer, this course equips you with the skills to harness these cutting-edge technologies and stay relevant in this AI driven industry. Throughout the course, we focus on practical hands-on learning, guiding you through the creation of six distinct applications leveraging Langchain4j:Hello World AI: Create an AI that greets users dynamically.Rephaser AI: Transform sentences into various tones for easy clipboard use.Unlost AI: Develop an AI to help users remember where they've placed their belongings.Text Adventure AI: Construct interactive storytelling experiences using AI.Resume QnA AI: Utilize AI to summarize resumes and retrieve candidate information interactively.RAG Medium Articles AI: Build a Retrieval-Augmented Generation (RAG) AI for summarizing and querying public Medium articles.You'll gain proficiency in Jetpack Compose for designing modern, responsive UIs and harness Langchain4j and Ollama for AI model management and natural language processing tasks. Learn essential Kotlin programming techniques to seamlessly integrate these technologies and optimize performance on local platforms.Join us to unlock the potential of Kotlin-based AI app development with practical skills and real-world applications in demand today.

Common scenario: You try to get into machine learning and data science, but there's SO&nbsp;MUCH&nbsp;MATH.Either you never studied this math, or you studied it so long ago you've forgotten it all.What do you do?Well my friends, that is why I created this course.Linear Algebra is one of the most important math prerequisites for machine learning. It's required to understand probability and statistics, which form the foundation of data science.The "data" in data science is represented using matrices and vectors, which are the central objects of study in this course.If you want to do machine learning beyond just copying library code from blogs and tutorials, you must know linear algebra.In a normal STEM college program, linear algebra is split into multiple semester-long courses.Luckily, I've refined these teachings into just the essentials, so that you can learn everything you need to know on the scale of hours instead of semesters.This course will cover systems of linear equations, matrix operations (dot product, inverse, transpose, determinant, trace), low-rank approximations, positive-definiteness and negative-definiteness, and eigenvalues and eigenvectors. It will even include machine learning-focused material you wouldn't normally see in a regular college course, such as how these concepts apply to GPT-4, and fine-tuning modern neural networks like diffusion models (for generative AI&nbsp;art) and LL Ms (Large Language Models) using LoRA. We will even demonstrate many of the concepts in this course using the Python programming language (don't worry, you don't need to know Python for this course). In other words, instead of the dry old college version of linear algebra, this course takes just the most practical and impactful topics, and provides you with skills directly applicable to machine learning and data science, so you can s

Der Kurs ist ein Einsteiger-Kurs in die Welt des Data Science, des Machine Learning, der künstlichen Intelligenz und dem Arbeiten mit Daten. In Zeiten der Digitalisierung und der digitalen Transformation stellt die Wissenschaft der Daten (Data Science) immer mehr eine zentrale Disziplin dar.&nbsp;Ohne grundlegende Kenntnisse und Qualifikationen im Bereich der Daten sind viele Arbeitsplätze kaum noch denkbar.Der Kurs liefert daher einen unkomplizierten Einstieg in die Welt der Daten und der Algorithmen. Dadurch ergibt sich ein Grundverständnis, was Daten überhaupt sind und man sie einer automatischen Verarbeitung mit Algorithmen zugänglich machen kann. Alle Algorithmen und mathematischen Verfahren werden Schritt für Schritt erklärt.Der Lernpfad dieses Kurses besteht u.a. aus folgenden Abschnitten:- Was sind Daten?- Datentypen, Data Mining und Visualisierung von Daten- Statistische Grundbegriffe- Einfache Clustering-Verfahren- Lineare und logistische Regression- Kurze Einführung in die Graphentheorie- Entscheidungsbäume und Random Forest- Einführung in die neuronalen Netze- Überblick über generative KI und deren Anwendungen Alle Algorithmen und Verfahren werden so ausführlich erläutert, dass keine speziellen mathematischen Vorkenntnisse oder IT-Fähigkeiten erforderlich sind. Ein grundlegendes Interesse an mathematischen Zusammenhängen wird hingegen vorausgesetzt. Die Beispiele stehen im Quellcode in der Programmiersprache Python zum Download und zum selber ausprobieren bereit.Der Kurs richtet sich insbesondere an Fach- und Führungskräfte, die selbst mit Daten arbeiten und sich ein tieferes Verständnis grundlegender Zusammenhänge erarbeiten möchten.

Common scenario: You try to get into machine learning and data science, but there's SO MUCH MATH.Either you never studied this math, or you studied it so long ago you've forgotten it all.What do you do?Well my friends, that is why I created this course.Probability is one of the most important math prerequisites for data science and machine learning. It's required to understand essentially everything we do, from the latest LL Ms like ChatGPT, to diffusion models like Stable Diffusion and Midjourney, to statistics (what I like to call "probability part 2").Markov chains, an important concept in probability, form the basis of popular models like the Hidden Markov Model (with applications in speech recognition, DNA analysis, and stock trading) and the Markov Decision Process or MDP (the basis for Reinforcement Learning).Machine learning (statistical learning) itself has a probabilistic foundation. Specific models, like Linear Regression, K-Means Clustering, Principal Components Analysis, and Neural Networks, all make use of probability.In short, probability cannot be avoided!If you want to do machine learning beyond just copying library code from blogs and tutorials, you must know probability.This course will cover everything that you'd learn (and maybe a bit more) in an undergraduate-level probability class. This includes random variables and random vectors, discrete and continuous probability distributions, functions of random variables, multivariate distributions, expectation, generating functions, the law of large numbers, and the central limit theorem.Most important theorems will be derived from scratch. Don't worry, as long as you meet the prerequisites, they won't be difficult to understand. This will ensure you have the strongest foundation possible in this subject. No more memorizing "rules" only to apply them incorrectly / inappropriately in the future! This course will provide you with

Fai un passo verso il futuro: AI, Machine Learning e Data Science.Sai cosa accomuna il successo dei più grandi colossi del web come Google, Amazon e Facebook ?&nbsp;L'utilizzo che hanno fatto del machine learning.Il machine learning è la branca dell'intelligenza artificiale che ha lo scopo di insegnare ai computer ad apprendere&nbsp;autonomamente, senza essere esplicitamente programmati.Il machine learning non è una novità, ma è finito sotto la luce dei riflettori solo con il nuovo millennio, per due motivi:L'enorme quantità di dati oggi disponibile sul web.Il progresso della tecnologia e il crescente aumento&nbsp;della potenza di calcolo.Questi due fattori, uniti alle sue innumerevoli applicazioni commerciali,&nbsp;stanno contribuendo alla crescita vertiginosa del machine learning&nbsp;che sta trascinando con se l'intero campo dell'intelligenza artificiale.In questo corso pratico imparerai come funziona il machine learning e come utilizzarlo&nbsp;in maniera pratica, utilizzando il linguaggio Python e librerie popolari come Scikit-Learn, Pandas e Py Plot.Vuoi dare una svolta alla tua carriera ?L'esperto di machine learning è la professione del futuro e Linkedin lo conferma; secondo una loro recente ricerca il Machine Learning Engineer è la nuova figura più ricercata dalle aziende con un tasso di crescita di quasi il 1000% negli ultimi 5 anni ed è subito seguito dal Data Scientist.Al termine di questo corso avrai acquisito&nbsp;l'esperienza pratica e le intuizioni teoriche necessarie per lanciare la tua carriera in entrambe queste due nuove professioni.Vuoi fondare la tua startup nel campo dell'AI ?Il valore totale del mercato dell'intelligenza artificiale nel 2016 era di 1.3 miliardi di dollari; secondo una ricerca di un'importante&nbsp;società di analisi americana&nbsp;il suo valore per il 2025 potrebbe superare il 60 miliard