Beginner 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 masterclass provides a broad perspective on managing generative AI systems. It includes hands-on experience deploying Hugging Face and OpenAI models with a focus on monitoring, cost optimization, and automation pipelines. It also covers version control with Git and CI/CD demonstrations.

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.

Welcome to "Machine Learning and Data Science with LangChain and LL Ms"! This comprehensive course is designed to equip you with the skills and knowledge needed to harness the power of LangChain and Large Language Models (LL Ms) for advanced data science and machine learning tasks.In today’s data-driven world, the ability to process, analyze, and extract insights from large volumes of data is crucial. Language models like GPT have transformed how we interact with and utilize data, allowing for more sophisticated natural language processing (NLP) and machine learning applications. LangChain is an innovative framework that enables you to build applications around these powerful LL Ms. This course dives deep into the integration of LL Ms within the data science workflow, offering hands-on experience with real-world projects.What You Will Learn?Throughout this course, you will gain a thorough understanding of how LangChain can be utilized in various data science applications, along with the practical knowledge of how to apply LL Ms in different scenarios. Starting with the basics of machine learning and data science, we gradually explore the core concepts of LL Ms and how LangChain can enhance data-driven solutions.Key Learning Areas:1. Introduction to Machine Learning and Data Science: Begin your journey by understanding the core principles of machine learning and data science, including the types of data, preprocessing techniques, and model-building strategies.2. Exploring Large Language Models (LL Ms): Learn what LL Ms are, how they function, and their applications in various domains. This section covers the latest advancements in language models, including their architecture and capabilities in text generation, classification, and more.3. LangChain Fundamentals: Discover the potential of LangChain as a tool for

Section 1: Introduction to the Course Get a complete overview of the course and what you will learn. Understand how ChatGPT-5 and AI tools can help you create, automate, and innovate efficiently.Section 2: Understanding AI Essentials Learn the core concepts of Artificial Intelligence in a simple way. Understand how AI works, how it is trained, and how AI tools are transforming industries in 2025.Section 3: Prompt Engineering & Creating Effective Prompts Discover the art of prompt engineering. Learn to communicate with AI models to get accurate, creative, and useful outputs. Explore prompt structures, chaining, and generating ideas and automation flows.Section 4: Creating AI Presentations Learn to make AI-powered presentations quickly. Present your ideas clearly and professionally for college, work, or business with AI assistance.Section 5: AI Video Generation Step into AI-driven video creation. Turn text prompts into professional-looking videos for marketing, education, or social media without needing any editing skills.Whether you are new to AI or already exploring tools like ChatGPT, this course will guide you step by step in a simple and practical way so you can apply AI in your studies, career, and daily life. join and start learning with our AI course start now

Ready for an electrifying plunge into the universe of language technology? Prepare to enter the thrilling realm of LangChain with "LangChain 101 for Beginners (OpenAI / ChatGPT / LLM Ops)", where you'll be taught how to harness the power of LangChain and Large Language Models (LL Ms) to build your very own Python applications.Our aim for this course is simple - to equip you with everything you need to embark on your LangChain adventure. You'll be walked through using different LL Ms from industry giants OpenAI and Hugging Face, understand the magic of calling prompts, creating templates, and chaining these prompts together to create a robust, interactive system.But that's not all! We’ll dive into the heart of conversational chatbots and explore how memory works within LangChain. We'll wrap things up with a detailed tutorial on how you can apply these impressive LL Ms to your own documents.This course isn’t just informative—it’s also seriously fun. Through the use of memes, real-world analogies, and an engaging, down-to-earth approach, we've designed this course to be an enjoyable journey into the world of LangChain.Say goodbye to those long, never-ending courses that are all fluff and no substance. This course is compact, to-the-point, and perfect for Python developers looking for a fast-track introduction to LangChain and LL Ms. We know your time is precious, so we've packed all the essential information into one power-packed hour."LangChain 101 for Beginners" is your golden ticket to understanding and implementing LangChain. By the end of this course, you'll not only have a comprehensive understanding of LangChain, but also be ready to dive headfirst into your next project with a newfound arsenal of skills and knowledge.Don't wait—let's start scripting the future, together. Let’s dive into the incredible world of LangChain and Large Language Models, and have some fun along the way!

The Python for Data Science and Machine Learning course is designed to equip learners with a comprehensive understanding of Python programming, data science techniques, and machine learning algorithms. Whether you are a beginner looking to enter the field or a seasoned professional seeking to expand your skillset, this course provides the knowledge and practical experience necessary to excel in the rapidly growing field of data science.Course Objectives:1. Master Python Programming: Develop a strong foundation in Python programming, including syntax, data structures, control flow, and functions. Gain proficiency in using Python libraries such as Num Py, Pandas, and Matplotlib to manipulate and visualize data effectively.2. Data Cleaning and Preprocessing: Learn how to handle missing data, outliers, and inconsistent data formats. Acquire skills in data cleaning and preprocessing techniques to ensure the quality and reliability of datasets.3. Exploratory Data Analysis: Understand the principles and techniques of exploratory data analysis. Learn how to extract insights, discover patterns, and visualize data using statistical methods and Python libraries.4. Statistical Analysis: Gain a solid understanding of statistical concepts and techniques. Apply statistical methods to analyze data, test hypotheses, and draw meaningful conclusions.5. Machine Learning Fundamentals: Learn the foundations of machine learning, including supervised and unsupervised learning, regression, classification, and clustering. Understand the strengths and limitations of different machine learning algorithms.6. Machine Learning Implementation: Gain hands-on experience in implementing machine learning models using Python libraries such as Scikit-Learn. Learn how to train, evaluate, and optimize machine learning models.7. Feature Engineering and Selection: Develop skills in feature engineering to create mea

Do you want to become an AWS Machine Learning Engineer Using Sage Maker in 30 days?Do you want to build super-powerful production-level Machine Learning (ML) applications in AWS but don’t know where to start?Are you an absolute beginner and want to break into AI, ML, and Cloud Computing and looking for a course that includes everything you need?Are you an aspiring entrepreneur who wants to maximize business revenues and reduce costs with ML but don’t know how to get there quickly and efficiently?Do you want to leverage ChatGPT as a programmer to automate your coding tasks?If the answer is yes to any of these questions, then this course is for you!Machine Learning is the future one of the top tech fields to be in right now! ML and AI will change our lives in the same way electricity did 100 years ago. ML is widely adopted in Finance, banking, healthcare, transportation, and technology. The field is exploding with opportunities and career prospectsAWS is the one of the most widely used cloud computing platforms in the world and several companies depend on AWS for their cloud computing purposes. AWS Sage Maker is a fully managed service offered by AWS that allows data scientist and AI practitioners to train, test, and deploy AI/ML models quickly and efficiently.This course is unique and exceptional in many ways, it includes several practice opportunities, quizzes, and final capstone projects. In this course, students will learn how to create production-level ML models using AWS. The course is divided into 8 main sections as follows:Section 1 (Days 1 – 3): we will learn the following: (1) Start with an AWS and Machine Learning essentials “starter pack” that includes key AWS services such as Simple Storage Service (S3), Elastic Compute Cloud (EC2), Identity and Access Management (IAM) an

This Course simplifies the advanced Deep Learning concepts like Deep Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Long Short Term Memory (LSTMs), Gated Recurrent Units(GRUs), etc. TensorFlow, Keras, Google Colab, Real World Projects and Case Studies on topics like Regression and Classification have been described in great detail. Advanced Case studies like Self Driving Cars will be discussed in great detail. Currently the course has few case studies.The objective is to include at least 20 real world projects soon.  Case studies on topics like Object detection will also be included. TensorFlow and Keras basics and advanced concepts have been discussed in great detail.  The ultimate goal of this course is to make the learner able to solve real world problems using deep learning. After completion of this course the Learner shall also be able to pass the Google TensorFlow Certification Examination which is one of the prestigious Certification. Learner will also get the certificate of completion from Udemy after completing the Course. After taking this course the learner will be expert in following topics. a) Theoretical Deep Learning Concepts.b) Convolutional Neural Networksc) Long-short term memoryd) Generative Adversarial Networkse) Encoder- Decoder Modelsf) Attention Modelsg) Object detectionh) Image Segmentationi) Transfer Learningj) OpenCV using Pythonk) Building and deploying Deep Neural Networks l) Professional Google TensorFlow developer m) Using Google Colab for writing Deep Learning coden) Python programming for Deep Neural Networks The Learners are advised to practice the TensorFlow code as they watch the videos on Programming from this course. First Few sections have been uploaded, The course is in updation phase and the remaining sections will be added soon.

Computer Vision With Deep Learningرؤية الكمبيوتر باستخدام التعلم العميقDescription This is a complete course that will prepare you to work in Computer Vision Using Deep Learning. We will cover the fundamentals of Deep Learning/ computer Vision and its applications, this course is designed to reduce the time for the learner to Learn Computer Vision using Deep learning.هذه دورة كاملة ستعدك للعمل في رؤية الكمبيوتر باستخدام التعلم العميق. سنغطي أساسيات التعلم العميق/رؤية الكمبيوتر وتطبيقاتها، وقد تم تصميم هذه الدورة لتقليل الوقت الذي يستغرقه المتعلم لتعلم رؤية الكمبيوتر باستخدام التعلم العميق.What Skills will you Learn:In this course, you will learn the following skills:Understand the Math behind Deep Learning Algorithms.Understand How computer vision Algorithms works.Write and build computer vision Algorithms using Deep learning technologies.Use opensource libraries.We will cover:Fundamentals of Computer Vision.Image Preprocessing.Deep Neural Network (DNNs) - PyTorch . Convolutional Neural Network (CNNs)- TensorFlow.Semantic Segmentation.Object Detection.Instance Segmentation.Pose Estimation.Generative AI.Face Recognition.If you do not have prior experience in Machine Learning OR Computer vision, that's NO PROBLEM!. This course is complete and concise, covering the fundamental Theory and needed coding knowledge. Let's work together to learn Computer Vision Using Deep Learning.إذا لم تكن لديك خبرة سابقة في التعلم الآلي أو رؤية الك

Dans ce cours accéléré, nous allons aborder les opportunités qu'offrent les modèles génératifs et ensuite, nous nous intéresserons plus particulièrement aux Generative Adversarial Networks (GANs). Je vais vous expliquer le fonctionnement des GANs de manière intuitive et ensuite, nous nous plongerons dans l'article qui les a introduit en 2014 (Ian J. Goodfellow et al.). Je vous expliquerai donc de manière mathématique le fonctionnement des GANs, ce qui vous permettra d'avoir les bases nécessaires pour implémenter votre premier GANs en partant de zéro.Nous implémenterons en approximativement 100 lignes de code un générateur, un discriminateur et le pseudo-code décrit dans l'article afin d'entraîner ces derniers. Nous utiliserons le langage de programmation Python et le framework PyTorch. Après entraînement, le générateur nous permettra de générer des images synthétiques.J'ai la conviction qu'un concept s'apprend par la pratique et ce cours accéléré a pour objectif de vous donner les bases nécessaires afin de continuer votre apprentissage du Machine Learning, de PyTorch et des modèles génératifs (GANS, Variational Autoencoders, Normalizing Flows, ...).À l'issue de ce cours, le participant aura la possibilité d'utiliser Python (et plus particulièrement le framework PyTorch) afin d'implémenter des articles scientifiques et des solutions d'intelligence artificielle. Ce cours a également pour objectif d'être un tremplin dans votre apprentissage des modèles génératifs.Au-delà des GANs, ce cours est également une introduction générale au framework PyTorch et un cours de Machine learning de niveau intermédiaire .Concepts abordés:Le framework PyTorch afin d'implémenter et d'optimiser des réseaux de neurones.Le framework Keras afin de charger un ensemble de données.Google colab.L'utilisation des modèles génératifs dans le monde de la recherche et industri