Advanced MLOps

Statistical Learning

Reinforcement Learning Specialization

Master TensorFlow 2 and Keras. ANNs, CNNs, RNNs, GANs, deployment.

This three-course professional certificate program, offered by Harvard University and Google TensorFlow, provides a deep dive into the emerging field of TinyML. It covers the essential language of TinyML, its real-world applications, and the practical deployment of machine learning models on resource-constrained embedded systems. The program emphasizes hands-on experience using a kit that includes an Arduino board.

Embark on a comprehensive journey through the fascinating realm of data science and machine learning with our course, "Data Science and Machine Learning with Python and GPT 3.5." This course is meticulously designed to equip learners with the essential skills required to excel in the dynamic fields of data science and machine learning.Throughout this immersive learning experience, you will delve deep into the core concepts of data science and machine learning, leveraging the power of Python programming alongside the cutting-edge capabilities of ChatGPT 3.5. Our course empowers you to seamlessly navigate the entire data science workflow, from data acquisition and cleaning to exploratory data analysis and model deployment.You will master the art of cleaning raw data effectively, employing techniques tailored to handle missing values, diverse data types, and outliers, thus ensuring the integrity and quality of your datasets. Through hands-on exercises, you will become proficient in data manipulation using Python's pandas library, mastering essential techniques such as sorting, filtering, merging, and concatenating.Exploratory data analysis techniques will be thoroughly explored, empowering you to uncover valuable insights through frequencies, percentages, group-by operations, pivot tables, crosstabulation, and variable relationships. Additionally, you will gain practical experience in data preprocessing, honing your skills in feature engineering, selection, and scaling to optimize datasets for machine learning models.The course curriculum features a series of engaging projects designed to reinforce your understanding of key data science and machine learning concepts. You will develop expertise in building and evaluating supervised regression and classification models, utilizing a diverse array of algorithms including linear regression, random forest, decision tree, xgboost, logistic regression, KNN, lightgbm, and more.Unsupervised learning techniques will also b

Ce cours vous guidera dans l'utilisation du dernier Framework TensorFlow 2 de Google pour créer des Réseaux de Neurones Artificiels pour le Deep Learning ! Ce cours a pour but de vous donner un guide facile à comprendre sur les complexités du Framework TensorFlow version 2.x de Google (dernière version à jour).Nous nous attacherons à comprendre les dernières mises à jour de TensorFlow et à exploiter l'API de Keras (l'API officielle de TensorFlow 2) pour construire rapidement et facilement des modèles. Dans ce cours, nous construirons des modèles pour prédire des prix futurs de maisons, classer des images médicales, prédire les données de ventes futures, générer artificiellement un nouveau texte complet et bien plus encore... !Ce cours est conçu pour équilibrer la théorie et la mise en œuvre pratique, avec des guides de code complets de type "Notebook Google Colab" et des slides et notes faciles à consulter. Il y a également de nombreux exercices pour tester vos nouvelles compétences au cours de la formation !Ce cours couvre une grande variété de sujets, notamment :Cours accéléré sur la bibliothèque Num PyCours intensif et accéléré sur l'analyse des données avec la bibliothèque Pandas Cours accéléré sur la visualisation de données Principes de base des réseaux de neurones Principes de base de TensorFlow Notions de syntaxe de KerasRéseaux de Neurones Artificiels (ANNs)Réseaux à forte densité de connexionRéseaux de Neurones Convolutifs (CNNs)Réseaux de Neurones Récurrents (RNNs)Auto EncodersRéseaux Adversatifs Générateurs (GANs)Déploiement de TensorFlow en production avec Flasket bien plus encore !Keras, une API standard conviviale pour le Deep Learning, elle sera l'API centrale de haut niveau u

Unlock the potential of data-driven insights with our comprehensive course, "Deep Dive into Mastering Data Science and Machine Learning." In today's data-driven world, the ability to extract knowledge, predict trends, and make informed decisions is a crucial skill. This course is designed to empower you with the expertise required to navigate the intricate landscape of data science and machine learning.Course Highlights:Dive into Data: Learn to wrangle, clean, and preprocess data from various sources, preparing it for in-depth analysis. Discover techniques to identify and handle missing values, outliers, and anomalies that could affect your analysis.Algorithm Mastery: Delve into the world of machine learning algorithms, from foundational concepts to cutting-edge techniques. Understand the nuances of classification, regression, clustering, and recommendation systems, and explore ensemble methods and deep learning architectures for enhanced performance.Visualize Insights: Develop the art of data visualization to effectively communicate your findings. Learn to create compelling graphs, plots, and interactive dashboards that bring data to life and aid decision-making.Real-world Projects: Put theory into practice with hands-on projects that simulate real-world scenarios. Tackle challenges ranging from predicting customer behavior to image recognition, gaining experience that mirrors the complexities of the field.Ethical and Transparent AI: Understand the ethical considerations in data science and machine learning. Explore methods to interpret and explain model predictions, ensuring transparency and accountability in your applications.Model Deployment: Take your models from the development stage to real-world deployment. Learn about containerization, cloud services, and deployment pipelines, ensuring your solutions are accessible and scalable.Peer Learning: Engage with a

MASTER ENTERPRISE AI AGENTS & FUTURE-PROOF YOUR CAREER2025 is the year AI agents enter the workforce. While 47% of companies believe organizations not using AI will fail, only 15% have skilled AI engineers. Don't get left behind.Transform from curious learner to Professional AI Agent Engineer using LangChain, Lang Graph, CrewAI, Auto Gen, and RAG systems with the same enterprise patterns deployed by Netflix, Google, and Fortune 500 companies. Master the complete journey from cost-free local development with Ollama to production enterprise deployment.WHY THIS AI AGENTS BOOTCAMP IS DIFFERENTELIMINATE COST BARRIERS: Start with 100% FREE local models using Ollama, Deep Seek-R1, and Llama 3.2, then scale intelligently to enterprise cloud when needed. No more $200/month GPT-4o bills blocking your learning with ANY LLM provider flexibility.ENTERPRISE-GRADE AI AGENT ARCHITECTURE: Learn the same multi-agent orchestration patterns using LangChain, Lang Graph, CrewAI, and Auto Gen that tech giants use to save millions in operational costs and scale AI agents to millions of users.2025 CUTTING-EDGE AI AGENT STACK: Master Deep Seek-R1 (competitive with OpenAI o1 at 96% lower cost), Lang Graph workflows, CrewAI multi-agent systems, and Auto Gen coordination patterns before your competition.COMPLETE PROFESSIONAL AI AGENT TECHNOLOGY STACKCORE ENTERPRISE AI FRAMEWORKS: LangChain & Lang Graph: AI agent orchestration with ANY LLM provider RAG Systems: Vector search with FAISS, ChromaDB, Pinecone for intelligent document retrieval Multi-Agent Systems: Auto Gen team coordination, CrewAI role-based agents, advanced orchestration patterns Visual Development: Langflow no-code AI agent pipelines for rapi

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

Deep Learning with TensorFlow focuses on building and deploying advanced neural network models that mimic the human brain’s learning capabilities to solve complex problems. This topic explains the architecture of deep neural networks, including layers, neurons, activation functions, loss functions, backpropagation, and optimization techniques. Learners explore how TensorFlow, a leading open-source framework, enables the design, training, and deployment of deep learning models efficiently, handling large datasets and computational requirements. Practical applications such as image classification, object detection, natural language processing, speech recognition, and recommendation systems are highlighted to show real-world relevance. The topic also covers hyperparameter tuning, model evaluation, performance optimization, and techniques to prevent overfitting or underfitting. Learners gain a comprehensive understanding of how to preprocess data, structure neural networks, and apply advanced algorithms to achieve accurate and reliable results. This topic is ideal for students, AI enthusiasts, developers, and data scientists seeking practical deep learning expertise. By mastering Deep Learning with TensorFlow, learners develop the skills necessary to build intelligent systems that solve complex problems, contribute to innovation in AI-driven industries, and prepare for advanced roles in artificial intelligence, data science, and machine learning engineering. The knowledge gained empowers learners to create scalable, high-performing AI solutions that can be applied across multiple sectors, from technology to business intelligence.

Welcome to Modern Computer Vision TensorFlow, Keras & PyTorch! 2025AI and Deep Learning are transforming industries and one of the most intriguing parts of this AI revolution is in Computer Vision!Update for 2025: Modern Computer Vision Course We're excited to bring you the latest updates for our 2024 modern computer vision course. Dive into an enriched curriculum covering the most advanced and relevant topics in the field:YOL Ov8: Cutting-edge Object RecognitionDINO-GPT4V: Next-Gen Vision Models Meta CLIP for Enhanced Image Analysis Detectron2 for Object Detection Segment Anything Face Recognition Technologies Generative AI Networks for Creative Imaging Transformers in Computer Vision Deploying & Productionizing Vision Models Diffusion Models for Image Processing Image Generation and Its Applications Annotation Strategy for Efficient Learning Retrieval Augmented Generation (RAG)Zero-Shot Classifiers for Versatile Applications Using Roboflow: Streamlining Vision Workflows What is Computer Vision?But what exactly is Computer Vision and why is it so exciting? Well, what if Computers could understand what they’re seeing through cameras or in images? The applications for such technology are endless from medical imaging, military, self-driving cars, security monitoring, analysis, safety, farming, industry, and manufacturing! The list is endless. Job demand for Computer Vision workers are skyrocketing

Master the art of building professional-grade Generative AI applications with this comprehensive course designed for advanced developers, data scientists, AI enthusiasts, and technology leaders. This program covers everything you need to know about leveraging Large Language Models (LL Ms) to create robust, scalable, and production-ready AI-powered solutions. Whether you're looking to enhance your skills or build innovative applications, this course is your gateway to success in the AI-driven future.Start with an in-depth exploration of foundational concepts, including the architecture of Generative AI systems, key components, and tools. Learn about advanced topics such as Retrieval-Augmented Generation (RAG), LangChain, Llama Index, and the integration of cutting-edge orchestration frameworks. Gain hands-on experience with cloud platforms like AWS Bedrock, Google Vertex AI, and others to fine-tune your applications and deploy them in real-world scenarios.This course also delves into practical implementations, including chatbots with memory, advanced data retrieval, sentiment analysis tools, and multimodal AI applications. You'll master essential techniques like managing custom data, creating efficient pipelines, and optimizing performance for scalability. By the end of the course, you'll have the expertise to design, deploy, and maintain production-level AI systems that exceed professional standards, empowering you to lead in the rapidly evolving field of Generative AI development and innovation.

Welcome! This comprehensive course is designed for individuals eager to dive into the world of Large Language Models (LL Ms) and harness their power to create innovative applications that can simplify tasks in everyday life.Course Overview In this course, you will learn how to effectively utilize various libraries and frameworks, including Ollama, LangChain, CrewAI, and Hugging Face, to build practical projects that demonstrate the capabilities of LL Ms. Through hands-on projects, you will gain a deep understanding of how these technologies work together to enhance productivity and creativity.What You Will Learn Understanding LL Ms: Gain insights into the architecture and functioning of Large Language Models, including their applications in natural language processing (NLP).Ollama and LangChain: Learn how to leverage Ollama for efficient model deployment and LangChain for building complex applications that integrate multiple components seamlessly.Hugging Face Transformers: Explore the Hugging Face library to access a wide range of pre-trained models for various NLP tasks.Practical Applications: Implement real-world projects that showcase the power of LL Ms in different contexts.Project Highlights Learning Python Tool with Ollama: Create an interactive tool that helps users learn Python programming through guided exercises and instant feedback using an LLM.Make a Video Describer: Develop an application that generates descriptive text for video content, enhancing accessibility and understanding for users.Chat with PDF using Ollama LLM: Build a chat interface that allows users to ask questions about the content of PDF documents, provi

2026 Upgrade: Course completely re-recorded with LangChain v1 and Lang Graph v1.All projects, agents, tools, and RAG pipelines rebuilt from scratch.Perfect for developers, AI engineers, and serious learners who want production-grade GenAI skills.This course is a comprehensive, practical guide to integrating LangChain v1 (latest release) and Ollama to build, automate, and deploy production-ready AI applications. Updated with the newest technologies and frameworks, you'll learn to set up these cutting-edge tools, create advanced prompt templates, build autonomous AI agents, implement RAG (Retrieval-Augmented Generation) systems, and deploy real-world applications on AWS. Each section is designed to provide you with hands-on skills and real-world experience with the latest AI development practices.What You Will Learn1. Ollama & LangChain Setup Complete installation and configuration of Ollama and LangChain Work with the latest models: GPT-OSS, Gemma3, Qwen3, Deep Seek R1, and LLAMA 3.2Master Ollama commands, custom model creation, and raw API integration Configure local LLM environments for optimal performance2. Advanced Prompt Engineering Design effective AI, human, and system message prompts Use Chat Prompt Template and Messages Placeholder for dynamic conversations Master the invoke method and structured prompt patterns Implement best practices for prompt tuning and optimization3. LCEL Chains for Workflow Automation Build Sequential, Parallel, and Router Chains with LangChain Expression Language (LCEL)Create custom chains using Runnable Lambda and Runnable Passthrough Implement chain decorators for simplified workflow automation Design conditio

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

If you're a budding data enthusiast, developer, or even an experienced professional wanting to make the leap into the ever-growing world of machine learning, have you often wondered how to integrate the power of TensorFlow with the vast scalability of Google Cloud? Do you dream of deploying robust ML models seamlessly without the fuss of infrastructure management?Delve deep into the realms of machine learning with our structured guide on "Machine Learning with TensorFlow on Google Cloud." This course isn't just about theory; it's a hands-on journey, uniquely tailored to help you utilize TensorFlow's prowess on the expansive infrastructure that Google Cloud offers.In this course, you will:Develop foundational models such as Linear and Logistic Regression using TensorFlow.Master advanced architectures like Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs) for intricate tasks.Harness the power and convenience of Google Cloud's Colab to run Python code effortlessly.Construct sophisticated Jupyter notebooks with real-world datasets on Google Colab and Vertex.But why dive into TensorFlow on Google Cloud? As machine learning solutions become increasingly critical in decision-making, predicting trends, and understanding vast datasets, TensorFlow's integration with Google Cloud is the key to rapid prototyping, scalable computations, and cost-effective solutions.Throughout your learning journey, you'll immerse yourself in a series of projects and exercises, from constructing your very first ML model to deploying intricate deep learning networks on the cloud.This course stands apart because it bridges the gap between theory and practical deployment, ensuring that once you've completed it, you're not just knowledgeable but are genuinely ready to apply these skills in real-world scenarios.Take the next step in your machi

Prompt Engineering & LLM Production Master the practical craft of prompt engineering and learn how to design, test, and deploy reliable AI-driven workflows that power real products. This immersive, hands-on course walks you from first principles to production-ready systems, with a focus on reproducible practices, measurable improvements, and real-world integrations. Whether you want to build smarter content pipelines, automated customer support, or code-generation assistants, this course teaches the exact skills, patterns, and guardrails you’ll use every day as an AI prompt engineering practitioner.What this course is (straight, no fluff)This is a pragmatic, exercise-first course on prompt engineering for people who want results — not just theory. You’ll learn how to craft prompts that produce consistent outputs, control model behavior (temperature, top_p, tokens, penalties), evaluate and A/B-test prompt variants, chain prompts into multi-step pipelines, and move from manual experimentation into reliable automation using AP Is and tooling like LangChain and Prompt Layer. The course emphasizes safety, cost-efficiency, and measurable outcomes so you can deploy prompt-based features in production with confidence.Key skills you’ll walk away with Expert-level chatgpt prompt engineering techniques: system/user/assistant role design, few-shot teaching, and format enforcement.Robust experiment practices: hypothesis design, A/B testing, logging, and quantitative metrics (accuracy, F1 proxies, user satisfaction).Production patterns: prompt chaining, map-reduce strategies, validation layers, caching, and failover/human-in-the-loop design.Cost & performance optimization: token compression, reuse strategies, and measurable latency/cost tradeoffs.Safety & compliance: anti-hallucination pattern

Master Data Science Workflows with H2O: From Prep to Deployment & Generative AI with Michelle Tanco and Jon Farland!This course equips you with H2O's suite of cutting-edge tools, such as Driverless AI, H2O Actions, the Wave App, Gen AI App Store, LLM Data Studio, H2O LLM Studio, Enterprise GP Te, h2oGPT, and Eval Studio. In this comprehensive course, you will develop a thorough understanding of data preparation and visualization using H2O's intuitive tools, enabling you to efficiently clean, transform, and explore data to uncover actionable insights without the traditional complexities of data wrangling. Dive deep into automated machine learning mastery with Driverless AI, leveraging its automation capabilities to streamline model building processes, allowing you to focus on strategic analysis and solving complex problems effectively. Gain expertise in seamless model deployment techniques, ensuring that your models translate into impactful business outcomes with ease and efficiency. Explore the best of what generative AI has to offer with Enterprise GP Te and H2OGPT, where you will delve into advanced tasks such as text generation, language translation, and creative content development, empowering you to innovate and excel in data science and business decision-making. Join us on this transformative journey to elevate your skills and harness the full potential of H2O's tools for driving data-driven insights and strategic business success.Come aboard our dynamic course and elevate your data science skills!

Avec l'avènement des intelligences artificielles comme ChatGPT et Midjourney, nous vivons une véritable révolution dans le monde de la technologie. Et il est devenu indispensable de posséder des compétences en intelligence artificielle pour rester compétitif sur le marché de l'emploi. Si vous cherchez à développer vos compétences en IA, ce cours est exactement ce dont vous avez besoin pour acquérir les bases nécessaires et vous positionner comme un expert dans ce domaine en pleine croissance.Pourquoi Le deep learning avec TensorFlow et non PyTorch ?Parce que :TensorFlow a été créé par Google en 2015, tandis que PyTorch est apparu en 2017. TensorFlow a donc été utilisé et testé plus longtemps dans des applications de production.TensorFlow est plus adapté aux projets de grande envergure. TensorFlow a été conçu pour être utilisé sur des clusters de machines, ce qui en fait un choix plus approprié pour les projets de grande envergure.TensorFlow offre une grande flexibilité en termes de déploiement. TensorFlow peut être utilisé pour déployer des modèles sur différents types d'appareils, y compris les ordinateurs, les serveurs, les téléphones mobiles et les dispositifs de l'internet des objets.TensorFlow dispose d'un écosystème plus large et est utilisé dans un large éventail d'applications, allant de la reconnaissance d'image et de la vision par ordinateur à la prédiction de séries temporelles et à la modélisation du langage naturel.Les bases mathématiques du Deep Learning : Pas besoin d’être un matheux Cependant, TensorFlow encapsule plusieurs concepts mathématiques de base dont la compréhension est indispensable pour bien entrainer des réseaux de neurones.C’est pourquoi nous débutons cette formation par les bases mathématiques du Deep Learning, mais de façon pratique avec du code et non des formules mathématiques.Si vous avez le niveau Lycée en Mathématique mais pense