1235 AI courses in Cardiff delivered Live Online

Peer Supervision for Clinical Hypnotherapists: Peer supervision for clinical hypnotherapists fosters professional growth through enhanced reflective practice, continuous learning, emotional support, feedback and validation, ethical guidance, networking, and professional accountability. - **Enhanced Reflective Practice**: Facilitates self-reflection, helping hypnotherapists identify strengths, weaknesses, and areas for improvement through peer discussions. - **Continuous Learning**: Expands knowledge by sharing innovative techniques, research findings, and emerging trends, promoting ongoing professional development. - **Emotional Support**: Provides a supportive space for therapists to share experiences and receive emotional support, addressing the emotional demands of the profession. - **Feedback and Validation**: Offers constructive criticism and fresh perspectives, aiding in skill refinement and improved clinical practice. - **Ethical Guidance**: Allows discussion of ethical dilemmas and collaborative solutions, ensuring adherence to professional standards. - **Networking and Collaboration**: Builds professional networks, leading to collaboration, referrals, and partnerships. - **Professional Accountability**: Encourages high standards and self-reflection through peer discussions, enhancing practice quality.

Duration 5 Days 30 CPD hours This course is intended for The skills covered in this course converge on four areas-software development, IT operations, applied math and statistics, and business analysis. Target students for this course should be looking to build upon their knowledge of the data science process so that they can apply AI systems, particularly machine learning models, to business problems. So, the target student is likely a data science practitioner, software developer, or business analyst looking to expand their knowledge of machine learning algorithms and how they can help create intelligent decisionmaking products that bring value to the business. A typical student in this course should have several years of experience with computing technology, including some aptitude in computer programming. This course is also designed to assist students in preparing for the CertNexus Certified Artificial Intelligence (AI) Practitioner (Exam AIP-210) certification Overview In this course, you will develop AI solutions for business problems. You will: Solve a given business problem using AI and ML. Prepare data for use in machine learning. Train, evaluate, and tune a machine learning model. Build linear regression models. Build forecasting models. Build classification models using logistic regression and k -nearest neighbor. Build clustering models. Build classification and regression models using decision trees and random forests. Build classification and regression models using support-vector machines (SVMs). Build artificial neural networks for deep learning. Put machine learning models into operation using automated processes. Maintain machine learning pipelines and models while they are in production Artificial intelligence (AI) and machine learning (ML) have become essential parts of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, all while following a methodical workflow for developing data-driven solutions. Solving Business Problems Using AI and ML Topic A: Identify AI and ML Solutions for Business Problems Topic B: Formulate a Machine Learning Problem Topic C: Select Approaches to Machine Learning Preparing Data Topic A: Collect Data Topic B: Transform Data Topic C: Engineer Features Topic D: Work with Unstructured Data Training, Evaluating, and Tuning a Machine Learning Model Topic A: Train a Machine Learning Model Topic B: Evaluate and Tune a Machine Learning Model Building Linear Regression Models Topic A: Build Regression Models Using Linear Algebra Topic B: Build Regularized Linear Regression Models Topic C: Build Iterative Linear Regression Models Building Forecasting Models Topic A: Build Univariate Time Series Models Topic B: Build Multivariate Time Series Models Building Classification Models Using Logistic Regression and k-Nearest Neighbor Topic A: Train Binary Classification Models Using Logistic Regression Topic B: Train Binary Classification Models Using k-Nearest Neighbor Topic C: Train Multi-Class Classification Models Topic D: Evaluate Classification Models Topic E: Tune Classification Models Building Clustering Models Topic A: Build k-Means Clustering Models Topic B: Build Hierarchical Clustering Models Building Decision Trees and Random Forests Topic A: Build Decision Tree Models Topic B: Build Random Forest Models Building Support-Vector Machines Topic A: Build SVM Models for Classification Topic B: Build SVM Models for Regression Building Artificial Neural Networks Topic A: Build Multi-Layer Perceptrons (MLP) Topic B: Build Convolutional Neural Networks (CNN) Topic C: Build Recurrent Neural Networks (RNN) Operationalizing Machine Learning Models Topic A: Deploy Machine Learning Models Topic B: Automate the Machine Learning Process with MLOps Topic C: Integrate Models into Machine Learning Systems Maintaining Machine Learning Operations Topic A: Secure Machine Learning Pipelines Topic B: Maintain Models in Production

This comprehensive course covers all Scrum principles and frameworks necessary to help participants understand how to guide a team and manage projects in a fast-paced agile environment. The course is meant for professionals who want to attain the certification of Scrum Master with deep insight into how AI can be utilized in increasing the effectiveness of agile practices. In addition to mastery of the core Scrum methodology, participants will be taken through state-of-the-art advancements in AI and machine learning in order to understand how these technologies can automate routine tasks, enhance decision-making, and continuous improvement. Real-world case studies and hands-on exercises will illustrate how to practically apply AI within Scrum to realize high efficiency and innovation for teams. Whether for enhancing one's career as a Scrum Master or the integration of AI into Agile practices, this course provides that ideal combination of conceptual theory and practical skills, assuring success in today's technology-driven world. Key Highlights: Certified Scrum Master training with AI applications Case studies in the real world about integrating AI in Scrum Hands-on projects to implement AI-driven tools and methodologies Workflow optimization techniques that ensure better collaboration of agile teams, with speeding up project delivery by the power of AI. Ideal for Scrum Masters, Agile Coaches, Product Owners, and tech pros looking to stay ahead.

Duration 1 Days 6 CPD hours This course is intended for To gain the most from attending this course you should possess the following incoming skills: Basic knowledge of programming concepts and syntax in Python. Familiarity with common data formats such as CSV, JSON, and XML. Experience using command-line interfaces and basic text editing tools. Understanding of basic machine learning concepts and algorithms. Overview Working in an interactive learning environment, led by our engaging expert, you will: Gain a solid understanding of prompt engineering concepts and their applications in software development and AI-driven solutions. Master the techniques for preprocessing and cleaning text data to ensure high-quality inputs for AI models like GPT-4. Develop expertise in GPT-4 tokenization, input formatting, and controlling model behavior for various tasks and requirements. Acquire the ability to design, optimize, and test prompts effectively, catering to diverse business applications and use cases. Learn advanced prompt engineering techniques, such as conditional text generation and multi-turn conversations, to create more sophisticated AI solutions. Practice creating prompts to generate, run, and test code in a chosen programming language using GPT-4 and OpenAI Codex. Understand the ethical implications and best practices in responsible AI deployment, ensuring fair and unbiased AI applications in software development. Prompt Engineering offers coders and software developers a competitive edge by empowering them to develop more effective and efficient AI-driven solutions in their projects. By harnessing the capabilities of cutting-edge AI models like GPT-4, coders can automate repetitive tasks, enhance natural language understanding, and even generate code suggestions, boosting productivity and creativity. In addition, mastering prompt engineering can contribute to improved job security, as professionals with these in-demand skills are highly sought after in the rapidly evolving tech landscape. Quick Start to Prompt Engineering for Coders and Software Developers is a one day course designed to get you quickly up and running with the prompting skills required to out AI to work for you in your development efforts. Guided by our AI expert, you?ll explore key topics such as text preprocessing, data cleansing, GPT-4 tokenization, input formatting, prompt design, and optimization, as well as ethical considerations in prompt engineering. In the hands-on labs you?ll explore tasks such as formatting inputs for GPT-4, designing and optimizing prompts for business applications, and implementing multi-turn conversations with AI. You?ll work with innovative tools like the OpenAI API, OpenAI Codex, and OpenAI Playground, enhancing your learning experience while preparing you for integrating prompt engineering into your professional toolkit. By the end of this immersive course, you?ll have the skills necessary to effectively use prompt engineering in your software development projects. You'll be able to design, optimize, and test prompts for various business tasks, integrate GPT-4 with other software platforms, and address ethical concerns in AI deployment. Introduction to Prompt Engineering Overview of prompt engineering and its importance in AI applications Major applications of prompt engineering in business Common challenges faced in prompt engineering Overview of GPT-4 and its role in prompt engineering Key terminology and concepts in prompt engineering Getting Things Ready: Text Preprocessing and Data Cleansing Importance of data preprocessing in prompt engineering Techniques for text cleaning and normalization Tokenization and n-grams Stop word removal and stemming Regular expressions and pattern matching GPT-4 Tokenization and Input Formatting GPT-4 tokenization and its role in prompt engineering Understanding and formatting GPT-4 inputs Context windows and token limits Controlling response length and quality Techniques for handling out-of-vocabulary tokens Prompt Design and Optimization Master the skills to design, optimize, and test prompts for various business tasks. Designing effective prompts for different tasks Techniques for prompt optimization GPT-4 system and user parameters for controlling behavior Importance of prompt testing and iteration Best practices for prompt engineering in business applications Advanced Techniques and Tools in Prompt Engineering Learn advanced techniques and tools for prompt engineering and their integration in business applications. Conditional text generation with GPT-4 Techniques for handling multi-turn conversations Overview of tools for prompt engineering: OpenAI API, OpenAI Codex, and OpenAI Playground Integration of GPT-4 with other software platforms and tools Monitoring and maintaining prompt performance Code Generation and Testing with Prompt Engineering Develop the skills to generate, integrate, and test AI-generated code effectively, enhancing productivity and creativity in software development projects. Introduction to code generation with AI models like GPT-4 Designing prompts for code generation across programming languages Techniques for specifying requirements and constraints in prompts Generating and interpreting code snippets using AI-driven solutions Integrating generated code into existing projects and codebases Best practices for testing and validating AI-generated code Ethics and Responsible AI Understand the ethical implications of prompt engineering and the importance of responsible AI deployment in business. Ethical considerations in prompt engineering Bias in AI systems and its impact on prompt engineering Techniques to minimize bias and ensure fairness Best practices for responsible AI deployment in business applications Monitoring and addressing ethical concerns in prompt engineering

Polarization, or the “polarized mind,” is a fixation on one view, causing widespread destructiveness. It needs presence and love to address. We aim to explore the lived experiences on irregular perceptions of reality with an open mind. Each Saturday includes: a live dialogue between Prof. Ernesto Spinelli and an International Existential Therapist; a moment to share your thoughts and feelings with the teachers; and a final integration facilitated by Bárbara Godoy. This series of ten dialogues set out to explore the multifaceted dimentions and complexities associated with Existential Therapies. It attempts to engage with various interpretations of insanity through the lens of patients often painful, confounding, and deeply unsettling life experiences. Polarisation - between Prof. Ernesto Spinelli and Prof. Kirk Schneider “I view polarization or that which I call the “polarized mind” as the fixation on a single point of view to the utter exclusion of competing points of view, and I see it as a core dimension of human destructiveness both individually and collectively. Arguably, polarization or the polarized mind is responsible for more devaluation and abuse than any other general psychological dimension, and it crosses cultures, parties, disciplines and so-called diagnosed and undiagnosed populations. In fact it is the undiagnosed populations—polarized cultural, political, and religious leaders and their followers–who have arguably caused the most human destructiveness by far, over those whom we conventionally termed the diagnosed The polarized mind may be partly dispositional but appears to be largely fear-driven and requires abiding presence and love to address it. We are in a race against time to avail people to these “nutrients.” Prof. Kirk Schneider. Prof. Kirk J. Schneider, Ph.D. is a leading spokesperson for contemporary existential-humanistic and existential-integrative psychology. Dr Schneider was a 2022 Candidate for President of the American Psychological Association (APA), a co-founder and current president of the Existential-Humanistic Institute (an award-winning psychotherapy training center), and a two-term Member of the Council of Representatives of the APA. He is also past president (2015-2016) of the Society for Humanistic Psychology (Division 32) of the APA, recent past editor of the Journal of Humanistic Psychology (2005-2012), a founder and frequent presenter/facilitator of the bridge-building dialogue approach the Experiential Democracy Dialogue and a trained moderator for the conflict mediation group Braver Angels. Dr Schneider is also an adjunct faculty member at Saybrook University and Teachers College, Columbia University and an Honorary Member of the Society for Existential Analysis of the UK and the East European Association for Existential Therapy. He received the Rollo May Award for “outstanding and independent contributions” to the field of humanistic psychology from the Society for Humanistic Psychology, APA and is a Fellow of seven Divisions of the APA (5, 9, 32, 42, 12, 29, and 24). His work on existential-integrative psychotherapy has been featured in a special issue of the Journal of Psychotherapy Integration (March 2016), as well as The Wiley World Handbook of Existential Therapy and the APA’s forthcoming Handbook of Psychotherapy. Dr Schneider has published over 200 articles, interviews and chapters and has authored or edited 14 books including The Paradoxical Self, Horror and the Holy, Rediscovery of Awe, Awakening to Awe, The Spirituality of Awe, The Polarized Mind, The Handbook of Humanistic Psychology, Existential-Humanistic therapy, Existential-Integrative Psychotherapy, The Wiley World Handbook of Existential Therapy, The Depolarizing of America: A Guidebook for Social Healing and his latest volume (February, 2023) Life-Enhancing Anxiety: Key to a Sane World. Dr. Schneider’s work has been featured in Scientific American, the New York Times, USA Today, The Guardian, Vanity Fair, Forbes Health, Psychology Today, BBC World News and many other health and psychology outlets. Prof. Ernesto Spinelli was Chair of the Society for Existential Analysis between 1993 and 1999 and is a Life Member of the Society. His writings, lectures and seminars focus on the application of existential phenomenology to the arenas of therapy, supervision, psychology, and executive coaching. He is a Fellow of the British Psychological Society (BPS) as well as an APECS accredited executive coach and coaching supervisor. In 2000, he was the Recipient of BPS Division of Counselling Psychology Award for Outstanding Contribution to the Profession. And in 2019, Ernesto received the BPS Award for Distinguished Contribution to Practice. His most recent book, Practising Existential Therapy: The Relational World 2nd edition (Sage, 2015) has been widely praised as a major contribution to the advancement of existential theory and practice. Living up to the existential dictum that life is absurd, Ernesto is also the author of an on-going series of Private Eye novels. Date and Time: Saturday 12 April from 2 pm to 3 pm – (UK time) Individual Dialogue Fee: £70 Venue: Online Zoom FULL PROGRAMME 2025: 25 January “Knots” with Prof. Ernesto Spinelli and Bárbara Godoy 22 February “Healing” with Dr. Michael Guy Thompson and Prof. Ernesto Spinelli 22 March “Difference” with Prof. Tod DuBose and Prof. Ernesto Spinelli 12 April “Polarisation” with Prof. Kirk Schneider and Prof. Ernesto Spinelli 3 May “Character” with Prof. Robert Romanyshyn and Prof. Ernesto Spinelli 21 June “Opening” with Dr. Yaqui Martinez and Prof. Ernesto Spinelli 19 July “Meaning” with Dr. Jan Resnick and Prof. Ernesto Spinelli 25 October “Invention” with Dr. Betty Cannon and Prof. Ernesto Spinelli 15 November “Hallucination” with Prof. Simon du Plock and Prof. Ernesto Spinelli 13 December “Hysteria” with Bárbara Godoy and Prof. Ernesto Spinelli Read the full programme here > Course Organised by:

Feeling overwhelmed by multiple tasks? Ready to enhance your product management strategy with AI technology? It’s time to meet your new AI partner! Our course, “Unlocking the Power of AI,” will demonstrate how cutting-edge tools like Generative Pre-trained Transformers (GPTs) can simplify your workflow and bolster your decision-making process. In modern-day fast paced commercial enterprise world, adaptability is vital for success. As a product manager, you oversee the entire product lifecycle—from concept to launch and beyond. With Certified Product Management techniques, you can navigate changing market dynamics, prioritize features efficiently, and deliver value to customers quickly. However, agility alone isn’t sufficient. To excel in your role, embrace the potential of AI. By integrating AI into your practices, you can automate tasks, analyze data effortlessly, and make informed decisions. Picture having a virtual assistant capable of analyzing data and predicting market trends. With AI as your ally, you can focus on engaging customers, innovating, and strategic planning. Don’t hesitate. Embrace the future of product management now. Join us on this journey to unlock the full potential of AI, revolutionizing your workflow and achieving your goals faster than ever before. What You'll Learn (in just 3 hours!) AI 101 for Product Managers We'll break down the buzzwords and get you up to speed on how AI (especially those clever GPTs) can transform your work life. Market Research Master Think of your new AI pal as a super-powered market researcher. Learn how to analyze competitor data, customer feedback, and trends faster than you can say "pivot!" AI-Powered Strategy Say goodbye to gut feelings and hello to data-driven insights. Discover how AI helps you strategize, prioritize features, and build roadmaps that will make your product shine. Hands-on Workshop Dive into real-world scenarios and use GPT tools to tackle market analysis, craft user stories, and nail down your product roadmap. Ethics in the AI Age We'll explore responsible AI use and make sure you understand the potential pitfalls. Because with great power comes great responsibility! Our AI + Your Workflow = Dream Team We'll cover how to access our Product Management tool, how to use it effectively and fit it seamlessly into your existing processes. The future of Product Management is here, don’t get left behind. This course is perfect for Product managers and owners are essential drivers of product success, constantly challenged to balance priorities, navigate complex decisions, and foster innovation in competitive markets. With technology advancing rapidly and consumer preferences evolving, staying ahead can be daunting. Our training programs offer a solution. Designed for product managers and owners, they equip you with the tools, insights, and strategies to enhance productivity, make informed decisions, and ignite innovation. Our courses empower you to navigate modern challenges successfully. Whether you seek to refine strategic planning, optimize product development, or enhance customer engagement, our tailored programs cater to your needs. Join us on a journey to unlock your full potential and propel your career to new heights as a product manager or owner. The Takeaway Empowerment: Leave this workshop feeling empowered, armed with a potent toolkit for achieving product success. AI in Product Management: Recognize that AI is the future of product management, and this course will equip you to leverage its potential effectively. Leadership Position: Position yourself as a leader in product management by embracing AI and staying ahead of industry trends. Innovation: Embrace innovation and drive change within your organization with the insights gained from this course. Confidence: Approach the future with confidence, knowing that you have the skills and knowledge to navigate the evolving landscape of product management.

1-2-1 bespoke training course

Comprendre les Exigences de l’ISO 22000:2018 et du protocole de certification FSSC 22000 V6 Formation non officielle en français. Dispensée en ligne (Zoom) en direct par notre partenaire Omar Ksibi de Pro Alimentarius. Frais d'examen et de certificat inclus dans le prix.

Duration 4 Days 24 CPD hours This course is intended for This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud. Overview Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure. Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow. Prerequisites Creating cloud resources in Microsoft Azure. Using Python to explore and visualize data. Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow. Working with containers AI-900T00: Microsoft Azure AI Fundamentals is recommended, or the equivalent experience. 1 - Design a data ingestion strategy for machine learning projects Identify your data source and format Choose how to serve data to machine learning workflows Design a data ingestion solution 2 - Design a machine learning model training solution Identify machine learning tasks Choose a service to train a machine learning model Decide between compute options 3 - Design a model deployment solution Understand how model will be consumed Decide on real-time or batch deployment 4 - Design a machine learning operations solution Explore an MLOps architecture Design for monitoring Design for retraining 5 - Explore Azure Machine Learning workspace resources and assets Create an Azure Machine Learning workspace Identify Azure Machine Learning resources Identify Azure Machine Learning assets Train models in the workspace 6 - Explore developer tools for workspace interaction Explore the studio Explore the Python SDK Explore the CLI 7 - Make data available in Azure Machine Learning Understand URIs Create a datastore Create a data asset 8 - Work with compute targets in Azure Machine Learning Choose the appropriate compute target Create and use a compute instance Create and use a compute cluster 9 - Work with environments in Azure Machine Learning Understand environments Explore and use curated environments Create and use custom environments 10 - Find the best classification model with Automated Machine Learning Preprocess data and configure featurization Run an Automated Machine Learning experiment Evaluate and compare models 11 - Track model training in Jupyter notebooks with MLflow Configure MLflow for model tracking in notebooks Train and track models in notebooks 12 - Run a training script as a command job in Azure Machine Learning Convert a notebook to a script Run a script as a command job Use parameters in a command job 13 - Track model training with MLflow in jobs Track metrics with MLflow View metrics and evaluate models 14 - Perform hyperparameter tuning with Azure Machine Learning Define a search space Configure a sampling method Configure early termination Use a sweep job for hyperparameter tuning 15 - Run pipelines in Azure Machine Learning Create components Create a pipeline Run a pipeline job 16 - Register an MLflow model in Azure Machine Learning Log models with MLflow Understand the MLflow model format Register an MLflow model 17 - Create and explore the Responsible AI dashboard for a model in Azure Machine Learning Understand Responsible AI Create the Responsible AI dashboard Evaluate the Responsible AI dashboard 18 - Deploy a model to a managed online endpoint Explore managed online endpoints Deploy your MLflow model to a managed online endpoint Deploy a model to a managed online endpoint Test managed online endpoints 19 - Deploy a model to a batch endpoint Understand and create batch endpoints Deploy your MLflow model to a batch endpoint Deploy a custom model to a batch endpoint Invoke and troubleshoot batch endpoints

Why Learn Sketchup and Stable Diffusion Rendering Course? Course Link SketchUp and Stable Diffusion Rendering Course. An AI image creation course designed to explore AI image creation techniques and master the use of advanced AI technology. You'll learn Ai 3D modeling, advanced rendering, and lighting techniques. Duration: 16 hrs. Method: 1-on-1 Online Over Zoom is also available. Schedule: Tailor your own schedule by pre-booking a convenient hour of your choice, available from Mon to Sat between 9 am and 7 pm. The Sketchup and Stable Diffusion Rendering Course equips students with comprehensive skills for visually stunning Ai (Artificial intelligence) 3D models and renderings. Master Sketchup's user-friendly interface, advanced rendering techniques, and stable diffusion rendering. Hands-on exercises and real-world projects enhance learning. Ideal for architecture, interior design, product development, and visualization careers. The Sketchup and Stable Diffusion Rendering Course equips students with comprehensive skills for visually stunning Ai (Artificial intelligence) 3D models and renderings. Master Sketchup's user-friendly interface, advanced rendering techniques, and stable diffusion rendering. Hands-on exercises and real-world projects enhance learning. Ideal for architecture, interior design, product development, and visualization careers. Sketchup and Stable Diffusion Rendering Course (16 hours) Module 1: Introduction to Sketchup (2 hours) Overview of Sketchup software and interface navigation Basic drawing tools and geometry creation techniques Module 2: Texturing and Materials (2 hours) Applying textures and customizing materials Exploring texture mapping and material libraries Module 3: Lighting and Shadows (2 hours) Understanding lighting principles and light placement Creating realistic shadows and reflections Module 4: Advanced Modeling Techniques (3 hours) Creating complex shapes and utilizing advanced tools Working with groups, components, and modifiers Module 5: Stable Diffusion Rendering (2 hours) Introduction to stable diffusion rendering Configuring rendering settings for optimal results Module 6: Scene Composition and Camera Setup (2 hours) Exploring composition principles and camera perspectives Managing scenes and creating walkthrough animations Module 7: Rendering Optimization (2 hours) Optimizing models for faster rendering Using render passes and post-processing techniques Module 8: Project Work and Portfolio Development (1 hour) Applying skills to complete a real-world project Showcasing work in a professional portfolio Optional: Installing Stable Diffusion and Python (Additional 10 hours) Module 1: Introduction to Stable Diffusion and Python Overview of Stable Diffusion and Python's significance Module 2: System Requirements Hardware and software prerequisites for installation Module 3: Installing Python Step-by-step installation process for different OS Module 4: Configuring Python Environment Setting up environment variables and package managers Module 5: Installing Stable Diffusion Downloading and installing the Stable Diffusion package Module 6: Setting Up Development Environment Configuring IDEs for Python and Stable Diffusion Module 7: Troubleshooting and Common Issues Identifying and resolving common installation errors Module 8: Best Practices and Recommendations Managing Python and Stable Diffusion installations Module 9: Practical Examples and Projects Hands-on exercises demonstrating usage of Stable Diffusion and Python Module 10: Advanced Topics (Optional) Exploring advanced features and techniques Stable Diffusion UI v2 | A simple 1-click way to install and use https://stable-diffusion-ui.github.io A simple 1-click way to install and use Stable Diffusion on your own computer. ... Get started by downloading the software and running the simple installer. Learning Outcomes: Upon completing the Sketchup and Stable Diffusion Rendering Course, with a focus on AI image rendering, participants will: Master AI Image Rendering: Gain expertise in using AI-powered rendering techniques to create realistic and high-quality visualizations. Utilize Sketchup for 3D Modeling: Navigate the software, proficiently use drawing tools, and create detailed 3D models. Optimize Renderings: Apply AI-based rendering to optimize model visuals, achieving faster rendering times and superior image quality. Implement AI-driven Lighting and Shadows: Utilize AI algorithms for lighting placement, shadows, and reflections, enhancing realism in renderings. Create Professional Portfolio: Showcase AI-rendered projects in a professional portfolio, highlighting advanced image rendering skills. Note: The course focuses on AI image rendering using Sketchup and Stable Diffusion techniques, empowering participants with cutting-edge skills for creating exceptional visual representations.