389 Courses in Cardiff delivered Live Online

SAP SD Training SAP SD (Sales & Distribution) module is a logistics module part which is a very important component of the SAP Business suite. SAP SD module empowers you to manage customer relationship starting from raising a quotation to sales order and billing of the product or service. This module is highly linked to other SAP modules like SAP Material Management and PP (Production Planning). Powered with an understanding and knowledge of the comprehensive functionality in SAP SD, you can easily manage the entire customer life cycle efficiently and intelligently. SAP SD Introductions and Master Data Document Types for Pre sales and Sales Documents Pricing Process and Determination Sales Cycles - sales processes Free goods Processing Revenue Account Determination SD Basic Functions Special Sales - Special Processes Invoicing Plan Variant Configuration Special Sales- Special Processes Case Study - business case Integration with Other Modules and support

Duration 3 Days 18 CPD hours This course is intended for This course is geared for experienced Scala developers who are new to the world of machine learning and are eager to expand their skillset. Professionals such as data engineers, data scientists, and software engineers who want to harness the power of machine learning in their Scala-based projects will greatly benefit from attending. Additionally, team leads and technical managers who oversee Scala development projects and want to integrate machine learning capabilities into their workflows can gain valuable insights from this course Overview Working in a hands-on learning environment led by our expert instructor you'll: Grasp the fundamentals of machine learning and its various categories, empowering you to make informed decisions about which techniques to apply in different situations. Master the use of Scala-specific tools and libraries, such as Breeze, Saddle, and DeepLearning.scala, allowing you to efficiently process, analyze, and visualize data for machine learning projects. Develop a strong understanding of supervised and unsupervised learning algorithms, enabling you to confidently choose the right approach for your data and effectively build predictive models Gain hands-on experience with neural networks and deep learning, equipping you with the know-how to create advanced applications in areas like natural language processing and image recognition. Explore the world of generative AI and learn how to utilize GPT-Scala for creative text generation tasks, broadening your skill set and making you a more versatile developer. Conquer the realm of scalable machine learning with Scala, learning the secrets to tackling large-scale data processing and analysis challenges with ease. Sharpen your skills in model evaluation, validation, and optimization, ensuring that your machine learning models perform reliably and effectively in any situation. Machine Learning Essentials for Scala Developers is a three-day course designed to provide a solid introduction to the world of machine learning using the Scala language. Throughout the hands-on course, you?ll explore a range of machine learning algorithms and techniques, from supervised and unsupervised learning to neural networks and deep learning, all specifically crafted for Scala developers. Our expert trainer will guide you through real-world, focused hands-on labs designed to help you apply the knowledge you gain in real-world scenarios, giving you the confidence to tackle machine learning challenges in your own projects. You'll dive into innovative tools and libraries such as Breeze, Saddle, DeepLearning.scala, GPT-Scala (and Generative AI with Scala), and TensorFlow-Scala. These cutting-edge resources will enable you to build and deploy machine learning models for a wide range of projects, including data analysis, natural language processing, image recognition and more. Upon completing this course, you'll have the skills required to tackle complex projects and confidently develop intelligent applications. You?ll be able to drive business outcomes, optimize processes, and contribute to innovative projects that leverage the power of data-driven insights and predictions. Introduction to Machine Learning and Scala Learning Outcome: Understand the fundamentals of machine learning and Scala's role in this domain. What is Machine Learning? Machine Learning with Scala: Advantages and Use Cases Supervised Learning in Scala Learn the basics of supervised learning and how to apply it using Scala. Supervised Learning: Regression and Classification Linear Regression in Scala Logistic Regression in Scala Unsupervised Learning in Scala Understand unsupervised learning and how to apply it using Scala. Unsupervised Learning:Clustering and Dimensionality Reduction K-means Clustering in Scala Principal Component Analysis in Scala Neural Networks and Deep Learning in Scala Learning Outcome: Learn the basics of neural networks and deep learning with a focus on implementing them in Scala. Introduction to Neural Networks Feedforward Neural Networks in Scala Deep Learning and Convolutional Neural Networks Introduction to Generative AI and GPT in Scala Gain a basic understanding of generative AI and GPT, and how to utilize GPT-Scala for natural language tasks. Generative AI: Overview and Use Cases Introduction to GPT (Generative Pre-trained Transformer) GPT-Scala: A Library for GPT in Scala Reinforcement Learning in Scala Understand the basics of reinforcement learning and its implementation in Scala. Introduction to Reinforcement Learning Q-learning and Value Iteration Reinforcement Learning with Scala Time Series Analysis using Scala Learn time series analysis techniques and how to apply them in Scala. Introduction to Time Series Analysis Autoregressive Integrated Moving Average (ARIMA) Models Time Series Analysis in Scala Natural Language Processing (NLP) with Scala Gain an understanding of natural language processing techniques and their application in Scala. Introduction to NLP: Techniques and Applications Text Processing and Feature Extraction NLP Libraries and Tools for Scala Image Processing and Computer Vision with Scala Learn image processing techniques and computer vision concepts with a focus on implementing them in Scala. Introduction to Image Processing and Computer Vision Feature Extraction and Image Classification Image Processing Libraries for Scala Model Evaluation and Validation Understand the importance of model evaluation and validation, and how to apply these concepts using Scala. Model Evaluation Metrics Cross-Validation Techniques Model Selection and Tuning in Scala Scalable Machine Learning with Scala Learn how to handle large-scale machine learning problems using Scala. Challenges of Large-Scale Machine Learning Data Partitioning and Parallelization Distributed Machine Learning with Scala Machine Learning Deployment and Production Understand the process of deploying machine learning models into production using Scala. Deployment Challenges and Best Practices Model Serialization and Deserialization Monitoring and Updating Models in Production Ensemble Learning Techniques in Scala Discover ensemble learning techniques and their implementation in Scala. Introduction to Ensemble Learning Bagging and Boosting Techniques Implementing Ensemble Models in Scala Feature Engineering for Machine Learning in Scala Learn advanced feature engineering techniques to improve machine learning model performance in Scala. Importance of Feature Engineering in Machine Learning Feature Scaling and Normalization Techniques Handling Missing Data and Categorical Features Advanced Optimization Techniques for Machine Learning Understand advanced optimization techniques for machine learning models and their application in Scala. Gradient Descent and Variants Regularization Techniques (L1 and L2) Hyperparameter Tuning Strategies

Duration 3 Days 18 CPD hours This course is intended for Application ConsultantBusiness AnalystBusiness Process ArchitectBusiness Process Owner / Team Lead / Power UserExecutiveIndustry SpecialistProgram/Project Manager Overview This course will prepare you to:Describe the motivation for S/4HANA Enterprise ManagementExecute innovative logistics processes in the areas of logistics planning and execution in S/4HANA, especially in manufacturing, procurement, and salesLearn about the simplification listOutline the SAP User Experience strategyUse SAP Fiori among other user interfaces to execute logistics processesOutline different migration strategies Students will learn to execute innovative logistics processes in the areas of logistics planning and execution in S/4HANA, especially in manufacturing, procurement, and sales. Motivation and Functional Overview of SAP S4/HANA (Materials Management and Operations)Simplification ListSAP User Experience, especially SAP FioriInnovative Logistics Processes in the Areas of Logistics Planning and Execution in S/4HANA, especially in Manufacturing, including MRP Life and Production Planning / Detailed Scheduling (PP/DS) Procurement, Sales, including Back Order Processing, and Extended Warehouse Management (EWM) Migration Scenarios

2-part workshop for parents hosted by Rachel Wright, Founder of Born at the Right Time and author of 'The Skies I'm Under'.

Why Choose 3ds Max and V-Ray Masterclass? V-Ray for 3ds Max stands as a proven rendering software, offering unparalleled versatility. From dynamic scenes with thousands of lights to elegant still life compositions, best choice for artists and designers across various 3D industries. Check our Website Training Details: Duration: 10 hrs Method: 1-on-1, Personalized attention and Tailored content. Schedule: Customize your learning experience with our flexible 1-2-1 sessions, available Mon to Sat between 9 am and 7 pm. Module 1: Introduction to 3ds Max and Vray Get acquainted with 3ds Max interface and Vray's user interface. Explore workspace configuration and navigation techniques. Module 2: 3D Modeling Master fundamental shapes and advanced modeling methods. Understand polygonal modeling for organic and inorganic objects. Module 3: Materials and Textures Apply and modify textures, incorporating UVW mapping. Utilize the material editor for custom and Vray materials. Module 4: Lighting Explore diverse lighting techniques and light types. Configure lights for scenes, incorporating Vray lights. Module 5: Cameras Familiarize yourself with camera properties and setup. Create animations using both standard and Vray cameras. Module 6: Rendering Understand rendering basics and Vray Global Illumination. Implement Vray HDRI Lighting and the Rendering Workflow. Module 7: Projects Integrate learned skills in practical projects. Craft interior, exterior, and complex scenes with various objects and materials. Course Requirements: Computer with 3ds Max and Vray installed (trial versions available). Basic computer operation skills. Interest in 3D modeling and animation. Course Goals: Upon completion, you'll have a comprehensive grasp of 3ds Max and Vray, enabling you to create realistic 3D scenes. You'll possess the skills for compelling visual storytelling and be prepared for further advancement in 3D modeling and rendering. Upon completing the course, participants will: Master 3D Modeling: Create intricate 3D models using advanced techniques. Expert Material Application: Apply textures and materials for realistic effects. Advanced Lighting Skills: Set up diverse lighting, including Vray-specific techniques. Camera Mastery: Understand camera properties and animations for visual storytelling. Rendering Proficiency: Utilize Vray's capabilities for stunning visuals. Project Integration: Apply skills in practical projects, from interiors to complex scenes. Job Opportunities: Graduates can pursue roles like 3D Modeler, Texture Artist, Lighting Artist, Visualizer, 3D Animator, or Rendering Specialist in animation studios, architectural firms, game development, and film production. Opportunities exist as freelancers or within creative agencies. What You'll Learn: Comprehensive 3ds Max Mastery: From foundational concepts to advanced techniques, delve deep into 3ds Max's extensive toolkit for 3D modeling, animation, and rendering. V-Ray Expertise: Master the nuances of V-Ray, honing your skills in photorealistic rendering and lighting techniques to bring your creations to life. Portfolio-Ready Projects: Work on industry-aligned projects that enhance your skills and create a robust portfolio showcasing your abilities. Optimized Workflow: Learn efficient workflows, time-saving techniques, and industry best practices from experienced professionals. Career Opportunities: Upon completion of this course, you'll be equipped for various roles such as: 3D Modeler: Create detailed 3D models for gaming, animation, or architectural visualization. Rendering Artist: Specialize in creating realistic renderings using V-Ray for architectural projects or product design. Game Designer: Develop immersive game environments and assets using 3ds Max and Unity 3D. Visualization Specialist: Work in architectural firms, creating lifelike visualizations for clients. Recommended Websites for 3D Models: TurboSquid: A leading marketplace for high-quality 3D models. CGTrader: Offers a wide range of 3D models for games, VR, and architectural visualization. Sketchfab: An excellent platform for finding interactive 3D models for various purposes. 3D Warehouse (by SketchUp): A vast collection of user-created models suitable for architectural projects. Free3D: Provides a vast selection of free 3D models across different categories. Enroll in our course to unlock your 3D design potential and explore the world of limitless creativity! Highly Skilled Instructors: Learn from certified professionals with extensive 3ds Max expertise and real-world experience. Flexible Training Choices: Opt for either in-person or live online sessions tailored to your schedule and preferences. Comprehensive Learning: Master advanced 3ds Max techniques for creating exceptional 3D models, animations, and visual effects. Interactive Learning: Engage in hands-on projects and exercises to reinforce complex concepts and enhance practical skills. Lesson Recordings: Access recorded sessions at your convenience for reviewing crucial topics at your own pace. Lifetime Email Assistance: Receive continuous email support post-course completion, ensuring guidance whenever required. Expert Career Guidance: Access valuable insights and career advice from industry experts to make informed decisions in your field.

Autodesk face to face training customised and bespoke. Certified Tutors.

Designed for professional B2B salespeople to develop a modern approach to winning business

Designed for professional B2B salespeople to develop a modern approach to winning business

A unique CPD accredited course delivered online - exploring domestic violence and abuse and coercive control - utilising lived experience and dynamic original theatre to help you make sure your first response is the right response

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