45 Courses in Nottingham delivered Live Online

Duration 1 Days 6 CPD hours This course is intended for This course is intended for: A technical audience at an intermediate level Overview Using Amazon SageMaker, this course teaches you how to: Prepare a dataset for training. Train and evaluate a machine learning model. Automatically tune a machine learning model. Prepare a machine learning model for production. Think critically about machine learning model results In this course, learn how to solve a real-world use case with machine learning and produce actionable results using Amazon SageMaker. This course teaches you how to use Amazon SageMaker to cover the different stages of the typical data science process, from analyzing and visualizing a data set, to preparing the data and feature engineering, down to the practical aspects of model building, training, tuning and deployment. Day 1 Business problem: Churn prediction Load and display the dataset Assess features and determine which Amazon SageMaker algorithm to use Use Amazon Sagemaker to train, evaluate, and automatically tune the model Deploy the model Assess relative cost of errors Additional course details: Nexus Humans Practical Data Science with Amazon SageMaker training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Practical Data Science with Amazon SageMaker course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 4 Days 24 CPD hours This course is intended for This course is intended for: Developers Solutions Architects Data Engineers Anyone with little to no experience with ML and wants to learn about the ML pipeline using Amazon SageMaker Overview In this course, you will learn to: Select and justify the appropriate ML approach for a given business problem Use the ML pipeline to solve a specific business problem Train, evaluate, deploy, and tune an ML model using Amazon SageMaker Describe some of the best practices for designing scalable, cost-optimized, and secure ML pipelines in AWS Apply machine learning to a real-life business problem after the course is complete This course explores how to use the machine learning (ML) pipeline to solve a real business problem in a project-based learning environment. Students will learn about each phase of the pipeline from instructor presentations and demonstrations and then apply that knowledge to complete a project solving one of three business problems: fraud detection, recommendation engines, or flight delays. By the end of the course, students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem. Module 0: Introduction Pre-assessment Module 1: Introduction to Machine Learning and the ML Pipeline Overview of machine learning, including use cases, types of machine learning, and key concepts Overview of the ML pipeline Introduction to course projects and approach Module 2: Introduction to Amazon SageMaker Introduction to Amazon SageMaker Demo: Amazon SageMaker and Jupyter notebooks Hands-on: Amazon SageMaker and Jupyter notebooks Module 3: Problem Formulation Overview of problem formulation and deciding if ML is the right solution Converting a business problem into an ML problem Demo: Amazon SageMaker Ground Truth Hands-on: Amazon SageMaker Ground Truth Practice problem formulation Formulate problems for projects Module 4: Preprocessing Overview of data collection and integration, and techniques for data preprocessing and visualization Practice preprocessing Preprocess project data Class discussion about projects Module 5: Model Training Choosing the right algorithm Formatting and splitting your data for training Loss functions and gradient descent for improving your model Demo: Create a training job in Amazon SageMaker Module 6: Model Evaluation How to evaluate classification models How to evaluate regression models Practice model training and evaluation Train and evaluate project models Initial project presentations Module 7: Feature Engineering and Model Tuning Feature extraction, selection, creation, and transformation Hyperparameter tuning Demo: SageMaker hyperparameter optimization Practice feature engineering and model tuning Apply feature engineering and model tuning to projects Final project presentations Module 8: Deployment How to deploy, inference, and monitor your model on Amazon SageMaker Deploying ML at the edge Demo: Creating an Amazon SageMaker endpoint Post-assessment Course wrap-up Additional course details: Nexus Humans The Machine Learning Pipeline on AWS training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the The Machine Learning Pipeline on AWS course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 1 Days 6 CPD hours This course is intended for This course is intended for: Developers responsible for developing Deep Learning applications Developers who want to understand concepts behind Deep Learning and how to implement a Deep Learning solution on AWS Overview This course is designed to teach you how to: Define machine learning (ML) and deep learning Identify the concepts in a deep learning ecosystem Use Amazon SageMaker and the MXNet programming framework for deep learning workloads Fit AWS solutions for deep learning deployments In this course, you?ll learn about AWS?s deep learning solutions, including scenarios where deep learning makes sense and how deep learning works. You?ll learn how to run deep learning models on the cloud using Amazon SageMaker and the MXNet framework. You?ll also learn to deploy your deep learning models using services like AWS Lambda while designing intelligent systems on AWS. Module 1: Machine learning overview A brief history of AI, ML, and DL The business importance of ML Common challenges in ML Different types of ML problems and tasks AI on AWS Module 2: Introduction to deep learning Introduction to DL The DL concepts A summary of how to train DL models on AWS Introduction to Amazon SageMaker Hands-on lab: Spinning up an Amazon SageMaker notebook instance and running a multi-layer perceptron neural network model Module 3: Introduction to Apache MXNet The motivation for and benefits of using MXNet and Gluon Important terms and APIs used in MXNet Convolutional neural networks (CNN) architecture Hands-on lab: Training a CNN on a CIFAR-10 dataset Module 4: ML and DL architectures on AWS AWS services for deploying DL models (AWS Lambda, AWS IoT Greengrass, Amazon ECS, AWS Elastic Beanstalk) Introduction to AWS AI services that are based on DL (Amazon Polly, Amazon Lex, Amazon Rekognition) Hands-on lab: Deploying a trained model for prediction on AWS Lambda Additional course details: Nexus Humans Deep Learning on AWS training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Deep Learning on AWS course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 2 Days 12 CPD hours This course is intended for Application Consultants, Business Analysts, and Program Managers Overview Describe how to organize and run payroll including subsequent activities and problem-solving aids This course provides the mandatory foundation knowledge required for processing payroll transactions in SAP HCM. Payroll Overview Setting Up the User Interface Identifying Payroll Elements Payroll Data Entering payroll data Payroll Elements Organizing a Payroll Run Reviewing the Payroll Status Infotype Identifying Retroactive Payroll Entries Payroll Process Running Payroll Payroll Reports Reporting on Payroll Generating Remuneration Statements Analyzing Payroll Results Analyzing Wage Types Reviewing Ad Hoc Query Functionality Post Payroll Results Posting Environments Verifying a Posting Run Updating a Live Posting Run Verifying Documents Bank Transfers & Check Preparation Generating Employee Payments Process Model Running a Payroll Process Model SuccessFactors Employee Central Payroll Outling employee central payroll basics

Duration 5 Days 30 CPD hours This course is intended for Implementer Overview Enrolling in this course will help you better understand and leverage PeopleSoft Payroll for North America (USA) payroll requirements and processes. This knowledge will help you create, adjust and troubleshoot your organization's employee payroll. Learn Off-Cycle Payroll Events In addition to typical on-cycle payroll processing, Payroll for North America provides functionality for a variety of off-cycle payroll events and other payroll requirements. This course will illustrate how to reverse a paycheck, record a manual check, produce online checks and final checks and create a gross up check. In addition, you'll also learn the necessary steps to set up and process mid-period job changes and multiple jobs. Finally, you'll set up and processes benefit deductions and garnishments. This PeopleSoft Payroll - US Rel 9.2 training teaches you how to set up and maintain employee tax data, additional pay, general deductions and direct deposits. Expert Oracle University instructors will show you how to use this solution to calculate payroll, review calculation results, identify and correct errors, confirm payroll and more. Setting Up PeopleSoft HRMS Tables for Payroll Processing Identifying HRMS Tables That Impact Payroll Processing Describing Installation Table Setup for PeopleSoft Enterprise Payroll for North America Describing the Role of SetID, Location, and Department in Payroll Processing Describing Company Table Setup Desc Setting Up Payroll Tables Identifying Payroll Setup Tables Setting Up Source Bank Accounts Creating Special Accumulators Setting Up Earnings Codes Setting Up Earnings Programs Describing Shift Pay Setup Creating a Pay Group Creating a Rate Code Setting Up U.S. Payroll Tax Tables Identifying Tax Table Maintenance Responsibility Describing PeopleSoft-Maintained Tax Tables Setting Up Customer-Maintained Tax Tables Setting Up Employee Data Identifying Sources of Employee Data Setting Up an Employee Instance Entering and Viewing Employee Job Data Identifying Employee Pay Data Updating Employee Tax Data Assigning Additional Pay to Employees Assigning General Deductions to Employees Updating P Creating and Updating Paysheets Describing Paysheets in Payroll for North America Describing Balance ID Creation Creating Pay Calendars Setting Up Pay Run IDs Creating Paysheets Viewing and Updating Paysheets and Paylines Describing the Payroll Unsheet Process Calculating Payroll Describing Payroll Calculation Running Preliminary Payroll Calculation Running Final Payroll Calculation Confirming Payroll and Producing Checks and Reports Confirming a Payroll Viewing Confirmed Payroll Results Online Printing Checks and Advices Describing Check Reprinting Describing Direct Deposit Transmittals and Reports Describing Payroll and Tax Reports Identifying Methods of Reversing Payroll Confirmati Processing Off-Cycle Payrolls Identifying Off-Cycle Payroll Processing Reversing a Paycheck Describing Reversal/Adjustment Processing Recording a Manual Check Producing an Online Check Describing Retroactive Processing Setting Up and Processing Additional Payroll Functionality Setting Up a Holiday Schedule Setting Up Advanced Earnings Options Setting Up Multiple Jobs Processing Setting Up and Processing Mid-period Job Changes Processing Gross Ups Setting Up and Processing Benefit Deductions in Payroll Setting Up Deductions and Earnings for Benefits Calculations Updating a Benefit Program and Enrolling Employees Analyzing Benefits-Related Payroll Reports Setting Up and Processing Garnishments Identifying Garnishment Setup Steps Describing PeopleSoft-Maintained Garnishment Tables Setting Up Company-Level Tables for Garnishment Processing Assigning an Employee Garnishment Deduction Setting Up Employee Garnishment Specification Data Reviewing Emp Describing PeopleSoft Enterprise ePay Identifying HRMS Tables That Impact Payroll Processing Describing Installation Table Setup for PeopleSoft Enterprise Payroll for North America Describing the Role of SetID, Location, and Department in Payroll Processing Describing Company Table Setup Desc