This 0E038G: Advanced Predictive Modeling Using IBM SPSS Modeler v18.1.1 course presents advanced models to predict categorical and continuous targets. Before reviewing the models, data preparation issues are addressed such as partitioning, detecting anomalies, and balancing data. The participant is first introduced to a technique named PCA/Factor, to reduce the number of fields to a number of core fields, referred to as components or factors. The next units focus on supervised models, including Decision List, Support Vector Machines, Random Trees, and XGBoost. Methods are reviewed to combine supervised models and execute them in a single run, both for categorical and continuous targets.
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This course gets you up and running with a set of procedures for analyzing time series data. Learn how to forecast using a variety of models, including regression, exponential smoothing, and ARIMA, which take into account different combinations of trend and seasonality. The Expert Modeler features will be covered, which is designed to automatically select the best fitting exponential smoothing or ARIMA model, but you will also learn how to specify your own custom models, and also how to identify ARIMA models yourself using a variety of diagnostic tools such as time plots and autocorrelation plots.
If you are enrolling in a Self Paced Virtual Classroom or Web Based Training course, before you enroll, please review the Self-Paced Virtual Classes and Web-Based Training Classes on our Terms and Conditions page, as well as the system requirements, to ensure that your system meets the minimum requirements for this course. http://www.ibm.com/training/terms
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This course focuses on reviewing concepts of data science, where participants will learn the stages of a data science project. Topics include using automated tools to prepare data for analysis, build models, evaluate models, and deploy models. To learn about these data science concepts and topics, participants will use IBM SPSS Modeler as a tool.
If you are enrolling in a Self Paced Virtual Classroom or Web Based Training course, before you enroll, please review the Self-Paced Virtual Classes and Web-Based Training Classes on our Terms and Conditions page, as well as the system requirements, to ensure that your system meets the minimum requirements for this course.
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In this three-day course, you learn how to install, configure and administer IBM Spectrum Protect Plus v10.1.6. You begin with a review of the software capabilities, requirements, and architecture. Then, through lecture and hands-on labs, you learn how to perform the various tasks required to configure the environment on an installed virtual appliance. You customize SLA policies and make use of available options to protect virtual machines and applications, as well as the IBM Spectrum Protect Plus catalog. You monitor and manage jobs, plan and prepare for disaster recovery, view log files, and create custom reports.
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This course covers the steps to follow to update a Red Hat OpenShift Container Platform 4.3.18 environment on Power Systems to 4.3.23. Lessons learned, and hints and tips will be provided to help you perform the update successfully.
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This course broadens the skills of the student who are required to implement and manage a Kubernetes-based Platform as a Service (PaaS) environment based on Red Hat OpenShift Container Platform on Power Systems. The course covers basic administration and configuration of Red Hat OpenShift Container Platform within a POWER processor-based server configured with IBM PowerVC. Hands-on exercises reinforce the lecture material, and allow students to use the Red Hat OpenShift Container Platform to work with images, applications, and manage a cluster.
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This 2 day course is designed to provide skills enablement for system administrators and product support specialists in the area of operating system based virtualization provided by Linux containers. Topics include introduction to Podman, Buildah, Docker, Kubernetes and CRI-O. Hands-on exercises reinforce the lecture material, allowing students to install and configure Linux containers.
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This course is also available as classroom course IBM Aspera Console Administration (WT012G).
This ZT012G: IBM Aspera Console Administration course is intended to teach the necessary knowledge and skills to install, configure, and use IBM Aspera Console.
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This course is also available as classroom course IBM App Connect Enterprise V11 Application Development (WM668G).
The ZM668G: IBM App Connect Enterprise v11 Application Development provides connectivity and universal data transformation in heterogeneous IT environments. It enables businesses of any size to eliminate point-to-point connections and batch processing, regardless of operating system, protocol, and data format.
This course teaches you how to use IBM App Connect Enterprise to develop, deploy, and support message flow applications. These applications use various messaging topologies to transport messages between service requesters and service providers, and allow the messages to be routed, transformed, and enriched during processing.
In this course, you learn how to construct applications to transport and transform data. The course explores how to control the flow of data by using various processing nodes, and how to use databases and maps to transform and enrich data during processing. You also learn how to construct data models by using the Data Format Description Language (DFDL).
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This course is also available as classroom course WM156: IBM MQ V9.1 System Administration
This course teaches you how to customize, operate, administer, and monitor IBM MQ on-premises on distributed operating systems. The course covers configuration, day-to-day administration, problem recovery, security management, and performance monitoring. In addition to the recorded lectures, the hands-on exercises provide practical experience with distributed queuing, working with MQ clients, and implementing clusters, publish/subscribe messaging. You also learn how to implement authorization, authentication, and encryption, and you learn how to monitor performance.
Note:This course does not cover any of the features of MQ for z/OS or MQ for IBM i.
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This WT012G: IBM Aspera Console Administration course is also available as self-paced virtual (e-learning) course IBM Aspera Console Administration (ZT012G). This option does not require any travel.
This course is intended to teach the necessary knowledge and skills to install, configure, and use IBM Aspera Console.
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This course is intended to teach the necessary knowledge and skills to install, configure, and use the IBM Aspera High-Speed Transfer Server.
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This WF318G: Developing Applications in IBM Datacap v9.1.7 course provides technical professionals with the skills that are needed to build Datacap applications.
The course begins with an introduction to IBM Datacap. You learn about capture concepts, Datacap process, page identification methods, and architecture. You process batches for Datacap applications in the Datacap clients.
You learn about the design and components of a Datacap application. You build a Datacap application by using Forms Template in Datacap Studio and configure it. You learn how to troubleshoot a Datacap application. You configure a Datacap application to process documents of multiple page types in a single batch. You implement OCR and OMR to extract data from data fields and from multiple choice check boxes. You export data to a text file and also to an IBM FileNet Content Manager repository. You build page layouts, create virtual page blocks, and extract data from tables and label-value pairs. Through instructor-led presentations and hands-on lab exercises, you learn about the core features of IBM Datacap.
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In this 3-days TOD67G: Network Performance Insight 1.3.1 – Implementation and Configuration course, you learn how to install IBM Network Performance Insight and integrate it with IBM Netcool Operations Insight. This course is lab-intensive, with an emphasis on hands-on exercises.
If you are enrolling in a Self Paced Virtual Classroom or Web Based Training course, before you enroll, please review the Self-Paced Virtual Classes and Web-Based Training Classes on our Terms and Conditions page, as well as the system requirements, to ensure that your system meets the minimum requirements for this course. /terms
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IBM Network Performance Insight is a network performance monitoring system. It offers real-time and historical trends in network performance and interactive views of network data that help reduce network downtime and optimize network performance. Network Performance Insight provides IBM Netcool Operations Insight with comprehensive IP network device performance monitoring and traffic analysis. In this 3-day course, you learn how to install IBM Network Performance Insight and integrate it with IBM Netcool Operations Insight. This course is lab-intensive, with an emphasis on hands-on exercises.
If you are enrolling in a Self Paced Virtual Classroom or Web Based Training course, before you enroll, please review the Self-Paced Virtual Classes and Web-Based Training Classes on our Terms and Conditions page, as well as the system requirements, to ensure that your system meets the minimum requirements for this course.
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This learning offering will tell a holistic story of Cloud Pak for Data including collaboration across an organization, which is key in this platform. Applicable to all personas. Multiple use cases will provide understanding of how organizations can benefit from Cloud Pak for Data. A variety of features will also be explored, providing students with the insight on how to use the platform. This learning is relevant for Cloud Pak for Data and Cloud Pak for Data System. This WBT contains instructional and interactive content, demonstrations and hands-on simulated exercises.
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This course goes through the stages of a data science project from importing data to deployment, using services in Watson Studio and Watson Machine Learning for Cloud Pak for Data.
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IBM App Connect Enterprise provides connectivity and universal data transformation in heterogeneous IT environments. It enables businesses of any size to eliminate point-to-point connections and batch processing, regardless of operating system, protocol, and data format.
This course teaches you how to use IBM App Connect Enterprise to develop, deploy, and support message flow applications. These applications use various messaging topologies to transport messages between service requesters and service providers, and allow the messages to be routed, transformed, and enriched during processing.
In this course, you learn how to construct applications to transport and transform data. The course explores how to control the flow of data by using various processing nodes, and how to use databases and maps to transform and enrich data during processing. You also learn how to construct data models by using the Data Format Description Language (DFDL).
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This GCP-AWS: Google Cloud Fundamentals for AWS Professionals course is designed for AWS system administrators, solutions architects, and SysOps administrators who are familiar with AWS features and setup and want to gain experience configuring Google Cloud products immediately. This course uses lectures, demos, and hands-on labs to show you the similarities and differences between the two platforms and teach you about some basic tasks on Google Cloud.
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If you are new to Google Workspace, this training will equip you with the skills you need to be productive in the workplace. Through a series of lectures, demonstrations, and hands-on activities, you will become proficient in the use of the following core Google Workspace applications: Gmail, Google Calendar, Google Drive, Google Docs, Google Sheets, Google Slides, Google Meet and Google Chat.
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What is machine learning, and what kinds of problems can it solve? Why are neural networks so popular right now? How can you improve data quality and perform exploratory data analysis? How can you set up a supervised learning problem and find a good, generalizable solution using gradient descent? In this course, you’ll learn how to write distributed machine learning models that scale in Tensorflow 2.x, perform feature engineering in BQML and Keras, evaluate loss curves and perform hyperparameter tuning, and train models at scale with Cloud AI Platform.
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In this GCP-MPGC: ML Pipelines on Google Cloud course, you will learn about TensorFlow Extended (or TFX), which is Google’s production machine learning platform based on TensorFlow for management of ML pipelines and metadata. The first few modules discuss pipeline components, pipeline orchestration with TFX, how you can automate your pipeline through CI/CD, and how to manage ML metadata.
Then we will discuss how to automate and reuse ML pipelines across multiple ML frameworks such as tensorflow, pytorch, scikit learn, and xgboost. You will also learn how to use Cloud Composer to orchestrate your continuous training pipelines, and MLflow for managing the complete machine learning life cycle.
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This GCP-MLOF: MLOps (Machine Learning Operations) Fundamentals course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production.
Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.
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The Logging, Monitoring and Observability in Google Cloud training course teaches participants techniques for monitoring, troubleshooting, and improving infrastructure and application performance in Google Cloud.
Learn how to monitor, troubleshoot, and improve your infrastructure and application performance. Guided by the principles of Site Reliability Engineering (SRE), this official Google Cloud course features a combination of lectures, demos, hands-on labs, and real-world case studies. In this course, you’ll gain experience with full-stack monitoring, real-time log management and analysis, debugging code in production, and profiling CPU and memory usage.
