07 Sep 2021

Administering WebSphere Application Server Liberty Profile V9 – WA190G CR,ILO

Short Summary


This course teaches you the skills that are needed to manage Liberty servers and collectives.

 

Details


Course Code: WA190G

Brand: Management & Platform

Category: Cloud

Skill Level: Basic

Duration: 16.00H

Modality: CR,ILO

 

Audience


This course is designed for administrators of IBM WebSphere Application Server Liberty Profile.

 

Prerequisites


Before taking this course, you should have a general knowledge of:

  • Java Platform, Enterprise Edition (Java EE)
  • Administering web servers and application servers
  • The Ubuntu Linux operating system

 

Overview


This course teaches you the skills that are needed to manage Liberty servers and collectives.

The course is designed for application server administrators. You learn how to use the graphical Admin Center and the command line scripting to manage servers from a collective controller. The course also covers how to deploy a cluster of packaged servers for Liberty runtimes, view the deployment environment, and view basic performance metrics.

You learn how to use the Dynamic Routing feature of Liberty to enable routing of HTTP requests to collective members. You also configure the auto-scaling and health management features for Liberty.

Finally, you learn how to secure Liberty and enable SSL communication in Liberty.

For information about other related courses, see the IBM Training website:

http:www.ibm.com/training

 

Topic


Course introduction
Introduction to Liberty administration and runtime architecture
Multi-server management
Exercise: Managing Liberty collectives with the Admin Center
Administration and application deployment with scripting
Exercise: WebSphere Liberty administration by using Jython Scripts
Dynamic Routing
Exercise: Dynamic Routing
Auto-scaling in Liberty
Exercise: Auto-scaling
Securing Liberty
Exercise: Using the IBM HTTP Server with SSL to a Liberty server
Course summary

 

Objectives


After completing this course, you should be able to:

  • Describe the WebSphere Liberty Profile architecture
  • Create a Liberty profile server
  • Use the Admin Center to manage Liberty servers
  • Deploy clusters of Liberty servers
  • Use the collective controller
  • Use Jython scripts to administer Liberty servers
  • Configure Dynamic Routing
  • Configure the auto scaling feature and define auto scaling policies
  • Configure SSL communication in Liberty
  • Use the IBM HTTP and web server plug-in with Liberty servers
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07 Sep 2021

WebSphere Application Server V9 Administration – WA590G CR,ILO

Short Summary


This course teaches you the skills that are needed to administer IBM WebSphere Application Server V9.

 

Details


Course Code: WA590G

Brand: Management & Platform

Category: Cloud

Skill Level: Intermediate

Duration: 20.00H

Modality: CR,ILO

 

Audience


This course is designed for administrators who configure and manage web-based applications on WebSphere Application Server. Web administrators, application developers and deployers, security specialists, and application architects can also benefit from this course.

 

Prerequisites


  • Basic operational skills for the Linux operating system
  • Administrative skills for a web server, such as IBM HTTP Server or Apache
  • Basic understanding of cloud concepts, private, public, and hybrid clouds, and traditional on-premises environments

 

Overview


This course teaches you the skills that are needed to administer IBM WebSphere Application Server V9.

This release of IBM WebSphere Application Server provides enhanced support for standards (notably Java 7 EE), emerging technology, and a choice of development frameworks.

In this course, you learn how to configure and maintain IBM WebSphere Application Server V9 in a single-server environment. You learn how to deploy enterprise Java applications in a single computer configuration. In addition, you learn how to work with features of WebSphere Application Server V9, such as the wsadmin scripting interface, security, and performance monitoring.

Hands-on exercises throughout the course give you practical experience with the skills you develop in the lectures.

For information about other related courses, see the IBM Training website:

http://www.ibm.com/training

 

Topic


Course introduction
WebSphere product family overview
WebSphere Application Server architecture – stand-alone
Exercise: Profile creation
WebSphere Application Server administrative console
Exercise: Exploring the administrative console
Introduction to the PlantsByWebSphere application
Application assembly
Exercise: Assembling an application
Application installation
Exercise: Installing an application
Problem determination
Exercise: Problem determination
Introduction to wsadmin and scripting
Exercise: Using wsadmin
WebSphere security
Exercise: Configuring WebSphere Application Server security
Exercise: Configuring application security
Performance monitoring
Exercise: Using the performance monitoring tools
Course summary

 

Objectives


After completing this course, you should be able to:

  • Relate WebSphere Application Server to the WebSphere family of products
  • Describe the features and standards in WebSphere Application Server V9
  • Describe the use of WebSphere Application Server in cloud, hybrid cloud, and on-premises environments
  • Describe the architectural concepts that are related to WebSphere Application Server
  • Assemble and install server-side Java enterprise applications
  • Use WebSphere administrative tools to configure and manage enterprise applications
  • Use wsadmin scripting
  • Configure WebSphere Application Server security
  • View performance information about server and application components
  • Troubleshoot problems by using problem determination tools and log files
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07 Sep 2021

Machine Learning Rapid Prototyping with IBM Watson Studio – W7072G WBT

Short Summary


This course will take the learner through the creation of an end-to-end automated pipeline built by Watson Studio’s AutoAI experiment tool, explaining the underlying technology at work.

 

Details


Course Code: W7072G

Brand: Watson

Category: Analytics

Skill Level: Intermediate

Duration: 9.00H

Modality: WBT

 

Audience


This course is intended for practicing Data Scientists. While it showcases the automated AI capabilities of IBM Watson Studio with AutoAI, the course does not explain Machine Learning or Data Science concepts.

 

Prerequisites


In order to be successful, you should have knowledge of:

 

– Data Science workflow, Data Preprocessing, Feature Engineering, Machine Learning Algorithms, Hyperparameter Optimization, Evaluation measures for models, Python and scikit-learn library (including Pipeline class)

 

Overview


An emerging trend in AI is the availability of technologies in which automation is used to select a best-fit model, perform feature engineering and improve model performance via hyperparameter optimization. This automation will provide rapid-prototyping of models and allow the Data Scientist to focus their efforts on applying domain knowledge to fine-tune models. This course will take the learner through the creation of an end-to-end automated pipeline built by Watson Studio’s AutoAI experiment tool, explaining the underlying technology at work as developed by IBM Research. The focus will be on working with an auto-generated Python notebook. Learners will be provided with test data sets for two use cases.

 

Purchase this course as part of a subscription

  • IBM Data/AI Individual Subscription (SUBR003G)
  • IBM Digital Learning Subscription — IBM Data/AI Enterprise Subscription (SUBR004G)

 

Topic


Building a rapid prototype of Watson Studio AI

– Describe the benefits of AutoAI for rapid prototyping
– Identify implementations of AutoAI
– Become familiar with the Watson Studio platform
– Build rapid prototypes using Watson Studio AutoAI
– Generate a Python notebook of the prototype with one click

Automated Data Preparation and Model Selection

– Evaluate the data preprocessing steps for the use cases
– Refine data preprocessing using the AutoAI-generated Python notebook
– Examine the model selection outcome for use cases
– Refine the Python notebook to make changes to the selected model

Automated Feature Engineering and Hyperparameter Optimization

– Explain how the Cognito algorithm can save time by automating feature engineering
– Evaluate the automated feature engineering performance for the use cases
– Describe several strategies for HPO in order of increasing sophistication
– Observe how changes to the model hyperparameters in the Python notebook affect the prototype’s performance

Evaluation and Deployment of AutoAI-generated Solutions

– Evaluate the prototype for further development or deployment based on calculated performance metrics
– Deploy the prototype using Watson Machine Learning
 

Objectives


  • Building a rapid prototype of Watson Studio AI
  • Automated Data Preparation and Model Selection
  • Automated Feature Engineering and Hyperparameter Optimization
  • Evaluation and Deployment of AutoAI-generated Solutions
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07 Sep 2021

The AI Ladder: A Framework for Deploying AI in your Enterprise – W7100G WBT

Short Summary


This course is intended for business and technical professionals involved in strategic decision-making focused on bringing AI into their enterprises.

 

Details


Course Code: W7100G

Brand: IBM Analytics

Category: Analytics

Skill Level: Basic

Duration: 3.00H

Modality: WBT

 

Audience


This course is designed for business and technical leaders who are considering AI-based solutions for business challenges in their enterprises.

 

Prerequisites


NonE

 

Overview


This course is designed for business and technical leaders who are considering AI-based solutions for business challenges in their enterprises.  First, this course will provide learners with a business-oriented summary of today’s technologies and themes in AI.  Following this summary, we will cover the concept of modernizing your information architecture (IA), summarized in the phrase, “…there is no AI without IA”. Most people think implementing AI is all about using AI technologies, but the reality is quite different. While you may be able to build small AI projects just focusing on AI technologies, the projects that will have the most impact on your business will require you to leverage not just AI technologies but other technologies related to data: your information architecture. Next, learners will be introduced to the concept of the AI Ladder.  The AI Ladder is a framework for understanding the work and processes that are necessary for the successful deployment of AI-based solutions in large enterprises.  This course will cover each step of the AI Ladder, explaining the relevance of each step and the work involved at each step. After completing this course learners will be equipped with a powerful set of strategic tools to use in successfully developing, deploying, and maintaining AI-based business solutions.

 

IBM Clients and Sellers – Consider this course as part of an individual or enterprise subscription service:

  • IBM Data/AI Individual Subscription (SUBR003G)
  • IBM Digital Learning Subscription — IBM Data/AI Enterprise Subscription (SUBR004G)
  • IBM Learning Individual Subscription with Red Hat Learning Services (SUBR013G)

 

Topic


  • Introduction to the AI Ladder and Fundamentals of AI
  • No AI without IA
  • First Step in the Ladder: Collect Data
  • Second Step in the Ladder: Organize Data
  • Third Step in the Ladder: Analyze Data
  • Final Step in the Ladder: Infuse AI
  • End of module review & evaluation

 

Objectives


  • Summarize the main technologies and themes in today’s AI landscape.
  • Explain the concept of the AI ladder and its relevance to business enterprises.
  • Describe the importance of information architecture to successful AI implementations.
  • Define and elaborate the “Collect” step of the AI Ladder
  • Define and elaborate the “Organize” step of the AI Ladder
  • Define and elaborate the “Analyze” step of the AI Ladder
  • Define and elaborate the “Infuse” step of the AI Ladder
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07 Sep 2021

Exploratory Data Analysis for Machine Learning – W7101G WBT

Short Summary


This course introduces you to Machine Learning. You will learn the importance of good, quality data, and how to retrieve it, clean it, and apply feature engineering.

 

Details


Course Code: W7101G

Brand: Cloud & Data Platform

Category: Cloud

Skill Level: Intermediate

Duration: 8.00H

Modality: WBT

 

Audience


This course targets aspiring data scientists interested in acquiring hands-on experience with Machine Learning and Artificial Intelligence in a business setting.

 

Prerequisites


To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Calculus, Linear Algebra, Probability, and Statistics.

 

Overview


This first course in the IBM Machine Learning Professional Certificate introduces you to Machine Learning and the content of the professional certificate. In this course you will realize the importance of good, quality data. You will learn common techniques to retrieve your data, clean it, apply feature engineering, and have it ready for preliminary analysis and hypothesis testing.

 

IBM Clients and Sellers – Consider this course as part of an individual or enterprise subscription service:

  • IBM Data/AI Individual Subscription (SUBR003G)
  • IBM Digital Learning Subscription — IBM Data/AI Enterprise Subscription (SUBR004G)
  • IBM Learning Individual Subscription with Red Hat Learning Services (SUBR013G)

 

Topic


1. A Brief History of Modern AI and its Applications

2. Retrieving Data, Exploratory Data Analysis, and Feature Engineering

3. Inferential Statistics and Hypothesis Testing

 

Objectives


By the end of this course you should be able to:
– Retrieve data from multiple data sources: SQL, NoSQL databases, APIs, Cloud. Describe and use common feature selection and feature engineering techniques.

– Handle categorical and ordinal features, as well as missing values.

– Use a variety of techniques for detecting and dealing with outliers.

– Articulate why feature scaling is important and use a variety of scaling techniques.

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07 Sep 2021

Supervised Learning: Regression – W7102G WBT

Short Summary


This course introduces you to one of the main types of modelling families of supervised Machine Learning: Regression

 

Details


Course Code: W7102G

Brand: Cloud & Data Platform

Category: Cloud

Skill Level: Intermediate

Duration: 11.00H

Modality: WBT

 

Audience


This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Regression techniques in a business setting.

 

Prerequisites


To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.

 

Overview


This course introduces you to one of the main types of modelling families of supervised Machine Learning: Regression. You will learn how to train regression models to predict continuous outcomes and how to use error metrics to compare across different models. This course also walks you through best practices, including train and test splits, and regularization techniques.

 

IBM Clients and Sellers – Consider this course as part of an individual or enterprise subscription service:

  • IBM Data/AI Individual Subscription (SUBR003G)
  • IBM Digital Learning Subscription — IBM Data/AI Enterprise Subscription (SUBR004G)
  • IBM Learning Individual Subscription with Red Hat Learning Services (SUBR013G)

 

Topic


1. Introduction to Supervised Machine Learning and Linear Regression

2. Data Splits and Cross Validation

3. Regression with Regularization Techniques: Ridge, LASSO, and Elastic Net

 

Objectives


By the end of this course you should be able to:
– Differentiate uses and applications of classification and regression in the context of supervised machine learning.

– Describe and use linear regression models.

– Use a variety of error metrics to compare and select a linear regression model that best suits your data.

– Articulate why regularization may help prevent overfitting.

– Use regularization regressions: Ridge, LASSO, and Elastic net.

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07 Sep 2021

Supervised Learning: Classification – W7103G WBT

Short Summary


This course introduces you to one of the main types of modeling families of supervised Machine Learning: Classification

 

Details


Course Code: W7103G

Brand: IBM Analytics

Category: Analytics

Skill Level: Intermediate

Duration: 11.00H

Modality: WBT

 

Audience


This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Classification techniques in a business setting.

 

Prerequisites


To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.

 

Overview


This course introduces you to one of the main types of modeling families of supervised Machine Learning: Classification. You will learn how to train predictive models to classify categorical outcomes and how to use error metrics to compare across different models. The hands-on section of this course focuses on using best practices for classification, including train and test splits, and handling data sets with unbalanced classes.

 

IBM Clients and Sellers – Consider this course as part of an individual or enterprise subscription service:

  • IBM Data/AI Individual Subscription (SUBR003G)
  • IBM Digital Learning Subscription — IBM Data/AI Enterprise Subscription (SUBR004G)
  • IBM Learning Individual Subscription with Red Hat Learning Services (SUBR013G)

 

Topic


1. Logistic Regression

2. K Nearest Neighbors

3. Support Vector Machines

4. Decision Trees

5. Ensemble Models

6. Modeling Unbalanced Classes

 

Objectives


By the end of this course you should be able to:
– Differentiate uses and applications of classification and classification ensembles.

– Describe and use logistic regression models.

– Describe and use decision tree and tree-ensemble models.

– Describe and use other ensemble methods for classification.

– Use a variety of error metrics to compare and select the classification model that best suits your data.

– Use oversampling and undersampling as techniques to handle unbalanced classes in a data set.

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07 Sep 2021

Unsupervised Learning – W7104G WBT

Short Summary


This course introduces you to one of the main types of Machine Learning: Unsupervised Learning

 

Details


Course Code: W7104G

Brand: IBM Analytics

Category: Analytics

Skill Level: Intermediate

Duration: 8.00H

Modality: WBT

 

Audience


This course targets aspiring data scientists interested in acquiring hands-on experience with Unsupervised Machine Learning techniques in a business setting.

 

Prerequisites


To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.

 

Overview


This course introduces you to one of the main types of Machine Learning: Unsupervised Learning. You will learn how to find insights from data sets that do not have a target or labeled variable. You will learn several clustering and dimension reduction algorithms for unsupervised learning as well as how to select the algorithm that best suits your data. The hands-on section of this course focuses on using best practices for unsupervised learning.

 

IBM Clients and Sellers – Consider this course as part of an individual or enterprise subscription service:

  • IBM Data/AI Individual Subscription (SUBR003G)
  • IBM Digital Learning Subscription — IBM Data/AI Enterprise Subscription (SUBR004G)
  • IBM Learning Individual Subscription with Red Hat Learning Services (SUBR013G)

 

Topic


1. Introduction to Unsupervised Learning and K Means

2. Selecting a clustering algorithm

3. Dimensionality Reduction

 

Objectives


By the end of this course you should be able to:
– Explain the kinds of problems suitable for Unsupervised Learning approaches.

– Explain the curse of dimensionality, and how it makes clustering difficult with many features.

– Describe and use common clustering and dimensionality-reduction algorithms.

– Try clustering points where appropriate, compare the performance of per-cluster models.

– Understand metrics relevant for characterizing clusters.

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07 Sep 2021

Deep Learning and Reinforcement Learning – W7105G WBT

Short Summary


This course introduces you to two of the most sought-after disciplines in Machine Learning: Deep Learning and Reinforcement Learning.

 

Details


Course Code: W7105G

Brand: IBM Analytics

Category: Analytics

Skill Level: Intermediate

Duration: 14.00H

Modality: WBT

 

 

Audience


This course targets aspiring data scientists interested in acquiring hands-on experience with Deep Learning and Reinforcement Learning.

 

Prerequisites


To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Unsupervised Learning, Supervised Learning, Calculus, Linear Algebra, Probability, and Statistics.

 

Overview


This course introduces you to two of the most sought-after disciplines in Machine Learning: Deep Learning and Reinforcement Learning. Deep Learning is a subset of Machine Learning that has applications in both Supervised and Unsupervised Learning, and is frequently used to power most of the AI applications that we use on a daily basis. First you will learn about the theory behind Neural Networks, which are the basis of Deep Learning, as well as several modern architectures of Deep Learning. Once you have developed a few  Deep Learning models, the course will focus on Reinforcement Learning, a type of Machine Learning that has caught up more attention recently. Although currently Reinforcement Learning has only a few practical applications, it is a promising area of research in AI that might become relevant in the near future.  After this course, if you have followed the courses of the IBM Specialization in order, you will have considerable practice and a solid understanding in the main types of Machine Learning which are: Supervised Learning, Unsupervised Learning, Deep Learning, and Reinforcement Learning.

 

IBM Clients and Sellers – Consider this course as part of an individual or enterprise subscription service:

  • IBM Data/AI Individual Subscription (SUBR003G)
  • IBM Digital Learning Subscription — IBM Data/AI Enterprise Subscription (SUBR004G)
  • IBM Learning Individual Subscription with Red Hat Learning Services (SUBR013G)

 

Topic


1. Introduction to Neural Networks

2. Neural Network Optimizers and Keras

3. Convolutional Neural Networks

4. Recurrent Neural Networks and Long-Short Term Memory Networks

5. Deep Learning with Autoencoders

6. Deep Learning Applications and Reinforcement Learning

 

Objectives


By the end of this course you should be able to:
– Explain the kinds of problems suitable for Unsupervised Learning approaches.

– Explain the curse of dimensionality, and how it makes clustering difficult with many features.

– Describe and use common clustering and dimensionality-reduction algorithms.

– Try clustering points where appropriate, compare the performance of per-cluster models.

– Understand metrics relevant for characterizing clusters

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07 Sep 2021

Specialized Models: Time Series and Survival Analysis – W7106G WBT

Short Summary


This course introduces you to additional topics in Machine Learning that complement essential tasks, including forecasting and analyzing censored data.

 

Details


Course Code: W7106G

Brand: IBM Analytics

Category: Analytics

Skill Level: Intermediate

Duration: 11.00H

Modality: WBT

 

Audience


This course targets aspiring data scientists interested in acquiring hands-on experience with Time Series Analysis and Survival Analysis.

 

Prerequisites


To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Supervised Machine Learning, Unsupervised Machine Learning, Probability, and Statistics.

 

Overview


This course introduces you to additional topics in Machine Learning that complement essential tasks, including forecasting and analyzing censored data. You will learn how to find analyze data with a time component and censored data that needs outcome inference. You will learn a few techniques for Time Series Analysis and Survival Analysis. The hands-on section of this course focuses on using best practices and verifying assumptions derived from Statistical Learning.

 

IBM Clients and Sellers – Consider this course as part of an individual or enterprise subscription service:

  • IBM Data/AI Individual Subscription (SUBR003G)
  • IBM Digital Learning Subscription — IBM Data/AI Enterprise Subscription (SUBR004G)
  • IBM Learning Individual Subscription with Red Hat Learning Services (SUBR013G)

 

Topic


1. Introduction to Time Series Analysis

2. Stationarity and Time Series Smoothing

3. ARMA and ARIMA Models

4. Deep Learning and Survival Analysis Forecasts

 

Objectives


By the end of this course you should be able to:
– Identify common modeling challenges with time series data.

– Explain how to decompose Time Series data: trend, seasonality, and residuals.

– Explain how autoregressive, moving average, and ARIMA models work.

– Understand how to select and implement various Time Series models.

– Describe hazard and survival modeling approaches.

– Identify types of problems suitable for survival analysis.

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