Short Summary

You will learn how Watson OpenScale on the IBM Cloud lets business analysts, data scientists, and developers build monitors for artificial intelligence (AI) models to manage risks.

Details

Course Code: W7069G

Brand: DS&BA – Watson Analytics

Category: Analytics

Skill Level: Intermediate

Duration: 6.50H

Modality: WBT

Audience

Analysts, Developers, Data Scientists and others who need to monitor machine learning jobs

Prerequisites

• Basic knowledge of cloud platforms, for example IBM Cloud
• Basic understanding of machine learning models, and how they are used

Overview

You will learn how Watson OpenScale lets business analysts, data scientists, and developers build monitors for artificial intelligence (AI) models to manage risks. You will understand how to use Watson OpenScale to build monitors for quality, fairness, and drift, and how monitors impact business KPIs. You will also learn how monitoring for unwanted biases and viewing explanations of predictions helps provide business stakeholders confidence in the AI being launched into production. Note: This course contains the same topics as 6X240G IBM Watson OpenScale on IBM Cloud Pak for Data WBT.

Topic

Introduction to IBM Watson OpenScale
• Describe the problem that Watson OpenScale solves
• Describe models, monitors, workflow
• Describe AIF and AIE 360 toolkits
• Describe workflow

Watson OpenScale architecture
• Describe Watson OpenScale architecture on IBM Cloud and on IBM Cloud Pak for Data
• Describe how Watson OpenScale works with other cloud services

Get started with Watson OpenScale
• Provision from catalog
• Start working with Watson OpenScale

Overview of Watson OpenScale monitors
• Identify the different Watson OpenScale monitors
• Define how the different monitors are used

Explore a use case
• Prepare the model for monitoring

Build and configure the fairness monitor
• Features to monitor
• Values that represent a favorable outcome of the model
• Reference and monitored groups
• Fairness thresholds
• Sample size
• Insights and explainability

Configure the quality monitor
• Quality alert threshold
• Sample size
• Insights and explainability

Detect drift and configure the drift monitor
• Alert threshold
• Sample size
• Insights and explainability

Configure application monitors
• Configure application monitors
• Configure KPI metrics in Watson OpenScale
• Configure event details
• Access and visualize custom metrics

Objectives

• Introduction to IBM Watson OpenScale
• Watson OpenScale architecture
• Get started with Watson OpenScale
• Overview of Watson OpenScale monitors
• Explore a use case
• Build and configure the fairness monitor
• Configure the quality monitor
• Detect drift and configure the drift monitor
• Configure application monitors

Short Summary

This course teaches how to prepare, develop, and deploy an IBM Watson Assistant. Applies to Watson Assistant on Cloud and Watson Assistant on Cloud Pak for Data.

Details

Course Code: W7070G

Brand: DS&BA – Watson Analytics

Category: Analytics

Skill Level: Intermediate

Duration: 4.00H

Modality: WBT

Audience

The audience for this training includes:
• Business decision makers looking for details on applications to increase employee productivity, optimize business processes, and improve the customer experience
• Subject matter experts who collect user input and craft dialog for the solution
• Developers who integrate front and back-end systems with corporate software
• Designers who create user interfaces for applications
• Performance managers who set goals and track success

Prerequisites

None

Overview

This course covers the methods of preparing for, training, and implementing an IBM Watson Assistant chatbot. It provides instruction on every step of the Assistant development process, including common use case review, user scenario development, vocabulary comprehension, data collection, tooling use, and deployment.  Applies to Watson Assistant on Cloud and Watson Assistant on Cloud Pak for Data.

Topic

Unit 1: Watson Assistant Essentials
• Understand assistant use cases and solution patterns
• Understand necessary data collection processes
• Understand IBM Watson Assistant vocabulary
• Create a dialog skill
• Define Intents and entities

Unit 2: Building Dialog Nodes
• Explain how to write dialog content
• Describe default node features
• Demonstrate how to edit a node
• Edit context variables
• Describe how to direct dialog flow
• Demonstrate next action options

Unit 3: Dialog Options
• Define the options panel
• Describe webhooks
• Describe disambiguation
• Describe autocorrect
• Describe irrelevance detection

Unit 4: Slots and Digressions
• Define the slots functionality
• Identify use case for slots
• Demonstrate slots
• Define digressions funcationality
• Identify uses for digressions
• Demonstrate digressions

Unit 5: Deploy and Manage
• Describe how the IBM Watson Assistant API calls work
• Describe five best practices of UI design for Assistants
• Explain how to deploy and manage an IBM Watson Assistant

Objectives

Watson Assistant Essentials
Building Dialog Nodes
Dialog Options
Slots and Digressions
Deploy and Manage

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

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

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.

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.

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

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.

Short Summary

This course introduces learners to Conversational AI and its associated technologies, historical development, contemporary forms and applications, all with a particular emphasis on business.

Details

Course Code: W7107G

Brand: IBM Analytics

Category: Analytics

Skill Level: Basic

Duration: 5.00H

Modality: WBT

Audience

This course is intended primarily for learners with a focus on business and/or technology, and specifically those seeking to better understand what Conversational AI is, where it came from, and how it can benefit their organizations. Although the course does not cover the technical aspects of building Conversational AI, Data Scientists, Machine Learning Engineers, and other technically-inclined learners may still find concepts and best practices introduced in the subject matter to be useful.

Prerequisites

No prerequisite knowledge is assumed, however a basic understanding of artificial intelligence and machine learning (i.e. what they are, what they do) will be beneficial.

Overview

In recent years there has been rapid development of AI systems aimed at communicating with humans. Advances in machine learning mean that chatbots and virtual agents are becoming ever-more capable of understanding, assisting, and entertaining us. Conversational AI is the field that encompasses these technologies, including their design, implementation, and applications.

This course introduces learners to Conversational AI and its associated technologies, examining its historical development, its contemporary forms and applications, and other key considerations in its use, all with a particular emphasis on business. The course will focus on intuitions, examples, and concepts surrounding Conversational AI, as opposed to its technical implementation—no coding is required.

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

Defining Conversational AI
• Identify elements of human conversation
• Examine the functions that artificial intelligence can play when interacting with humans
• Consider how what constitutes “artificial intelligence” may change over time

A Brief History of Conversational AI
• Contemplate the importance of Conversational AI to the history and ongoing development of artificial intelligence
• Characterize the role of chatbots in the history of Conversational AI, and how they differ from other modern variants of the technology
• Identifying key supporting technologies that enabled the development of Conversational AI

Conversational Solutions in Business
• Examine Conversational AI in direct customer-assistance use cases
• Examine Conversational AI as an agent-assist tool
• Identify additional sources of business value

Building a Conversational Agent
• Consider challenges and properties of human natural language relevant in building Conversational AI
• Define dialogue frameworks and the GUS architecture
• Analyze information retrieval approaches as they apply to building Conversational AI

Conversational Solution Design
• Define Conversational Solutions and IBM’s four-step approach to building and deploying them
• Consider risks and challenges at specific stages of the process of building and deploying Conversational Solutions
• Examine specific best practices for Conversational Solutions

Conversational AI Laws and Ethics
• Identify key regulations affecting the building and deployment of Conversational AI in various parts of the world
• Consider ways that Conversational AI can be used to misinform or harm users, either intentionally or unintentionally
• Examine specific cases where Conversational AI can be used to help and harm—what works and what does not

What’s Next for Conversational AI?
• Examine the role of generative AI and other contemporary machine learning approaches in recent Conversational AI models, and consider how further advancements in these fields will change Conversational AI going forward
• Witness one cutting-edge Conversational AI system in action: IBM Project Debater

Objectives

• Defining Conversational AI
• A Brief History of Conversational AI
• Conversational Solutions in Business
• Building a Conversational Agent
• Conversational Solution Design
• Conversational AI Laws and Ethics
• What’s Next for Conversational AI?

Short Summary

This course introduces key concepts and methods in Natural Language Processing (NLP), the subfield of data science and artificial intelligence that deals with computer interaction with human language.

Details

Course Code: W7108G

Brand: DS&BA – Cognos CC and CIS

Category: Analytics

Skill Level: Basic

Duration: 4.50H

Modality: WBT

Audience

The course is intended primarily for learners with a focus on business and/or technology, and specifically those seeking to better understand what NLP is, where it came from, and how it can benefit their organizations. Although the course does not cover the technical aspects of building NLP systems, Data Scientists, Machine Learning Engineers, and other technically-inclined learners may still find concepts and best practices introduced in the subject matter to be useful.

Prerequisites

No prerequisite knowledge is assumed, however a basic understanding of artificial intelligence and machine learning (i.e. what they are, what they do) will be beneficial.

Overview

This course introduces key concepts and methods in Natural Language Processing (NLP), the subfield of data science and artificial intelligence that deals with computer interaction with human language. The course covers 1) The definition of NLP, and its relationship to data science, artificial intelligence, and other subfields therein, 2) Historical thought and development in NLP as well as recent breakthroughs in NLP enabled by artificial intelligence, and 3) Select methods and use cases for NLP, focused mainly on business contexts. The course will also briefly introduce learners to award-winning IBM Watson NLP tools, which make the discussed technologies accessible to non-technical users.

Upon completion of the course, learners will feel comfortable describing what NLP is, how it has evolved from historical precedents to contemporary developments, and several specific methods and use cases for NLP technologies in organizational settings. Learners will leave this course well-prepared for more applied and technical courses on NLP.

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

Defining NLP
• Defining natural languages, and contrasting them from constructed and formal languages
• Examining the ways that computers handle natural language as numbers (data)
• Recognizing simple applications for computer processing of natural language data

NLP History
• Identifying early thinkers who speculated about computation, computers, and human language
• Analyzing successes and failures in early NLP experiments in machine translation and chatbots
• Examining key enabling technologies that supported the advancement of NLP over the past few decades

NLP Applications
• Relating early NLP experiments to modern applications for individuals and organizations
• Describing specific sources of value brought by NLP applications to businesses, such as in customer engagement and sentiment analysis
• Considering the future of common NLP applications

Working With Text
• Identifying computer processes (algorithms) useful for handling and transforming text
• Recognizing common challenges in working with textual data, such as issues related to data quality
• Analyzing components of NLP frameworks, and linking them to common NLP methods such as named-entity recognition (NER)

From Models to Systems
• Differentiating text-level NLP methods from NLP systems
• Relating system-level NLP to NLP applications
• Recognizing the intuitions behind two complex NLP systems: Information Extraction and Conversational AI

Objectives

• Defining NLP
• NLP History
• NLP Applications
• Working With Text
• From Models to Systems