Digital Transformation: Advanced Intelligent Automation/Digital Transf (original) (raw)

Note: This new course is coming soon and is currently available for pre-order.

The Digital Transformation: Advanced Intelligent Automation course covers digital transformation and fundamental RPA and AI topics, and further delves into intelligent automation environments that interrelate the usage of AI and RPA bots, including the control of RPA bots by autonomous decision-making logic within AI systems. Both the potential benefits and challenges of intelligent automation environments are explained.

Complete the Digital Transformation: Advanced Intelligent Automation course and, optionally, get accredited as a Certified Digital Transformation Intelligent Automation Specialist by passing the certification exam. You can purchase the course now and get the exam later, or you can get them together at a discount as part of the Certification Bundle.

Upon completing the course you will receive a digital certificate of completion, as well as a digital training badge from Acclaim/Credly. Because this course encompasses multiple certifications, upon passing the exam you will also receive official Digital Transformation Specialist, Digital Transformation Intelligent Automation Professional and Digital Transformation Intelligent Automation Specialist digital accreditation certificates and certification badges from Acclaim/Credly, along with an account that can be used to verify your certification status.

If you already completed Modules 1, 2, 11 and 17 in this certification track, you can purchase a partial course (or a partial bundle) with only the modules specific to the Digital Transformation Intelligent Automation Specialist track here.

The Digital Transformation: Advanced Intelligent Automation course is comprised of the following 6 course modules, each of which has an estimated completion time of 10 hours:

• Module 1: Fundamental Digital Transformation
• Module 2: Digital Transformation in Practice
• Module 11: Fundamental AI
• Module 14: Advanced AI
• Module 17: Fundamental RPA
• Module 18: Advanced RPA & Intelligent Automation

Choose the Certification Bundle to receive the entire course together with the online-proctored certification exam and a set of practice exam questions, all at a bundle discount.

Exam Details

Upon purchasing this course, you will automatically receive access via the Online Interactive eLearning platform. To provide you with the greatest flexibility, you will also have the option to access the course materials via two additional eLearning formats, at no extra cost. All three eLearning formats are briefly described below. A more detailed comparison can be found here.

1. For everyday learning: An online interactive eLearning platform with individual lessons, as well as interactive and automatically graded exercises and practice questions.
2. For learning on-the-go: A study kit platform with access to full course documents that support online/offline synching, annotations, comments, custom bookmarks and cross-document searches.
3. For your reference: A set of printable watermarked PDF documents that you can keep (for all course workbooks and posters). All three forms of access are subject to Arcitura’s *Lifetime Access Guarantee. Upon purchase, access to the online interactive eLearning platform (1) is provided within one business day. Access to the study kits (2) and the PDF documents (3) is provided upon request.

The course is comprised of a set of modules. Each module has a set of lessons and is further supplemented with exercises to help reinforce your understanding of key topics. Shown below are the digital contents and the topic outline for each course module:

Module 1: Fundamental Digital Transformation

This course module provides an easy-to-understand introduction to Digital Transformation and how it relates to business, technology, data and people. Coverage includes the benefits, risks and challenges of Digital Transformation, as well as its business and technology drivers.

Course Module Contents

• Workbook Lessons (100+ pages)
• Video Lessons (for all topics)
• Interactive Exercises

• Mind Map Poster
• Symbol Legend Poster
• Practice Exam Questions
• PDFs of Workbook and Posters (printable)

Topics Covered

• Understanding Digital Transformation
• Benefits of Digital Transformation
• Challenges of Digital Transformation
• Digital Transformation Business and Technology Drivers
• Understanding Customer-Centricity
• Product-Centric vs. Customer-Centric Relationships
• Relationship-Value Actions and Warmth
• Omni-Channel Customer Interactions
• Customer Journeys and Customer Data Intelligence

• Data Intelligence Basics
• Data Origins and Data Sources
• Data Collection Methods and Data Utilization Types
• Intelligent Decision-Making
• Computer-Assisted Manual Decision-Making and Conditional Automated Decision-Making
• Intelligent Manual Decision-Making vs. Intelligent Automated Decision-Making
• Direct-Driven Automated Decision-Making and Periodic Automated Decision-Making
• Realtime Automated Decision-Making

Module 2: Digital Transformation in Practice

This course module delves into Digital Transformation automation environments by exploring the key contemporary technologies used to build Digital Transformation Automation solutions, including AI, RPA, IoT, machine learning, blockchain, cloud computing and big data.

Course Module Contents

• Workbook Lessons (100+ pages)
• Video Lessons (for all topics)
• Interactive Exercises

• Mind Map Poster
• Practice Exam Questions
• PDFs of Workbook and Poster (printable)

Topics Covered

• Distributed Solution Design Basics
• Data Ingress Basics, including File Pull, File Push, API Pull, API Push and Data Streaming
• An Introduction to Digital Transformation Automation Technologies
• Cloud Computing Basics and Cloud Computing as part of Digital Transformation Solutions
• Common Cloud Computing Risks and Challenges
• Blockchain Basics and Blockchain as part of Digital Transformation Solutions
• Common Blockchain Risks and Challenges
• Internet of Things (IoT) Basics and IoT as part of Digital Transformation Solutions
• Common IoT Risks and Challenges
• Robotic Process Automation (RPA) and RPA as part of Digital Transformation Solutions
• Common RPA Risks and Challenges

• An Introduction to Digital Transformation Data Science Technologies
• Big Data and Data Analytics and Big Data as part of Digital Transformation Solutions
• Common Big Data Risks and Challenges
• Machine Learning Basics and Machine Learning as part of Digital Transformation Solutions
• Common Machine Learning Risks and Challenges
• Artificial Intelligence (AI) Basics and AI as part of Digital Transformation Solutions
• Common AI Risks and Challenges
• Inside a Customer-Centric Digital Transformation Solution (a comprehensive, step-by-step exploration)
• Mapping Individual Digital Transformation Technologies to Solution Processing
• Tracking how Data Intelligence is Collected and Used in a Digital Transformation Solution

Module 11: Fundamental AI

This course module provides essential coverage of artificial intelligence and neural networks in easy-to-understand, plain English. The module provides concrete coverage of the primary parts of AI, including learning approaches, functional areas that AI systems are used for and a thorough introduction to neural networks, how they exist, how they work and how they can be used to process information.

Course Module Contents

• Workbook Lessons (100+ pages)
• Video Lessons (for all topics)
• Interactive Exercises
• Mind Map Poster
• Symbol Legend Poster
• Neural Networks and Neuron Types Mapping Poster

• Problem Types and Neural Networks Mapping Poster
• Neural Networks and Practices Mapping Poster
• Problem Types and Practices Mapping Poster
• Practice Exam Questions
• PDFs of Workbook and Posters (printable)

Topics Covered

• AI Business and Technology Drivers
• AI Benefits and Challenges
• Business Problem Categories Addressed by AI
• AI Types (Narrow, General, Symbolic, Non-Symbolic, etc.)
• Common AI Learning Approaches and Algorithms
• Supervised Learning, Unsupervised Learning, Continuous Learning
• Heuristic Learning, Semi-Supervised Learning, Reinforcement Learning
• Common AI Functional Designs
• Computer Vision, Pattern Recognition
• Robotics, Natural Language Processing (NLP)
• Speech Recognition, Natural Language Understanding (NLU)
• Frictionless Integration, Fault Tolerance Model Integration
• Neural Networks, Neurons, Layers, Links, Weights
• Understanding AI Models and Training Models and Neural Networks

• Understanding how Models and Neural Networks Exist
• Loss, Hyperparameters, Learning Rate, Bias, Epoch
• Activation Functions (Sigmoid, Tanh, ReLU, Leaky ReLU, Softmax, Softplus)
• Neuron Cell Types (Input, Backfed, Noisy, Hidden, Probabilistic, Spiking, Recurrent, Memory, Kernel, nvolution, Pool, Output, Match Input, etc.)
• Fundamental and Specialized Neural Network Architectures
• Perceptron, Feedforward, Deep Feedforward, AutoEncoder, Recurrent, Long/Short Term Memory
• Deep Convolutional Network, Extreme Learning Machine, Deep Residual Network
• Support Vector Machine, Kohonen Network, Hopfield Network
• Generative Adversarial Network, Liquid State Machine
• How to Build an AI System (Step-by-Step)

Module 14: Advanced AI

This course module covers a series of practices for preparing and working with data for training and running contemporary AI systems and neural networks. It further provides techniques for designing and optimizing neural networks, including approaches for measuring and tuning neural network model performance. The practices and techniques are documented as design patterns that can be applied individually or in different combinations to address a range of common AI system problems and requirements.

Course Module Contents

• Workbook Lessons (100+ pages)
• Video Lessons (for all topics)
• Interactive Exercises
• Mind Map Poster
• Neural Networks and Design Patterns Mapping Poster

• Problem Types and Design Patterns Mapping Poster
• Practices and Design Patterns Mapping Poster
• Practice Exam Questions
• PDFs of Workbook and Posters (printable)

Topics Covered

• Data Wrangling Patterns for Preparing Data for Neural Network Input
• Feature Encoding for Converting Categorical Features
• Feature Imputation for Inferring Feature Values
• Feature Scaling for Training Datasets with Broad Features
• Text Representation for Converting Data while Preserving Semantic and Syntactic Properties
• Dimensionality Reduction to Reduce Feature Space for Neural Network Input
• Supervised Learning Patterns for Training Neural Network Models
• Supervised Network Configuration for Establishing the Number of Neurons in Network Layers
• Image Identification for using a Convolutional Neural Network
• Sequence Identification for using a Long Short Term Memory Neural Network
• Unsupervised Learning Patterns for Training Neural Network Models

• Pattern Identification for Visually Identifying Patterns via a Self Organizing Map
• Content Filtering for Generating Recommendations
• Model Evaluation Patterns for Measuring Neural Network Performance
• Training Performance Evaluation for Assessing Neural Network Performance
• Prediction Performance Evaluation for Predicting Neural Network Performance in Production
• Baseline Modeling for Assessing and Comparing Complex Neural Networks
• Model Optimization Patterns for Refining and Adapting Neural Networks
• Overfitting Avoidance for Tuning a Neural Network
• Frequent Model Retraining for Keeping a Neural Network in Synch with Current Data
• Transfer Learning for Accelerating Neural Network Training

Module 17: Fundamental RPA

This course module establishes the components and models that comprise contemporary robotic process automation (RPA) environments. Different types of RPA bots are explained, along with different RPA architectures and bot utilization models. This course further provides detailed scenarios that demonstrate different deployments of RPA bots and other components in relation to different business automation requirements.

Course Module Contents

• Workbook Lessons (100+ pages)
• Interactive Exercises
• Mind Map Poster

• Symbol Legend Poster
• Practice Exam Questions
• PDFs of Workbook and Posters (printable)

Topics Covered

• Understanding Robotic Process Automation
• RPA Business Drivers and Technology Drivers
• RPA Goals and Benefits
• RPA Risks and Challenges
• Front-end and Back-end Integration
• RPA Components and Bot Runners
• RPA Architecture Layers and Models
• RPA Life Cycle

• Front-End Integration with RPA Bots
• Back-End Integration with RPA Controllers and APIs
• Automated Data Entry
• Automated Routing
• Automated Web Searching
• Automated Data Search and Fetch
• Automated Digitization
• Automated User Acceptance Testing Usage Scenario

Module 18: Advanced RPA & Intelligent Automation

This course module explores the relationship between artificial intelligence (AI) and RPA and describes how these technologies can be combined to establish intelligence automation (IA) environments. The course covers different types of autonomous decision-making and further extends the usage scenarios from Module 1 by incorporating Artificial Intelligence (AI) systems as part of intelligent automation solutions.

Course Module Contents

• Workbook Lessons (100+ pages)
• Interactive Exercises
• Mind Map Poster

• Practice Exam Questions
• PDFs of Workbook and Poster (printable)

Topics Covered

• Introduction to Intelligent Automation and Hyperautomation
• Intelligent Automation Business Drivers and Technology Drivers
• Intelligent Automation Goals and Benefits
• Intelligent Automation Risks and Challenges
• Components of Intelligent Automation Solutions
• Intelligent Automation and Business Process Management
• Introduction to Artificial Intelligence
• Understanding Autonomous Decision-Making

• Direct-Driven Automated Decision-Making
• Periodic Automated Decision-Making
• Realtime Automated Decision-Making
• Intelligent Automated Data Entry
• Intelligent Automated Routing
• Intelligent Automated Web Searching
• Intelligent Automated Data Search and Fetch
• Intelligent Automated Digitization
• Intelligent Automated User Acceptance Testing Usage Scenario

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In addition to standard video lessons, courses include full-color workbooks and reference posters for all lessons.

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Lifetime Access Guarantee

Access to the interactive eLearning platform (1) and the study kit platform (3) is dependent on third-party platform providers. As a result, Arcitura cannot make any guarantees or warranties regarding the behavior or availability of the platform, nor can Arcitura guarantee a specific duration for which these platforms will remain available. When your course is set up, your access will have no expiration date and will be provided for a minimum of one year and indefinitely thereafter, for as long as the platforms remain available under the conditions by which they were contracted by Arcitura. The PDF documents (3) that will be made available for you to download are yours to keep forever.