Artificial Intelligence Ai Masterclass E-Learning Ausbildung

Die Feind der künstlichen Intelligenz. Niederlassungen implementieren KI, weil sie ihre persönlichen Antworten replizieren können, sterben noch schneller und müssen. Diese Reise vermittelt, dass die die, die, sind, um von einem KI-Lehrling zu einem KI-Architekten zu gehören.

ARTIFICIAL INTELLIGENCE - AI APPRENTICE TO AI ARCHITECT

Dieser Lernpfad mit mehr als 57 Stunden Online-Inhalten ist in die folgenden vier Titel unterteilt:

• Track 1: AI Apprentice
• Track 2: AI Developer
• Track 3: AI Practitioner
• Track 4: AI Architect

Kursinhalt

Track 1: AI Apprentice

In this track of the AI Apprentice to AI Architect journey, the focus is on AI development and theory, HCI principles and methods, AI development with Python, computer vision for AI and cognitive modelling.

E-Learning courses

Artificial Intelligence: Basic AI Theory

Course: 1 Hour, 5 Minutes

• Course Overview
• What Is Artificial Intelligence?
• Types of Intelligent Systems
• Importance of Data in AI Development
• History of AI
• Recent Breakthroughs in AI
• Popular Environments for AI Development
• Big Data as a Catalyst for AI Development
• Ethical Use of Big Data
• Reliability of Intelligent Systems
• Importance of Testing AI
• What Can Go Wrong with AI?
• Efficient Use of AI
• Course Summary

Artificial Intelligence: Types of Artificial Intelligence

Course: 48 Minutes

• Course Overview
• Overview of Existing Types of AI
• Reactive and Limited Memory AI
• Artificial Narrow Intelligence
• Agricultural, Medical, and Philanthropic AI Uses
• Aviation, Robotics, and Military AI Uses
• Finance, Retail, and Government AI Uses
• Current AI Research and Further Possibilities
• Theory of Mind AI Research
• Self-Aware AI
• AI in Perspective
• Artificial General Intelligence
• Artificial Super Intelligence
• Course Summary

Artificial Intelligence: Human-computer Interaction Overview

Course: 56 Minutes

• Course Overview
• HCI Introduction
• Main Components of HCI
• Goals of HCI Studies
• Areas Involved in HCI
• HCI History
• HCI Research Trends
• HCI Tools
• User and Context of an AI Application
• Importance of a User-oriented Approach in AI
• Explainability of AI
• Defining Tasks for an AI Application
• Interface Design
• Course Summary

Artificial Intelligence: Human-computer Interaction Methodologies

Course: 57 Minutes

• Course Overview
• HCI Design Methodologies
• Anthropomorphic Approach to HCI
• Cognitive Approach to HCI
• Empirical Approach to HCI
• Predictive Modeling Approach to HCI
• Iterative Design Process
• Prototyping Principles
• Developing Personas and Scenarios
• Prototyping in AI
• Usability Testing
• Importance of User Feedback to HCI
• Continuous Development and Integration
• Course Summary

Python AI Development: Introduction

Course: 47 Minutes

• Course Overview
• Overview of Python
• Interpreted and Compiled Languages
• AI Tool Landscape
• Python and AI
• Why is Python so Popular?
• Python vs. R in AI
• Python vs. C++ in AI
• Python vs. Java in AI
• Python vs. New Languages in AI
• Industry Support of Python
• Major Python Libraries for AI
• Python Environment Requirement and Setup
• Different Ways to Use Python
• Future Directions of Python for AI
• Course Summarys

Python AI Development: Practice

Course: 1 Hour, 21 Minutes

• Course Overview
• Setting Up the Basic Environment for AI
• Understanding Anaconda Distribution
• Jupyter Notebooks
• Google Colab
• Data Exploration
• Data Pre-processing
• Basic ML Models
• Basic DL Models
• Classifying Hand Written Digits
• Predicting House Prices
• Pilot Distraction Prediction
• Distracted Driver Detection
• Course Summary

Computer Vision: Introduction

Course: 40 Minutes

• Course Overview
• What Is Computer Vision?
• Computer Vision vs. Image Processing
• Traditional Solutions to Vision Problems
• AI-driven CV Solution
• Landscape of CV Tools and Models
• Computer Vision Problems
• Importance of Computer Vision in the Industry
• Computer Vision in Healthcare
• Computer Vision in the Banking Sector
• Computer Vision in Retail
• Computer Vision in Cybersecurity
• Computer Vision in Agriculture
• Computer Vision in Manufacturing
• Course Summary

Computer Vision: AI & Computer Vision

Course: 45 Minutes

• Course Overview
• Computer Vision in Consumer Electronics
• Computer Vision in Aerospace
• Computer Vision in the Automotive Industry
• Computer Vision in Robotics
• Computer Vision in Space Technology
• Basic Overview of CNN
• Different Types of CNN
• Object Detection
• Image Classification
• Image Segmentation
• Image Generation
• Predicting Deforestation in Amazon Rainforest
• Lung Cancer Detection Using MRI Images
• Course Summary

Cognitive Models: Overview of Cognitive Models

Course: 37 Minutes

• Course Overview
• What is Cognitive Modeling
• Features of Cognitive Models
• Different Types of Cognitive Models
• Different Types of Cognitive Disciplines
• Cognitive Models in Healthcare
• Cognitive Models in Neuroscience
• Cognitive Models in Manufacturing
• Cognitive Computing and Decision-making
• Cognitive Computing in Psychology
• Cognitive Models and AI
• History of Cognitivism
• Cognitivism and Computer Science

Cognitive Models: Approaches to Cognitive Learning

Course: 44 Minutes

• Course Overview
• Types of Cognitive Learning
• Symbolic Learning - Learning by Instruction
• Sub-symbolic Learning - Learning by Example
• Hybrid Learning
• Effect of AI in Cognitive Model
• Different Tools for Cognitive Modeling
• Decision-making for Business
• Predict Learner Confusion Using EEG Brainwave Data
• Predicting Personality Types Based on Conversation
• Analyzing Big Five Personality Traits
• Building Simple Chatbot Using RASA
• Course Summary

Online Mentor
You can reach your Mentor by entering chats or submitting an email.

Final Exam assessment
Estimated duration: 90 minutes

Practice Labs: Software Tester (estimated duration: 8 hours)
Perform AI Apprentice tasks such as exploratory data analysis, machine learning regression and classification, and multi-layered perception classification. Then, test your skills by answering assessment questions after performing deep neural network and convolutional neural network classification, as well as performing fully convolutional neural network boundary detection and NLP neural network text analysis. This lab provides access to tools typically used by AI Apprentices, including: o Jupyter Notebook, Python, Anaconda, Scikit-learn, Keras

Track 2: AI Developer

In this track of the AI Apprentice to AI Architect journey, the focus is on Microsoft Cognitive Toolkit (CNTK), working with Keras, Apache Spark, Amazon Machine Learning, robotics, and Google BERT.

E-Learning courses

AI Framework Overview: AI Developer Role

Course: 39 Minutes

• Course Overview
• AI Developer Role
• AI Developer Skills and Tools
• Responsibilities of AI Developer
• AI Developer vs. Software Engineer
• AI Developer vs. Data/AI Scientist
• AI Developer vs. ML Engineer
• AI Developer vs. AI Engineer
• AI Developer Work Mindset
• Importance of Research for AI Developers
• Career Progression for AI Developers
• AI Developer Role in Companies and Industries
• Future Opportunities for AI Developers
• Course Summary

AI Framework Overview: Development Frameworks

Course: 45 Minutes

• Course Overview
• What are AI Frameworks
• Types of AI Framework and Their Importance
• The Need for Different AI Frameworks
• Distributed Deep Learning Using Microsoft CNTK
• Keras as a High Level Neural Network API
• Distributed AI for Big Data Using Spark
• Amazon ML: End-to-end Cloud-based AI Library
• Microsoft CNTK vs. Others
• Keras vs. Others
• Spark vs. Others
• Amazon ML vs. Others
• Model Building Using Microsoft CNTK
• Model Building Using Amazon SageMaker
• Course Summary

Working With Microsoft Cognitive Toolkit (CNTK)

Course: 59 Minutes

• Course Overview
• Microsoft Cognitive Toolkit (CNTK)
• Microsoft’s Cognitive Computing
• AI at Microsoft: Deep Learning
• Benefits of Using CNTK
• Python API for CNTK
• CNTK: Support for Other Languages
• CNTK vs. Other Frameworks
• Machine Learning With CNTK
• Deep Learning With CNTK
• CNTK and Cloud (AWS, GCP, and Azure)
• CNTK Documentation and Tutorials
• CNTK Community
• CNTK Installation (Windows and Linux)
• Predicting Diabetes Using Retina Scans
• Course Summary

Deep Learning Packages: Keras - a Neural Network Framework

Course: 54 Minutes

• Course Overview
• The Keras Framework
• Cognitive Computing and Keras
• Deep Learning and Keras
• TensorFlow and Keras
• Benefits of Using Keras
• Keras: Support for Other Languages
• Keras and Other Frameworks
• Machine Learning With Keras
• Deep Learning With Keras
• Keras and the Cloud (AWS, GCP, and Azure)
• Keras Documentation, Community, and Tutorials
• Identifying Presidential Debate Twitter Sentiments
• Recognizing Plant Diseases Using Deep Learning
• Course Summary

Introducing Apache Spark for AI Development

Course: 43 Minutes

• Course Overview
• Apache Spark: Features and Uses
• Distributed Computing: Explained
• Disk and Memory-based systems: Comparison
• Using Spark in AI Development: Benefits
• Spark in AI Development: Components
• Spark vs. Other Big Data AI Platforms
• Machine Learning With Spark
• Deep Learning With Spark
• Natural Language Processing With Spark
• Computer Vision With Spark
• Spark Documentation, Community, and Tutorials
• Spark Installation: Windows and Linux
• Movie Recommendations Using Spark ML
• Course Summary

Implementing AI With Amazon ML

Course: 45 Minutes

• Course Overview
• The Amazon ML Framework
• Big Data in AI With Amazon ML
• Benefits of Using Amazon ML
• Amazon Web Services vs. Amazon ML
• Amazon ML vs. Other Frameworks
• Machine Learning With Amazon ML
• Deep Learning With Amazon ML
• Build, Train, and Deploy Models in Amazon SageMaker
• Image and Video Analysis: Amazon Rekognition
• Text-to-speech: Amazon Polly
• Text Analytics: Amazon Comprehend
• Amazon ML Documentation, Community, and Tutorials
• Prediction: IMDB Movie Review Sentiment
• Course Summary

Implementing AI Using Cognitive Modeling

Course: 52 Minutes

• Course Overview
• What Is Cognitive Modeling?
• Applications of Cognitive Modeling
• Cognitive Models in Various Industries
• Cognitive Modeling and NLP
• Cognitive Modeling and Image Recognition
• Cognitive Modeling and Neural Networks
• Open Source Cognitive Modeling
• Cognitive Modeling: Real-life Use Cases
• What Are Expert Systems?
• What Are Cognitive Machines?
• Computational Agents: Reinforcement Learning
• What Is AI Reasoning?
• Differential Neural Computer
• Question Answering System
• YouTube Video Tag Prediction
• Predicting Heart Disease Using MRI
• Course Summary

Applying AI to Robotics

Course: 1 Hour, 5 Minutes

• Course Overview
• A History of Robotics
• AI in Robotics
• Collaborative Robots: Cobots
• AI and Robotics: Applications
• AI and Robotics in the Automotive Industry
• AI and Robotics in Healthcare
• AI and Robotics in Manufacturing
• AI and Robotics in Logistics
• AI and Robotics in the Military
• Open Source Robotics
• Reinforcement Learning in Robotics
• Cognitive and Deep Learning Models in Robotics
• Predict Floor Surfaces Using a Robot’s Sensor data
• Deep Reinforcement Learning for Robotics
• ROS and Jupyter Notebooks
• Course Summary

Working with Google BERT: Elements of BERT

Course: 1 Hour, 8 Minutes

• Course Overview
• Supervised vs. Unsupervised NLP
• The Purpose of Language Models
• Legacy Language Models
• Deep Learning-based Language Models
• Current State-of-art Language Models
• The Purpose of Language Representation
• Transfer Learning in NLP
• The Purpose of Transformer Models
• Google BERT and NLP
• BERT Architecture and Variants
• NLP Problems Solved by BERT
• Developing an Amazon Review Sentiment Predictor
• Creating a Disaster Tweet Classifier
• Course Summary

Online Mentor
You can reach your Mentor by entering chats or submitting an email.

Final Exam assessment
Estimated duration: 90 minutes

Practice Labs: QA Specialist (estimated duration: 7 hours)
Perform AI Developer tasks such as implementing prediction models and using the CNTL framework, as well as performing sentiment analysis and image classification. Then, test your skills by answering assessment questions after performing category classification using BERT and prediction analysis using pySpark. This lab provides access to tools typically used by AI Developer, including:
o Jupyter Notebook - Python - Anaconda - Scikit-learn - Keras

Track 3: AI Practitioner

In this track of the AI Apprentice to AI Architect journey, the focus is on advanced CNTK, Keras, Apache Spark, Amazon Machine Learning and building intelligent information systems.

E-Learning collections

The AI Practitioner: Role & Responsibilities

Course: 47 Minutes

• Course Overview
• AI Practitioner Role Overview
• Skillset & Tools for AI Practitioner
• AI Practitioner in the Industry
• AI Practitioner vs. AI Developer
• AI Practitioner vs. Data Scientist/AI Scientist
• AI Practitioner vs. ML Engineer
• AI Practitioner vs. AI Engineer
• AI Practitioner Mindset
• AI Practitioner & Research Work
• AI Practitioners in the Data Science Domain
• AI Practitioners in the AI Industry
• AI Practitioner Roles & Responsibilities
• Course Summary

The AI Practitioner: Optimizing AI Solutions

Course: 39 Minutes

• Course Overview
• AI Optimization Overview
• Types of AI Optimization
• Benefits of AI Optimization
• Gradient Descent Optimization in AI
• Stochastic Gradient Descent Optimization in AI
• Momentum Optimization in AI
• AdaGrad Optimization in AI
• RMSprop Optimization in AI
• Adam Optimization in AI
• AdaMax Optimization in AI
• Applying Gradient Descent Optimization
• Applying AdaGrad Optimization
• Course Summary

The AI Practitioner: Tuning AI Solutions

Course: 42 Minutes

• Course Overview
• Hyper Parameters in AI Development
• Hyper Parameter Tuning
• Hyper Parameters in Machine Learning Algorithms
• Hyper Parameters in Deep Learning Algorithms
• Describe Hyper Parameter Tuning Using Grid Search in AI
• Hyper Parameter Tuning Using Random Search
• Hyper Parameter Tuning Using Bayesian Method
• Gradient-based Hyper Parameter Tuning
• Evolutionary Hyper Parameter Tuning
• Hyper Parameter Tuning AI Libraries
• Applying Hyper Parameter Grid Search
• Applying Random Search for Hyper Parameter Tuning
• Course Summary

Advanced Functionality of Microsoft Cognitive Toolkit (CNTK)

Course: 47 Minutes

• Course Overview
• CNTK vs. Other Platforms
• Working With Data in CNTK
• CNTK Training Using Imperative APIs
• CNTK Training Using Declarative APIs
• Epochs and Batch Sizes in CNTK
• Model Serialization Using CNTK
• Model Visualization Using CNTK
• CNTK Model Training
• CNTK Model Evaluation
• Diabetes Prediction Using CNTK
• Credit Rating Prediction Using CNTK
• Housing Price Prediction Using CNTK
• Salary Prediction Using CNTK
• Course Summary

Working With the Keras Framework

Course: 50 Minutes

• Course Overview
• Keras vs. Other Platforms
• Keras vs. Microsoft CNTK
• Keras Sequential Model API
• Keras Functional API
• Core and Convolutional Layers in Keras
• Pooling and Recurrent Layers in Keras
• Embedding Layers in Keras
• Preprocessing in Keras
• Keras Model Training
• Keras Model Evaluation
• Sales Estimation Using Keras
• Insurance Premium Estimation Using Keras
• Cancer Prediction Using Keras
• Loan Prediction Using Keras
• Course Summary

Using Apache Spark for AI Development

Course: 38 Minutes

• Course Overview
• SPARK vs. Other Platforms
• Resilient Distributed Dataset Sources
• Resilient Distributed Dataset Features
• Resilient Distributed Dataset Creation
• Resilient Distributed Dataset Operations
• Spark DataFrame Sources
• Spark DataFrame Features
• Spark DataFrame Creation
• Spark ML Pipelines
• Spark ML Pipeline Concepts
• Creating a Pipeline with Spark ML
• Course Summary

Extending Amazon Machine Learning

Course: 1 Hour, 2 Minutes

• Course Overview
• Amazon ML vs. Other Platforms
• Amazon ML vs. Google Cloud Platform
• Amazon ML vs. Azure ML
• Data Sources in Amazon ML
• Feature Processing in Amazon ML
• Data Splitting in Amazon ML
• Amazon ML Model Types
• Batch Prediction in Amazon ML
• Real-time Prediction in Amazon ML
• Improving Model Accuracy in Amazon ML
• Introduction to Sagemaker
• Sagemaker Model Training and Validation
• Validating Sagemaker Model Results
• Course Summary

Using Intelligent Information Systems in AI

Course: 52 Minutes

• Course Overview
• Intelligent Information Systems (IIS)
• The Need for Intelligent Information Systems
• Intelligent Information Systems Applications
• AI and ML in IIS
• Challenges in IIS Development
• Components of Intelligent Information Systems
• Building Self-driving Cars
• The Markov Decision Process
• Setting Up a Markov Decision Process
• ValueIteration Call
• Setting Up a ValueIteration Call
• Interpreting the Results of a ValueIteration Call
• Self-driving Car Subsystems
• Course Summary

Online Mentor
You can reach your Mentor by entering chats or submitting an email.

Final Exam assessment
Estimated duration: 90 minutes

Practice Labs: QA Lead (estimated duration: 8 hours)
Description will follow shortly

Track 4: AI Architect

In this track of the AI Apprentice to AI Architect journey, the focus is on AI enterprise planning, reusable AI architecture patterns, current and future AI technologies and frameworks and XAI.

E-Learning collections

Elements of an Artificial Intelligence Architect

Course: 26 Minutes

• Course Overview
• The AI Architect Role
• The AI Architect's Work
• AI Architect vs. IT Architect vs. IT Developer
• AI Architects in the Enterprise
• AI Architect Membership
• Course Summary

AI Enterprise Planning

• Course: 1 Hour, 8 Minutes
• Course Overview
• What Is AI Enterprise Planning?
• Tactical vs. Strategic Planning Goals
• AI Maturity Model and Assessment
• Discovery Maps
• Business Value vs. Complexity Model
• AI Accelerators
• Complexity Reduction
• AI Enterprise Roadmap
• AI Application Examples by Type
• AI Application Examples by Business Area
• Course Summary

AI in Industry

Course: 41 Minutes

• Course Overview
• AI in Industry
• AI in Finance
• AI in Marketing
• AI in Sales
• AI in Manufacturing
• AI in Transportation
• AI in Cybersecurity
• AI in Pharmaceuticals
• AI in Telecommunications
• AI in Utilities
• Course Summary

Leveraging Reusable AI Architecture Patterns

Course: 50 Minutes

• Course Overview
• AI Architecture Patterns
• Architecture Patterns in AI Development Life Cycles
• Federated Learning Pattern
• Data Lake Pattern
• Distinguish Business Logic from ML Models Pattern
• Gateway Routing Architecture Pattern
• Data-algorithm-serving-evaluator Pattern
• Closed-loop Intelligence Pattern
• Daisy Architecture Pattern
• Kappa and Lambda Architecture Patterns
• Microservices and Event-driven ML Microservices Patterns
• AI Architecture Anti-patterns
• Course Summary

Evaluating Current and Future AI Technologies and Frameworks

Course: 40 Minutes

• Course Overview
• AI Technologies: Platforms, Frameworks, Libraries
• AI Platforms: H2O, IBM Watson, Amazon Lex, and MS CS
• TensorFlow Framework
• Keras Framework
• PyTorch Framework
• MXNet Framework
• CNTK Framework
• Cortex Framework
• AI Libraries
• Pre-trained Models
• AI Datasets
• Course Summary

Explainable AI

Course: 43 Minutes

• Course Overview
• Explainable AI
• The Interpretability Problem
• Right to Explanation
• Counterfactual Method
• Axiomatic Attribution
• Intelligible Models
• Monotonicity
• Rationalization
• Feature Visualization
• Course Summary

Online Mentor
You can reach your Mentor by entering chats or submitting an email.

Final Exam assessment
Estimated duration: 90 minutes

Practice Labs: DevOps Automated Tester (estimated duration: 8 hours)
Explore topics and scenarios typically encountered by AI Architects such as working with an AI Steward Board, implementing an AI analytics dashboard, and identifying similarities and differences in AI applications implemented across difference industries. You will also be tasked with identifying the best platform given a scenario, comparing, and contrasting Parameter-Sharing and Federated Learning AI architectures, and applying AI explain ability methods. This lab provides access to tools typically used by AI Architects, including: - Jupyter Notebook - Anaconda