There are multiple definitions of Artificial Intelligence (AI), but the most common view is that it is software which enables a machine to think and act like a human, and to think and act rationally. Because AI differs from plain programming, the programming language will depend on the application, such as the ethics and reliability of its use. Gain the ability to communicate the value AI can bring to businesses today, along with multiple areas where AI is already being used.
Training topics to be covered include:
AI-900: Azure AI Fundamentals
• Artificial Intelligence and Machine Learning
• Machine Learning with Azure Services
• Using Azure Machine Learning Studio
• Authoring the Azure ML Studio Designer
• Evaluating Models with the ML Designer
• Anomaly Detection
• Natural Language Processing
• Creating a Conversational AI Bot
• Computer Vision
• Face and Optical Character Recognition
AI Architect Track 1: AI Apprentice
• Basic AI Theory
• Types of Artificial Intelligence
• Human-computer Interaction Overview
• Human-computer Interaction Methodologies
• Python AI Development – Introduction
• Python AI Development – Practice
• Computer Vision – Introduction
• Computer Vision – AI and Computer Vision
• Cognitive Models – Overview
• Cognitive Models – Approaches to Cognitive Learning
• Final Exam – AI Apprentice
AI Architect Track 2: AI Developer
• AI Developer Role
• Development Frameworks
• Working with Cognitive Toolkit (CNTK)
• Deep Learning Packages: Keras – a Neural Network Framework
• Introducing Apache Spark for AI Development
• Implementing AI with Amazon ML
• Implementing AI Using Cognitive Modeling
• Applying AI to Robotics
• Working with Google BERT: Elements of BERT
• Final Exam – AI Developer
AI Architect Track 2: AI Practitioner
• Role and Responsibilities
• Optimizing AI Solutions
• Tuning AI Solutions
• Advanced Functionality of Microsoft Cognitive Toolkit (CNTK)
• Working with the Keras Framework
• Using Apache Spark for AI Development
• Extending Amazon Machine Learning
• Using Intelligent Information Systems in AI
• Final Exam – AI Developer
AI Architect Track 2: AI Architect
• Elements of an Artificial Intelligence Architect
• AI Enterprise Planning
• AI in Industry
• Leveraging Reusable AI Architecture Patterns
• Evaluating Current and Future AI Technologies and Frameworks
• Explainable AI
• Final Exam – AI Architect
Course Outcomes
Upon the completion of this course, students will be able to:
• Deploy and test the prediction capabilities of a model
• Identify the features and capabilities of the Form Recognizer service
• Implement an interactive chat-bot capable of simple conversation
• Work with a Twitter dataset and Google BERT to create disaster Tweet classifiers
• Demonstrate how to use mdptoolbox library to describe how to set up a Markov Decision Process problem
• Describe the meaning and application of feature visualization
Target Student
The target students for this course consists of anyone interested in learning about the types of solution AI makes possible and the services that you can use to create them; this includes Machine Learning Engineers, Robotics Engineers, Computer Vision Engineers, Data Scientist and so on.
Features
Modality: Self-paced Duration: 43 Hours + 31 Hours of Virtual Practice Lab Offered: 24/5 Start Date: On the first Monday of every month Registration Ends: One week before start date Certificate: Yes
Instructor
