Introduction to Artificial Intelligence (AI) - eLearning

Introduction to Artificial Intelligence (AI) - eLearning

Gain an understanding of AI concepts, workflows and performance metrics

Artificial Intelligence Introduction to Artificial Intelligence E-learning: Gain an understanding of AI concepts, workflows and performance metrics.

If you want to build your knowledge in artificial intelligence and want to gain an understanding of its business applications, our Introduction to Artificial Intelligence course is exactly what you need! With this course, you'll get a broad overview of AI concepts, workflows, and performance metrics, as well as machine learning and deep learning. You'll find out how clustering and classification algorithms help identify AI business applications and will also learn the difference between supervised, unsupervised, and reinforcement learning.

Course Overview

The basic terminologies, concepts, scope and stages of Artificial Intelligence are all covered in this course, it will also look at their effect on real-world business processes and how AI drives business value. By the end of the course, you will be able to use machine learning workflows to solve business problems, clearly define various supervised and unsupervised AI algorithms and measure ROI based on performance metrics.

What's included?

• Course and material are in English
• Beginner friendly
• 1 year access to the self-paced study eLearning platform 24/7
• 2 hours of video content
• 6 hours study time recommended
• Practices
• Quizzes to refresh your studies
• No exam for the course but student will get certification of training completion

AIMS OF THE COURSE

At the end of this course, you will be able to understand:

• The meaning and purpose of AI, as well as the scope, stages, applications, and impacts
• The basic concepts of machine learning and deep learning
• How to effectively implement the steps of a machine learning workflow
• The difference between supervised, semi-supervised and unsupervised learning
• The role of performance metrics and how to identify key practices

Target Audience

The course is designed for individuals from various backgrounds who want to gain foundational knowledge of artificial intelligence and its applications. No formal prerequisites needed. but a basic understanding of mathematics, statistics, and programming will be beneficial.

• IT Professionals
• Non-Technical Professionals
• Data Analysts
• Students
• Educators and Researchers
• Entrepreneurs and Innovators

Curriculum:

Lesson 01 - Decoding Artificial Intelligence

• Decoding artificial intelligence
• Meaning, scope and phases of artificial intelligence
• Three phases of artificial intelligence
• Applications of artificial intelligence
• Identification of images
• Applications of artificial intelligence - examples
• Impact of artificial intelligence on society
• Monitoring learning for telemedicine
• Solves complex social problems
• Benefits for multiple industries
• Key conclusions

Lesson 02 - Machine Learning and Deep Learning Basics

• Machine learning basics and deep learning
• Importance of machine learning
• The relationship between machine learning and statistical analysis
• Process of machine learning
• Types of machine learning
• Importance of unsupervised learning
• Importance of semi-supervised learning
• Algorithms for machine learning
• Regression
• Naive Bayes
• Classification with Naive Bayes
• Algorithms for machine learning
• Deep learning
• Definition of artificial neural networks
• Definition of perceptron
• Online and batch learning
• Key conclusions

Lesson 03 - Machine learning workflow

• Objectives for learning
• Machine learning workflow
• Get more data
• Ask a sharp question
• Add data to the table
• Checking quality
• Converting functions
• Answer the questions
• Use the answer
• Key conclusions

Lesson 04 - Performance measurements

• Performance metrics
• The need for performance measurement
• Key methods for performance measurement
• Example of confusion matrix
• Terms in the confusion matrix
• Minimize the number of incorrect cases
• Minimize the number of false positives
• Accuracy
• Precision
• Recall or sensitivity
• Specificity
• F1 score
• Key conclusions