Introduction to Artificial Intelligence (AI) - eLearning
450,00 EUR
- 6 hours
This introductory program in artificial intelligence provides a comprehensive overview of AI concepts and workflows, covering the basics of machine learning and deep learning. You'll explore AI by working on real-world use cases and understand the distinctions between supervised, unsupervised, and reinforcement learning. This free AI course is a perfect starting point for anyone aspiring to become an AI engineer.
Key Features
Language
Course and material are in English
Level
Beginner friendly
Access
1 year access to the self-paced study eLearning platform 24/7
2 hours of video content
with 6 hours study time recommended
Practices
Quizzes to refresh your studies
No exam
No exam for the course but student will get certification of training completion
Course Overview
If you’re looking to build your knowledge in artificial intelligence, and want to get an understanding of its business applications, then 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 be taught the difference between supervised, unsupervised and reinforcement learning.
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 apply machine learning workflow to solve business problems, clearly define various supervised and unsupervised AI algorithms, and measure ROI based on performance metrics.
Learning Outcomes
At the end of this course you will be able to understand:
Meaning and Applications
The meaning and purpose of AI, as well as the scope, stages, applications and effects
ML and DL
The basic concepts of machine learning and deep learning
Efficiency
How to effectively implement the steps of a machine learning workflow
Supervision
The difference between supervised, semi-supervised and unsupervised learning
Metrics
The role of performance metrics and how to identify key methods
Course timeline
Course introduction
Lesson 01
Decoding artificial intelligence
Lesson 02
- Decoding Artificial Intelligence
- Meaning, Scope, and Stage of AI
- Three stages of AI
- Applications of AI
- Image Recognition
- Application of AI
- Effects of AI on Society
- Supervises learning for telemedicine
- Solves complex social problems
- Benefits multipl eindustries
Fundamentals of machine learning and deep learning
Lesson 03
- Fundamentals of Machine Learning and Deep Learning
- Meaning of Machine Learning
- Relationship between Machine Learning and Statistical Analysis
- Process of Machine Learning
- Types of Machine Learning
- Meaning of Unsupervised Learning
- Meaning of Semi-supervised Learning
- Algorithms of Machine Learning
- Regression
- Naive Bayes
- Machine Learning Algorithms
- Deep Learning
- Artificial Neural Network Definition
- Definition of perceptron
- Online and batch learning
Machine learning workflow
Lesson 04
- Machine Learning workflow
- Get more data
- Ask a sharp question
- Add data to the table
- Check for quality
- Transform features
- Answer the questions
- Use the answer
Performance metrics
Lesson 05
- Performance metrics
- Key methods of performance metrics
- Confusion matrix example
- Terms of confusion matrix
- Minimize false cases
- Minimize false positive example
- Accuracy, precision
- Recall or sensitivity
- Specificity
- F1 Score
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
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