Intelligent Applications Fundamentals

• An understanding of basic programming concepts
• Experience with Python programming
• Familiarity with basic statistics and mathematics

Audience

• IT Professionals

Intelligent Applications are software systems that utilize machine learning, data analytics, and other advanced technologies to simulate human-like intelligence, enabling them to perform tasks that typically require human intelligence.

This instructor-led, live training (online or onsite) is aimed at beginner-level IT professionals who wish to gain a foundational understanding of intelligent applications and how they can be applied in various industries.

By the end of this training, participants will be able to:

• Understand the history, principles, and impact of artificial intelligence.
• Identify and apply different machine learning algorithms.
• Manage and analyze data effectively for AI applications.
• Recognize the practical applications and limitations of AI in different sectors.
• Discuss the ethical considerations and societal implications of AI technology.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to AI

• History of AI
• Definitions and terminology
• AI vs. human intelligence
• Future trends and potential

Machine Learning Basics

• Types of machine learning: supervised, unsupervised, reinforcement
• Key ML algorithms
• ML workflow: from data collection to model evaluation

Data Management

• Data collection techniques
• Data cleaning and preprocessing
• Data analysis and visualization

AI in Practice

• Case studies of AI applications
• Industry-specific AI solutions
• AI in consumer products

Ethical Considerations

• AI and job displacement
• Bias and fairness in AI
• Privacy and security issues
• Future of AI ethics

Lab Project

• Python programming assignments
• Data analysis projects using real-world datasets
• Development of a simple ML model

Summary and Next Steps