Introduction to Machine Learning and Artificial Intelligence - online

This two-day online introductory course offers an overview of Artificial Intelligence (AI) and Machine Learning (ML), covering key concepts, real-world applications, basic learning algorithms, and ethical considerations. Designed for beginners, it includes hands-on activities and case studies to illustrate how AI and ML are shaping industries like healthcare, finance, and communication. The course also features a real-time application demo using R or Python programming language.

The course covers: 

• Introduction to AI and ML

1. What is Artificial Intelligence (AI)
   • Definition and history
   • AI vs. Machine Learning vs. Deep Learning
2. Real-World Applications
   • Healthcare, finance, autonomous vehicles, chatbots
3. Types of AI
4. Ethical Considerations in AI

• Basics of Machine Learning

1. What is Machine Learning?
2. Types of ML
   • Supervised Learning (Classification and Regression)
   • Unsupervised Learning (Clustering)
   • Reinforcement Learning (Brief Overview)

• How Machines Learn

1. The Learning Process
   • Data collection and processing
   • Training vs. Testing data
2. Basic Algorithms Overview
   • Linear Regression (Simple Example)
   • Decision Trees
3. Evaluation Metrics: Accuracy, Precision, Recall

• Hands on Exercises using R/Python

1. Exploring and Visualizing Data
2. Hands on exercise (supervised learning)
   • Decision Trees
   • Random Forests
   • Support Vector Machine (SVM)
3. Hands on exercise (unsupervised learning)
   • K-Means Clustering
   • Hierarchical Clustering

• AI in the Real World and Future Trends

1. Case Studies: (healthcare, finance, NLP)
2. Limitations of AI
   • Bias in AI models
   • Data privacy concerns
3. Future of AI

By the end of the course participants will:

•  Understand the core concepts and types of AI and ML.
• Recognize key real-world applications of AI across industries.
• Differentiate between supervised, unsupervised, and reinforcement learning.
• Apply basic ML algorithms using Orange open-source software.
• Identify ethical issues and limitations associated with AI systems.