Introduction to Artificial intelligence

Artificial intelligence
 

This 5-day training program is designed to provide a comprehensive introduction to Artificial Intelligence, aimed at individuals with little to no prior experience in the field.
 

Participants will be introduced to the core concepts, algorithms, and tools that power AI technologies. The program combines theoretical learning with practical, hands-on exercises, allowing participants to build real-world AI models and applications using widely used programming languages and tools such as Python, TensorFlow, and OpenAI Gym.
 

By the end of the program, participants will have a solid understanding of AI’s fundamental principles, its key applications in various industries, and the hands-on experience required to begin using AI tools and techniques in their professional work.

• Define Artificial Intelligence, its history, and its relevance in modern society
• Differentiate between types of AI (Narrow AI vs. General AI)
• Explore the key subfields of AI, including Machine Learning, Deep Learning, and Natural Language Processing
• Understand the principles of supervised, unsupervised, and reinforcement learning
• Implement key machine learning algorithms such as linear regression, decision trees, and K-means clustering
• Evaluate machine learning models using appropriate metrics and performance measures
• Identify and prepare data for AI applications, including handling missing values and normalizing data
• Implement feature engineering and dimensionality reduction techniques
• Work with real-world datasets and preprocess data using Python
• Understand the basics of neural networks and deep learning techniques
• Apply convolutional neural networks (CNN) for image classification tasks
• Understand the role of Recurrent Neural Networks (RNN) in sequential data analysis
• Understand the key concepts in Natural Language Processing
• Implement basic NLP tasks such as sentiment analysis and text classification
• Use NLP libraries like NLTK and SpaCy to process and analyze textual data
• Understand the core concepts of computer vision and image recognition
• Use deep learning models like CNNs for image classification and object detection tasks
• Apply transfer learning with pre-trained models for more efficient image analysis
• Understand the fundamentals of reinforcement learning, including agents, environments, and rewards
• Implement simple reinforcement learning algorithms using OpenAI Gym
• Explore how reinforcement learning can be used in real-world applications like gaming and robotics
• Identify the ethical challenges associated with AI, including bias, fairness, and privacy concerns
• Understand the social and economic implications of AI on jobs, inequality, and society
• Recognize the importance of responsible AI development and usage