This comprehensive course is designed to provide students with a strong foundation in database management, machine learning (ML), and artificial intelligence (AI). Participants will learn how to effectively integrate databases into ML and AI systems, enabling them to harness the power of data for intelligent decision-making.

Module 1: Fundamentals of Databases

Introduction to databases and their role in information management.
Relational database concepts, normalization, and database design principles.
Practical skills in using SQL for data manipulation and retrieval.

Module 2: Basics of Machine Learning

Understanding the core concepts of machine learning.
Types of ML algorithms: supervised, unsupervised, and reinforcement learning.
Feature engineering, data preprocessing, and model evaluation.

Module 3: Introduction to Artificial Intelligence

Overview of artificial intelligence and its historical development.
Different branches of AI, including symbolic AI, machine learning, and deep learning.
Ethical considerations and societal impacts of AI.

Module 4: Data Processing and Analysis

Techniques for data cleaning, preprocessing, and exploratory data analysis.
Strategies for handling missing data and outliers.
Visualization tools for effective data analysis.

Module 5: Database Integration with ML and AI

Connecting databases to machine learning and AI frameworks.
Utilizing databases as data sources for training and testing ML models.
Case studies of successful integration in real-world applications.

Module 1: Fundamentals of Databases

Module 2: Basics of Machine Learning

Module 3: Introduction to Artificial Intelligence

Module 4: Data Processing and Analysis

Module 5: Database Integration with ML and AI

Module 6: Advanced Machine Learning Algorithms