Course description

• Introduction to Artificial Intelligence: Explore the core concepts and foundations of AI, delving into real-world applications and innovative AI technologies.
• Tree Traversals: Master tree data structures and their traversal techniques, crucial for implementing AI algorithms efficiently.
• Uses of Artificial Intelligence: Understand the diverse applications of AI in industries like healthcare, finance, and autonomous systems, and how to harness its potential.
• Artificial Neural Networks (ANN): Deep dive into neural networks, learning how ANN models are structured and used for predictive analysis and pattern recognition.
• Autoencoders & Generative AI: Explore cutting-edge AI technologies, focusing on autoencoders for unsupervised learning and the rise of generative AI for creating complex models.
• Linear & Logistic Regression: Gain hands-on experience with linear and logistic regression for predictive analytics and classification tasks in various data-driven scenarios.
• Clustering & Classification: Develop a deep understanding of essential ML techniques like clustering for grouping data and classification methods for categorizing data points.
• Recurrent Neural Networks (RNN): Learn the intricacies of RNN, focusing on sequence prediction and the advanced capabilities of RNN in time-series data and natural language processing.

By the end of the course, you'll be equipped with practical knowledge and the ability to build, train, and optimize AI/ML models, providing a competitive edge in today's data-driven world.