Introduction to Artificial Intelligence and Machine Learning

Overview

Explore the core concepts of AI and ML, their practical applications, and future potential in this beginner-friendly introduction to modern technological innovation.

Syllabus

• Introduction to Artificial Intelligence
Definition and history of AI
Key components of AI
Differentiating AI from Machine Learning
• Fundamentals of Machine Learning
Definition and history of ML
Types of Machine Learning: Supervised, Unsupervised, Reinforcement
Key algorithms and their applications
• Data Preprocessing and Visualization
Importance of data in ML
Techniques for data cleaning and transformation
Introduction to data visualization tools
• Supervised Learning
Concepts of regression and classification
Overview of common algorithms: Linear Regression, Decision Trees, and Random Forests
Evaluation metrics for supervised learning
• Unsupervised Learning
Introduction to clustering and association
Algorithms: K-Means, Hierarchical Clustering
Dimensionality reduction techniques: PCA and t-SNE
• Neural Networks and Deep Learning
Understanding neural networks
Introduction to Deep Learning frameworks: TensorFlow and PyTorch
Basics of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)
• Reinforcement Learning
Concepts and examples
Markov Decision Processes (MDPs)
Introduction to Q-learning and Deep Q-Networks (DQNs)
• AI in Practice
Real-world applications of AI and ML
Case studies in various industries: healthcare, finance, and autonomous systems
Ethical considerations and AI safety
• Future Trends in AI and ML
Emerging technologies and research areas
Impact of AI on society and job markets
AI and sustainability innovations
• Course Summary and Further Learning
Recap of key concepts
Resources for continued education
Online communities and networks for AI professionals

Subjects

Data Science