Artificial Intelligence and Machine Learning - An Introduction to Core Concepts and Applications

Overview

Discover the fundamentals of AI and ML, exploring core components, real-world applications, future trends, and key challenges in technology transformation across multiple industries.

Syllabus

• Introduction to Artificial Intelligence and Machine Learning
Definition and overview of AI and ML
Historical context and evolution
Differences and interrelations between AI and ML
• Core Components of AI and ML
Algorithms and models
Data and feature engineering
Training, validation, and testing processes
• Supervised and Unsupervised Learning
Classification and regression
Clustering and dimensionality reduction
Evaluation metrics
• Neural Networks and Deep Learning
Introduction to neural networks
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM)
• Natural Language Processing (NLP)
Basics of NLP
Text processing and sentiment analysis
Applications in chatbots and voice assistants
• Computer Vision
Image recognition and processing
Object detection and classification
Applications in autonomous vehicles and surveillance
• Real-World Applications of AI and ML
Healthcare: diagnostics and personalized medicine
Finance: algorithmic trading and fraud detection
Retail: recommendation systems and customer insights
Manufacturing: predictive maintenance and robotics
• Future Trends in AI and ML
AI ethics and responsible AI
Machine learning at the edge
Quantum computing and AI
• Key Challenges in AI and ML Deployment
Data privacy and security
Bias and fairness in algorithms
Interpretability and explainability
• Course Summary and Project Presentation
Recap of key concepts
Group discussion on future implications
Presentation of individual or group projects on AI/ML applications
• Additional Resources and Further Reading
Recommended books and papers
Online courses and tutorials
Professional organizations and conferences

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Subjects

Data Science