Introduction to Artificial Intelligence and Machine Learning

Overview

Discover the fundamentals of AI and ML, from core algorithms to real-world applications across industries, while exploring future trends and key challenges in modern technology.

Syllabus

• Introduction to Artificial Intelligence and Machine Learning
Definition and significance of AI and ML
Historical context and evolution
• Core Concepts of AI
Understanding intelligence and knowledge representation
Problem-solving in AI
Search algorithms and optimization
• Machine Learning Basics
Types of machine learning: supervised, unsupervised, and reinforcement learning
Key components: datasets, features, training, and evaluation
• Fundamental Algorithms
Linear regression and logistic regression
Decision trees and random forests
Support vector machines
Neural networks and deep learning basics
• Data Handling and Preprocessing
Data cleaning and transformation
Feature selection and engineering
• Model Evaluation and Validation
Understanding overfitting and underfitting
Cross-validation techniques
Performance metrics
• AI and ML in Real-World Applications
AI in healthcare, finance, and automated systems
Case studies of successful implementations
• Challenges and Ethical Considerations
Bias in AI and ML models
Privacy and security concerns
Ethical AI and responsible innovation
• Future Trends in AI and ML
Emerging technologies and paradigms
The impact of AI on society and the workforce
The role of AI in next-generation technologies
• Practical Session: Building a Simple ML Model
Preparing a dataset
Implementing a basic machine learning model
Interpreting results and improving model performance
• Conclusion and Further Learning Resources
Recap of key concepts
Suggested readings and online resources for extended learning

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Subjects

Data Science