Artificial Intelligence and Machine Learning - An Introduction to Core Principles

Overview

Master the fundamental concepts of AI and machine learning while exploring their transformative impact across various industries and real-world applications.

Syllabus

• Introduction to Artificial Intelligence and Machine Learning
Definition and history of AI and ML
Key differences between AI, ML, and data science
• Fundamental Concepts in Machine Learning
Supervised learning
Unsupervised learning
Reinforcement learning
• Data and Model Preparation
Data collection and preprocessing
Feature selection and engineering
Training, validation, and testing datasets
• Supervised Learning Algorithms
Linear regression
Logistic regression
Decision trees and random forests
Support vector machines (SVM)
Overview of neural networks
• Unsupervised Learning Techniques
Clustering algorithms (e.g., K-means, hierarchical)
Dimensionality reduction (e.g., PCA, t-SNE)
• Introduction to Neural Networks and Deep Learning
Basics of neural networks
Introduction to deep learning architectures
Overview of popular frameworks (e.g., TensorFlow, PyTorch)
• Model Evaluation and Optimization
Performance metrics (accuracy, precision, recall, F1 score)
Cross-validation
Hyperparameter tuning
• AI and ML in Real-world Applications
AI in healthcare
Machine learning in finance
AI applications in autonomous systems
AI for natural language processing
• Ethical Considerations in AI and ML
Bias and fairness in AI
Privacy concerns
Ethical decision-making in AI systems
• Future Trends and Opportunities in AI and ML
Emerging technologies in AI
Careers and skills in AI and ML

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Subjects

Data Science