A4.1
Machine learning fundamentals
Learning approaches, Real-world applications, Processing, Storage, Scalability
A4.2
Data preprocessingHL
Data cleaning, Missing data, Normalization, Standardization, Feature selection, Dimensionality reduction
A4.3
Machine learning approachesHL
Regression, Classification, Model evaluation, Clustering, Association rules, Reinforcement learning, Genetic algorithms, Neural networks, Model selection
A4.4
Ethical considerations
Training-data bias, Fairness, Accountability, Online communication, Privacy, Emerging technologies
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