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Questions and Answers
What is the primary goal of machine learning?
What is the primary goal of machine learning?
- To mimic human intelligence
- To process large amounts of data
- To achieve artificial intelligence (correct)
- To design algorithms
What is a characteristic of cloud computing?
What is a characteristic of cloud computing?
- Limited resource allocation
- Managed by the user
- Network accessible (correct)
- Fixed storage capacity
What is a type of machine learning?
What is a type of machine learning?
- Cloud computing
- Artificial intelligence
- Reinforcement learning (correct)
- Deep learning
What is the primary goal of supervised learning?
What is the primary goal of supervised learning?
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What is a benefit of cloud computing?
What is a benefit of cloud computing?
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What is a characteristic of private cloud deployment?
What is a characteristic of private cloud deployment?
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What is the primary goal of reinforcement learning?
What is the primary goal of reinforcement learning?
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What is a benefit of using cloud computing resources?
What is a benefit of using cloud computing resources?
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Study Notes
Cloud Computing
- Cloud computing provides flexible self-service, network-accessible computing resource pools that can be allocated to meet demand.
- There are three service models: IaaS (Infrastructure as a Service), PaaS (Platform as a Service), and SaaS (Software as a Service).
- Deployment models include Private, Public, and Hybrid.
- Benefits of cloud computing include scalability, storage, security, data-loss prevention, maintenance, and pay-per-usage of resources.
- Characteristics of cloud computing include network accessibility, sustainability, and self-service on-demand allocation of resources.
- Service Level Agreements (SLA) are used to define the quality of service.
Machine Learning (ML)
- Artificial Intelligence (AI) is the ability to mimic human intelligence and behavior.
- Machine Learning (ML) is the process of achieving AI through designing algorithms that learn from data to become more accurate and effective over time.
- ML subfields include Deep Learning, which draws inspiration from how the brain works.
- Categories of ML include Supervised Learning, Unsupervised Learning, and Reinforcement Learning.
- Supervision types include Classification (yes/no), Regression (continuous), Clustering, and Unsupervised Learning.
- Reinforcement Learning involves training using trial and error, with models learning from external interactions and improving with time.
- Applications of ML include gaming, robotics, industrial automation, healthcare, and online stock trading.
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