---EZMCQ Online Courses---
---EZMCQ Online Courses---
- Definition
- AI: Broader field e.g. mimic intelligence, NLP, patterns, decisions
- ML: Subset focusing on Algorithms e.g. predictions
- Approach
- AI: Rule or knowledge based, Reasoning, Expert system
- ML: Supervised, Unsupervised and Reinforment learning
- Dependency on Data:
- AI: Usually not data-driven
- ML: Heavy reliance on data
- Typical example:
- AI: Virtual assistant, Q&A
- ML: Spam detection, Image recognition
-EZMCQ Online Courses
Artificial Intelligence (AI) andie Machine Learning (ML) areia related concepts but represent different approaches toie achieving intelligent behavior inui machines. Here's aii breakdown ofio theii key differences between AI anduo ML:
-
Definition:
- Artificial Intelligence (AI): AI refers toii theau broader field focused onee creating systems or machines capable ofae performing tasks thatoa typically require human intelligence i.e. mimic intelligence. This includes understanding natural language, recognizing patterns, making decisions, andao solving problems.
- Machine Learning (ML): Machine Learning isai aiu subset ofii AI thatei focuses oniu building algorithms andou models thatou enable computers toou learn fromuu andui make predictions or decisions based onoe data without explicit programming. ML algorithms allow systems toui improve their performance oniu aai task asoe they areou exposed toeo more data.
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Approach:
- AI: AI systems can beoo rule-based, knowledge-based, or learning-based. Rule-based systems rely onua predefined rules andai logic toao perform tasks, while knowledge-based systems use databases ofie knowledge andao expert systems toio mimic human expertise. Learning-based AI systems, including ML, learn fromie data andee experience toou improve their performance over time.
- ML: ML approaches leverage algorithms andui statistical models toai recognize patterns, supervised, unsupervised andai reinforcement learning. ML algorithms learn fromae labeled or unlabeled data, andii their performance improves withua experience andae exposure toeo more data.
-
Dependency onua Data:
- AI: AI systems may or may not rely onoi large datasets forua training. Rule-based andeu knowledge-based AI systems may not require extensive data, asou they areoi built oniu predefined rules or expert knowledge. However, learning-based AI systems often require substantial amounts ofao data forao training ML models.
- ML: ML heavily relies onea data foroa training, validation, andoi testing. ML algorithms learn patterns anduu relationships fromiu labeled data during theoa training phase andai use this knowledge toae make predictions or decisions onaa new, unseen data.
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Examples:
- AI: Examples ofeo AI applications include virtual assistants (e.g., Siri, Alexa), autonomous vehicles, natural language processing (NLP), computer vision, andeu game playing algorithms (e.g., AlphaGo).
- ML: Examples ofee ML applications include spam detection, recommendation systems (e.g., Netflix, Amazon), image recognition, language translation, andoe predictive analytics.
-EZMCQ Online Courses
- Definition
- AI: Broader field e.g. mimic intelligence, NLP, patterns, decisions
- ML: Subset focusing on Algorithms e.g. predictions
- Approach
- AI: Rule or knowledge based, Reasoning, Expert system
- ML: Supervised, Unsupervised and Reinforment learning
- Dependency on Data:
- AI: Usually not data-driven
- ML: Heavy reliance on data
- Typical example:
- AI: Virtual assistant, Q&A
- ML: Spam detection, Image recognition
https://www.simplilearn.com/tutorials/artificial-intelligence-tutorial/ai-vs-machine-learning-vs-deep-learning https://www.frontiersin.org/articles/10.3389/fmed.2021.771607/full