Understanding Machine Learning
- Definition — Machine learning (ML) is a branch of artificial intelligence (AI) and computer science that focuses on using data and algorithms to enable AI to imitate the way that humans learn, gradually improving its accuracy.
- How It Works — ML algorithms work through a decision process, an error function, and a model optimization process to make predictions or classifications based on input data and improve accuracy over time.
- Applications — ML finds applications in natural language processing, computer vision, speech recognition, email filtering, agriculture, medicine, and more, including predictive analytics in business.
- Relationship to AI — ML is a subfield of AI, specifically focusing on the development and study of statistical algorithms that can learn from data and generalize to unseen data, performing tasks without explicit instructions.
From ibm.com
Machine learning (ML) is a branch of artificial intelligence (AI) and computer science that focuses on the using data and algorithms to enable AI to imitate the way that humans learn, gradually improving its accuracy.
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How Machine Learning Works
- Decision Process — ML algorithms make predictions or classifications based on input data, which can be labeled or unlabeled, to estimate patterns in the data.
- Error Function — An error function evaluates the prediction of the model, comparing it to known examples to assess the model's accuracy.
- Model Optimization — The model is optimized by adjusting weights to reduce the discrepancy between known examples and the model estimate, repeating this process autonomously until a threshold of accuracy is met.
From ibm.com
A Decision Process: In general, machine learning algorithms are used to make a prediction or classification. Based on some input data, which can be labeled or unlabeled, your algorithm will produce an estimate about a pattern in the data.
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Try it for freeApplications of Machine Learning
- Natural Language Processing — ML is used for language translation, chatbots, and predictive text.
- Computer Vision — ML enables facial recognition, motion tracking, and object detection.
- Speech Recognition — ML powers voice assistants and speech-to-text applications.
- Email Filtering — ML algorithms filter spam and categorize emails.
- Agriculture — ML is used for crop monitoring and disease detection.
- Medicine — ML assists in medical image analysis and personalized treatment recommendations.
ML finds application in many fields, including natural language processing, computer vision, speech recognition, email filtering, agriculture, and medicine.
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Machine Learning in the Context of AI
- Subfield of AI — ML is a subfield of AI, focusing on statistical algorithms that learn from data.
- Generalization — ML algorithms generalize to unseen data, performing tasks without explicit instructions.
- Computational Statistics — ML methods are often based on computational statistics, using data to make decisions.
- Data Mining — ML is related to data mining, focusing on exploratory data analysis through unsupervised learning.
From en.wikipedia.org
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions.
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