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Browse CoursesMachine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so.
By : Manas Dasgupta
Concepts and Projects based learning for aspiring Machine Learning Professionals...
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106 lectures All Level
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TinyML is a program for machine learning (ML) and in this course we will help you sta...
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By : Temotec Learning Academy
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Machine Learning is a branch of artificial intelligence that focuses on developing algorithms and models that enable computers to learn from data and make predictions or decisions without explicit programming. ML allows systems to improve their performance over time based on experience.
Machine Learning works by training models on labeled data, allowing the algorithm to learn patterns and relationships. These trained models can then make predictions or decisions when presented with new, unseen data. The process involves training, testing, and iterating to improve the model's accuracy.
Types of ML include:
Supervised Learning: Models learn from labeled data with input-output pairs.
Unsupervised Learning: Models discover patterns and relationships in unlabeled data.
Reinforcement Learning: Agents learn through interaction with an environment, receiving rewards or penalties based on actions.
Semi-Supervised Learning and Self-Supervised Learning: Hybrid approaches using both labeled and unlabeled data.
ML is used in various applications, including:
Image and speech recognition
Natural language processing
Recommendation systems
Fraud detection
Predictive analytics
Autonomous vehicles
Healthcare diagnostics
Machine Learning algorithms process data and learn patterns to create models. These models, based on learned parameters, can then make predictions or decisions when presented with new data. Common algorithms include decision trees, neural networks, support vector machines, and clustering algorithms.