기계 학습

기계 학습 강좌는 대규모 데이터를 활용하고 학습할 수 있는 시스템 만들기에 중점을 두고 있습니다. 연구 주제는 예측적 알고리즘, 자연 언어 처리 및 통계 패턴 인식을 포함하고 있습니다.

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Machine Learning
Stanford University
Machine Learning
강좌
Neural Networks and Deep Learning
deeplearning.ai
Neural Networks and Deep Learning
강좌
Natural Language Processing with Classification and Vector Spaces
deeplearning.ai
Natural Language Processing with Classification and Vector Spaces
강좌
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
deeplearning.ai
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
강좌
Mathematics for Machine Learning: Linear Algebra
Imperial College London
Mathematics for Machine Learning: Linear Algebra
강좌
Structuring Machine Learning Projects
deeplearning.ai
Structuring Machine Learning Projects
강좌
Natural Language Processing with Probabilistic Models
deeplearning.ai
Natural Language Processing with Probabilistic Models
강좌
Convolutional Neural Networks
deeplearning.ai
Convolutional Neural Networks
강좌
Convolutional Neural Networks in TensorFlow
deeplearning.ai
Convolutional Neural Networks in TensorFlow
강좌
Natural Language Processing in TensorFlow
deeplearning.ai
Natural Language Processing in TensorFlow
강좌
Machine Learning with Python
IBM
Machine Learning with Python
강좌
Sequence Models
deeplearning.ai
Sequence Models
강좌
Fundamentals of Reinforcement Learning
University of Alberta
Fundamentals of Reinforcement Learning
강좌
AI for Medical Diagnosis
deeplearning.ai
AI for Medical Diagnosis
강좌
Introduction to Artificial Intelligence (AI)
IBM
Introduction to Artificial Intelligence (AI)
강좌
Google Cloud Platform Big Data and Machine Learning Fundamentals
Google Cloud
Google Cloud Platform Big Data and Machine Learning Fundamentals
강좌
Sequences, Time Series and Prediction
deeplearning.ai
Sequences, Time Series and Prediction
강좌
Natural Language Processing with Sequence Models
deeplearning.ai
Natural Language Processing with Sequence Models
강좌
Foundations of Data Science: K-Means Clustering in Python
University of London
Foundations of Data Science: K-Means Clustering in Python
강좌

    기계 학습에 대한 자주 묻는 질문

  • Machine learning is a branch of artificial intelligence that seeks to build computer systems that can learn from data without human intervention. These powerful techniques rely on the creation of sophisticated analytical models that are “trained” to recognize patterns within a specific dataset before being unleashed to apply these patterns to more and more data, steadily improving performance without further guidance.

    For example, machine learning is making increasingly accurate image recognition algorithms possible. Human programmers provide a relatively small set of images that are labeled as “cars” or “not cars,” for instance, and then expose the algorithms to vastly larger numbers of images to learn from. While the iterative algorithms typically used in machine learning aren’t new, the power of today’s computing systems have enabled this method of data analysis to become more effective more rapidly than ever.