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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필터링 기준:
191 결과
정렬 기준:
Machine Learning

Machine Learning

Stanford University
강좌
5점 만점에 4.9점을 받았습니다. 144705 리뷰
Neural Networks and Deep Learning

Neural Networks and Deep Learning

deeplearning.ai
강좌
5점 만점에 4.9점을 받았습니다. 91523 리뷰
Natural Language Processing with Classification and Vector Spaces

Natural Language Processing with Classification and Vector Spaces

deeplearning.ai
강좌
5점 만점에 4.6점을 받았습니다. 941 리뷰
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

deeplearning.ai
강좌
5점 만점에 4.9점을 받았습니다. 52822 리뷰
Mathematics for Machine Learning: Linear Algebra

Mathematics for Machine Learning: Linear Algebra

Imperial College London
강좌
5점 만점에 4.7점을 받았습니다. 7585 리뷰
Structuring Machine Learning Projects

Structuring Machine Learning Projects

deeplearning.ai
강좌
5점 만점에 4.8점을 받았습니다. 42363 리뷰
Convolutional Neural Networks

Convolutional Neural Networks

deeplearning.ai
강좌
5점 만점에 4.9점을 받았습니다. 34844 리뷰
Convolutional Neural Networks in TensorFlow

Convolutional Neural Networks in TensorFlow

deeplearning.ai
강좌
5점 만점에 4.7점을 받았습니다. 4869 리뷰
Google Cloud Platform Big Data and Machine Learning Fundamentals

Google Cloud Platform Big Data and Machine Learning Fundamentals

Google Cloud
강좌
5점 만점에 4.6점을 받았습니다. 11378 리뷰
Natural Language Processing with Probabilistic Models

Natural Language Processing with Probabilistic Models

deeplearning.ai
강좌
5점 만점에 4.8점을 받았습니다. 277 리뷰
Introduction to Artificial Intelligence (AI)

Introduction to Artificial Intelligence (AI)

IBM
강좌
5점 만점에 4.7점을 받았습니다. 4649 리뷰
Sequence Models

Sequence Models

deeplearning.ai
강좌
5점 만점에 4.8점을 받았습니다. 23751 리뷰
Machine Learning with Python

Machine Learning with Python

IBM
강좌
5점 만점에 4.7점을 받았습니다. 9415 리뷰
Natural Language Processing in TensorFlow

Natural Language Processing in TensorFlow

deeplearning.ai
강좌
5점 만점에 4.6점을 받았습니다. 3984 리뷰
Foundations of Data Science: K-Means Clustering in Python

Foundations of Data Science: K-Means Clustering in Python

University of London
강좌
5점 만점에 4.7점을 받았습니다. 210 리뷰
Fundamentals of Reinforcement Learning

Fundamentals of Reinforcement Learning

University of Alberta
강좌
5점 만점에 4.8점을 받았습니다. 1354 리뷰

    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.