機械学習

機械学習のコースでは、大規模なデータセットを活用した学習システムの作成について学習します。学習トピックには、予測アルゴリズム、自然言語処理、統計パターン認識などがあります。

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Deep Learning
deeplearning.ai
Deep Learning
専門講座
IBM AI Foundations for Business
IBM
IBM AI Foundations for Business
専門講座
Applied Data Science
IBM
Applied Data Science
専門講座
Natural Language Processing
deeplearning.ai
Natural Language Processing
専門講座
TensorFlow in Practice
deeplearning.ai
TensorFlow in Practice
専門講座
Data Science: Foundations using R
Johns Hopkins University
Data Science: Foundations using R
専門講座
Mathematics for Machine Learning
Imperial College London
Mathematics for Machine Learning
専門講座
Data Engineering, Big Data, and Machine Learning on GCP
Google Cloud
Data Engineering, Big Data, and Machine Learning on GCP
専門講座
Reinforcement Learning
University of Alberta
Reinforcement Learning
専門講座
AI for Medicine
deeplearning.ai
AI for Medicine
専門講座
Advanced Machine Learning
National Research University Higher School of Economics
Advanced Machine Learning
専門講座
Advanced Data Science with IBM
IBM
Advanced Data Science with IBM
専門講座
Data Science: Statistics and Machine Learning
Johns Hopkins University
Data Science: Statistics and Machine Learning
専門講座
Машинное обучение и анализ данных
Moscow Institute of Physics and Technology
Машинное обучение и анализ данных
専門講座
Investment Management with Python and Machine Learning
EDHEC Business School
Investment Management with Python and Machine Learning
専門講座
Big Data
University of California San Diego
Big Data
専門講座
Machine Learning with TensorFlow on Google Cloud Platform
Google Cloud
Machine Learning with TensorFlow on Google Cloud Platform
専門講座
Machine Learning
University of Washington
Machine Learning
専門講座
Robotics
University of Pennsylvania
Robotics
専門講座

    機械学習に関するよくある質問

  • 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.