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:
37 결과
정렬 기준:
Deep Learning

Deep Learning

deeplearning.ai
특화 과정
5점 만점에 4.8점을 받았습니다.
IBM AI Foundations for Business

IBM AI Foundations for Business

IBM
특화 과정
5점 만점에 4.7점을 받았습니다.
Applied Data Science

Applied Data Science

IBM
특화 과정
5점 만점에 4.6점을 받았습니다.
Natural Language Processing

Natural Language Processing

deeplearning.ai
특화 과정
5점 만점에 4.6점을 받았습니다.
TensorFlow in Practice

TensorFlow in Practice

deeplearning.ai
특화 과정
5점 만점에 4.7점을 받았습니다.
Data Science: Foundations using R

Data Science: Foundations using R

Johns Hopkins University
특화 과정
5점 만점에 4.6점을 받았습니다.
Data Engineering, Big Data, and Machine Learning on GCP

Data Engineering, Big Data, and Machine Learning on GCP

Google Cloud
특화 과정
5점 만점에 4.6점을 받았습니다.
Mathematics for Machine Learning

Mathematics for Machine Learning

Imperial College London
특화 과정
5점 만점에 4.4점을 받았습니다.
Reinforcement Learning

Reinforcement Learning

University of Alberta
특화 과정
5점 만점에 4.7점을 받았습니다.
Machine Learning with TensorFlow on Google Cloud Platform

Machine Learning with TensorFlow on Google Cloud Platform

Google Cloud
특화 과정
5점 만점에 4.5점을 받았습니다.
AI for Medicine

AI for Medicine

deeplearning.ai
특화 과정
5점 만점에 4.7점을 받았습니다.
Advanced Machine Learning

Advanced Machine Learning

National Research University Higher School of Economics
특화 과정
5점 만점에 4.4점을 받았습니다.
Big Data

Big Data

University of California San Diego
특화 과정
5점 만점에 4.5점을 받았습니다.
Investment Management with Python and Machine Learning

Investment Management with Python and Machine Learning

EDHEC Business School
특화 과정
5점 만점에 4.3점을 받았습니다.
Advanced Data Science with IBM

Advanced Data Science with IBM

IBM
특화 과정
5점 만점에 4.5점을 받았습니다.
Машинное обучение и анализ данных

Машинное обучение и анализ данных

Moscow Institute of Physics and Technology
특화 과정
5점 만점에 4.7점을 받았습니다.

    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.