"기계 학습"의 두 판 사이의 차이

2020년 12월 22일 (화) 22:32 판

개요

컴퓨터 과학의 핵심 과제 중 하나는 컴퓨터에게 수행 방법을 알려주지 않고 수행해야 하는 작업을 수행하도록 하는 것이다.
많은 경우 작업은 매개변수가 있는 수학적 모델로 기술된다
이러한 모델의 매개변수는 통계적 방법을 사용하여 데이터에서 추정해야 한다.
기계 학습이란 이 매개변수를 찾아가는 과정이라 할 수 있다.
fundamental problems in machine learning
- regression
- classification
- 차원축소
- probability density estimation
기계 학습은 시스템에서 학습을 위해 사용하는 피드백의 특성에 따라 보통 다음의 세 가지 범주로 나누어진다:

노트

It is problems like this which machine learning is trying to solve.^[1]
“Deep learning” – another hot topic buzzword – is simply machine learning which is derived from “deep” neural nets.^[1]
Without a doubt, machine learning is proving itself to be a technology with far-reaching transformative powers.^[1]
Machine learning is the key which has unlocked it, and its potential future applications are almost unlimited.^[1]
Machine learning brings together computer science and statistics to harness that predictive power.^[2]
The past decade has brought tremendous advances in an exciting dimension of artificial intelligence—machine learning.^[3]
A prediction, in the context of machine learning, is an information output that comes from entering some data and running an algorithm.^[3]
It can also be dangerously easy to introduce biases into machine learning, especially if multiple factors are in play.^[3]
In many ways, building a sustainable business in machine learning is much like building a sustainable business in any industry.^[3]
For example, machine learning can reveal customers who are likely to churn, likely fraudulent insurance claims, and more.^[4]
Companies that effectively implement machine learning and other AI technologies gain a massive competitive advantage.^[4]
Machine learning could also be used for security applications, such as analysing email communications or internet usage.^[5]
The project will focus on current and near-term (5-10 years) applications of machine learning.^[5]
The above definition encapsulates the ideal objective or ultimate aim of machine learning, as expressed by many researchers in the field.^[6]
As with any concept, machine learning may have a slightly different definition, depending on whom you ask.^[6]
– Stanford “Machine learning is based on algorithms that can learn from data without relying on rules-based programming.^[6]
Emerj helps businesses get started with artificial intelligence and machine learning.^[6]
Funding for research and development in the fields of machine learning and artificial intelligence is growing at a rapid pace.^[7]
The field of machine learning is booming and having the right skills and experience can help you get a path to a lucrative career.^[7]
From Apple to Google to Toyota, companies across the world are pouring resources into developing AI systems with machine learning.^[8]
While there are different forms of AI, machine learning (ML) represents today's most widely valued mechanism for reaching intelligence.^[8]
Machine learning became popular in the 1990s, and returned to the public eye when Google's DeepMind beat the world champion of Go in 2016.^[8]
Since then, ML applications and machine learning's popularity have only increased.^[8]
ML is one of the most exciting technologies that one would have ever come across.^[9]
Machine learning is a branch of artificial intelligence that includes methods, or algorithms, for automatically creating models from data.^[10]
Game-playing machine learning is strongly successful for checkers, chess, shogi, and Go, having beaten human world champions.^[10]
Machine learning depends on a number of algorithms for turning a data set into a model.^[10]
As with all machine learning, you need to check the predictions of the neural network against a separate test data set.^[10]
A diverse community of developers, enterprises and researchers are using ML to solve challenging, real-world problems.^[11]
Natural Language Derive insights from unstructured text using Google machine learning.^[12]
Translation Dynamically translate between languages using Google machine learning.^[12]
A very important group of algorithms for both supervised and unsupervised machine learning are neural networks.^[13]
DeepMind continue to break new ground in the field of machine learning.^[13]
But beyond these very visible manifestations of machine learning, systems are starting to find a use in just about every industry.^[13]
Machine learning enables analysis of massive quantities of data.^[14]
The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning.^[15]
Machine learning involves computers discovering how they can perform tasks without being explicitly programmed to do so.^[15]
As a scientific endeavor, machine learning grew out of the quest for artificial intelligence.^[15]
However, an increasing emphasis on the logical, knowledge-based approach caused a rift between AI and machine learning.^[15]
In machine learning, a situation in which a model's predictions influence the training data for the same model or another model.^[16]
More typically in machine learning, a hyperplane is the boundary separating a high-dimensional space.^[16]
An i.i.d. is the ideal gas of machine learning—a useful mathematical construct but almost never exactly found in the real world.^[16]
In machine learning, often refers to the process of making predictions by applying the trained model to unlabeled examples.^[16]
The main intend of machine learning is to build a model that performs well on both the training set and the test set.^[17]
Machine learning is the concept that a computer program can learn and adapt to new data without human intervention.^[18]
The various data applications of machine learning are formed through a complex algorithm or source code built into the machine or computer.^[18]
Machine learning is used in different sectors for various reasons.^[18]
The supply of able ML designers has yet to catch up to this demand.^[19]
Determining which inputs to use is an important part of ML design.^[19]
The goal of ML is never to make “perfect” guesses, because ML deals in domains where there is no such thing.^[19]
Fortunately, the iterative approach taken by ML systems is much more resilient in the face of such complexity.^[19]
– Make revolutionary advances in machine learning and AI.^[20]
Machine learning and AI are often discussed together, and the terms are sometimes used interchangeably, but they don’t mean the same thing.^[21]
Machine learning and the technology around it are developing rapidly, and we're just beginning to scratch the surface of its capabilities.^[21]
All ML tasks can be represented this way, or it's not an ML problem from the beginning.^[22]
That's why selecting the right features usually takes way longer than all the other ML parts.^[22]
That's why the phrase "will neural nets replace machine learning" sounds like "will the wheels replace cars".^[22]
1.1 Supervised Learning Classical machine learning is often divided into two categories – Supervised and Unsupervised Learning.^[22]
This type of machine learning involves algorithms that train on unlabeled data.^[23]
This approach to machine learning involves a mix of the two preceding types.^[23]
Uses of machine learning Today, machine learning is used in a wide range of applications.^[23]
But just because some industries have seen benefits doesn't mean machine learning is without its downsides.^[23]

소스

↑ ^1.0 ^1.1 ^1.2 ^1.3 What Is Machine Learning - A Complete Beginner's Guide
↑ Introduction to Machine Learning Course
↑ ^3.0 ^3.1 ^3.2 ^3.3 How to Win with Machine Learning
↑ ^4.0 ^4.1 DataRobot Artificial Intelligence Wiki
↑ ^5.0 ^5.1 What is machine learning?
↑ ^6.0 ^6.1 ^6.2 ^6.3 What is Machine Learning?
↑ ^7.0 ^7.1 Learn Machine Learning with Online Courses and Classes
↑ ^8.0 ^8.1 ^8.2 ^8.3 Machine learning: A cheat sheet
↑ Machine Learning
↑ ^10.0 ^10.1 ^10.2 ^10.3 What is machine learning? Intelligence derived from data
↑ TensorFlow
↑ ^12.0 ^12.1 Google Cloud
↑ ^13.0 ^13.1 ^13.2 What is machine learning? Everything you need to know
↑ What is Machine Learning? A definition - Expert System
↑ ^15.0 ^15.1 ^15.2 ^15.3 Machine learning
↑ ^16.0 ^16.1 ^16.2 ^16.3 Machine Learning Glossary
↑ Machine Learning - an overview
↑ ^18.0 ^18.1 ^18.2 Machine Learning
↑ ^19.0 ^19.1 ^19.2 ^19.3 An Introduction to Machine Learning Theory and Its Applications: A Visual Tutorial with Examples
↑ What is Deep Learning?
↑ ^21.0 ^21.1 What is machine learning?
↑ ^22.0 ^22.1 ^22.2 ^22.3 Machine Learning for Everyone
↑ ^23.0 ^23.1 ^23.2 ^23.3 What is Machine learning (ML)?

노트

위키데이터

ID : Q2539

말뭉치

If you are a newbie in machine learning you may have thought that what programming language should I learn?^[1]
Machine Learning is one of the fastest-growing fields which has witnessed an exponential growth in the technical world.^[1]
Python leads all the other languages with more than 60% of machine learning developers are using and prioritising it for development because python is easy to learn.^[1]
This programming language is the “Jack of all the trade” and continues to dominate over in the ML industry also.^[1]
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.^[2]
Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events.^[2]
In contrast, unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled.^[2]
Reinforcement machine learning algorithms is a learning method that interacts with its environment by producing actions and discovers errors or rewards.^[2]
To help those interested in the field better understand how to break into a career in machine learning, we compiled the most important details and resources.^[3]
In terms of specific positions, 94% of job postings that contained AI or machine learning terminology were for machine learning engineers, the report found.^[3]
"Software is eating the world and machine learning is eating software," Vitaly Gordon, vice president of data science and software engineering for Salesforce Einstein, told TechRepublic.^[3]
"Machine learning engineering is a discipline that requires production grade coding, PhD level machine learning, and the business acumen of a product manager.^[3]
So, can machine learning be self-taught?^[4]
Even though there are many different skills to learn in machine learning it is possible for you to self-teach yourself machine learning.^[4]
There are actually so many machine learning courses available now that choosing the right path for you can be quite daunting.^[4]
course is that it will give you the chance to see if machine learning is something that you are actually interested in.^[4]
which statuses you are reacting to—and using machine learning to make a highly educated guess about what you might want next.^[5]
Deep learning is machine learning on steroids: it uses a technique that gives machines an enhanced ability to find—and amplify—even the smallest patterns.^[5]
In machine learning, algorithms are 'trained' to find patterns and features in massive amounts of data in order to make decisions and predictions based on new data.^[6]
There are four basic steps for building a machine learning application (or model).^[6]
Training data is a data set representative of the data the machine learning model will ingest to solve the problem it’s designed to solve.^[6]
Supervised machine learning trains itself on a labeled data set.^[6]
Machine learning is a branch of artificial intelligence that includes methods, or algorithms, for automatically creating models from data.^[7]
Unlike a system that performs a task by following explicit rules, a machine learning system learns from experience.^[7]
We hear about applications of machine learning on a daily basis, although not all of them are unalloyed successes.^[7]
Game-playing machine learning is strongly successful for checkers, chess, shogi, and Go, having beaten human world champions.^[7]
Machine learning is one modern innovation that has helped man enhance not only many industrial and professional processes but also advances everyday living.^[8]
But what is machine learning?^[8]
Currently, machine learning has been used in multiple fields and industries.^[8]
The intelligent systems built on machine learning algorithms have the capability to learn from past experience or historical data.^[8]
Machine 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.^[9]
Types of machine learning Classical machine learning is often categorized by how an algorithm learns to become more accurate in its predictions.^[9]
this type of machine learning, data scientists supply algorithms with training data and define the variables they want the algorithm to assess for correlations.^[9]
This type of machine learning involves algorithms that train on unlabeled data.^[9]
Similar drag and drop modules have been added to Azure Machine Learning designer.^[10]
Scoring is also called prediction, and is the process of generating values based on a trained machine learning model, given some new input data.^[10]
Machine Learning Studio (classic) supports a flexible, customizable framework for machine learning.^[10]
In this phase of machine learning, you apply a trained model to new data, to generate predictions.^[10]
Some machine learning algorithms are described as “supervised” machine learning algorithms as they are designed for supervised machine learning problems.^[11]
Some machine learning algorithms do not just experience a fixed dataset.^[11]
Fitting a machine learning model is a process of induction.^[11]
In the context of machine learning, once we use induction to fit a model on a training dataset, the model can be used to make predictions.^[11]
A very important group of algorithms for both supervised and unsupervised machine learning are neural networks.^[12]
For instance, consider the example of using machine learning to recognize handwritten numbers between 0 and 9.^[12]
As the use of machine learning has taken off, so companies are now creating specialized hardware tailored to running and training machine-learning models.^[12]
In 2020, Google said its fourth-generation TPUs were 2.7 times faster than previous gen TPUs in MLPerf, a benchmark which measures how fast a system can carry out inference using a trained ML model.^[12]
📚 Check out our editorial recommendations on the best machine learning books.^[13]
next → ← prev Applications of Machine learning Machine learning is a buzzword for today's technology, and it is growing very rapidly day by day.^[14]
We are using machine learning in our daily life even without knowing it such as Google Maps, Google assistant, Alexa, etc.^[14]
Below are some most trending real-world applications of Machine Learning: 1.^[14]
While using Google, we get an option of "Search by voice," it comes under speech recognition, and it's a popular application of machine learning.^[14]
A subset of machine learning is closely related to computational statistics, which focuses on making predictions using computers; but not all machine learning is statistical learning.^[15]
The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning.^[15]
Machine learning involves computers discovering how they can perform tasks without being explicitly programmed to do so.^[15]
The discipline of machine learning employs various approaches to teach computers to accomplish tasks where no fully satisfactory algorithm is available.^[15]
Note: Unfortunately, as of April 2019 we no longer update non-English versions of Machine Learning Crash Course.^[16]
Bias (also known as the bias term) is referred to as b or w 0 in machine learning models.^[16]
A type of machine learning model for distinguishing among two or more discrete classes.^[16]
Machine learning developers may inadvertently collect or label data in ways that influence an outcome supporting their existing beliefs.^[16]
Machine learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world.^[17]
Machine learning came directly from minds of the early AI crowd, and the algorithmic approaches over the years included decision tree learning, inductive logic programming.^[17]
As it turned out, one of the very best application areas for machine learning for many years was computer vision, though it still required a great deal of hand-coding to get the job done.^[17]
Deep learning has enabled many practical applications of machine learning and by extension the overall field of AI.^[17]
ML is one of the most exciting technologies that one would have ever come across.^[18]
Machine learning is the concept that a computer program can learn and adapt to new data without human intervention.^[19]
The various data applications of machine learning are formed through a complex algorithm or source code built into the machine or computer.^[19]
Machine learning is used in different sectors for various reasons.^[19]
Lending institutions can incorporate machine learning to predict bad loans and build a credit risk model.^[19]
A diverse community of developers, enterprises and researchers are using ML to solve challenging, real-world problems.^[20]
Natural Language Derive insights from unstructured text using Google machine learning.^[21]
Translation Dynamically translate between languages using Google machine learning.^[21]
At Emerj, the AI Research and Advisory Company, many of our enterprise clients feel as though they should be investing in machine learning projects, but they don’t have a strong grasp of what it is.^[22]
The above definition encapsulates the ideal objective or ultimate aim of machine learning, as expressed by many researchers in the field.^[22]
The purpose of this article is to provide a business-minded reader with expert perspective on how machine learning is defined, and how it works.^[22]
As with any concept, machine learning may have a slightly different definition, depending on whom you ask.^[22]
Machine Learning is an international forum for research on computational approaches to learning.^[23]
Rapidly build and deploy machine learning models using tools that meet your needs regardless of skill level.^[24]
Machine learning brings together computer science and statistics to harness that predictive power.^[25]
This is a class that will teach you the end-to-end process of investigating data through a machine learning lens.^[25]
Machine learning could also be used for security applications, such as analysing email communications or internet usage.^[26]
The project will focus on current and near-term (5-10 years) applications of machine learning.^[26]
Machine Learning can play a pivotal role in a range of applications such as Deep Learning, Reinforcement Learning, Natural Language Processing, etc.^[27]
Microsoft, Columbia, Caltech and other major universities and institutions offer introductory courses and tutorials in machine learning and artificial intelligence.^[27]
Gain a stronger understanding of the major machine learning projects with helpful examples.^[27]
Funding for research and development in the fields of machine learning and artificial intelligence is growing at a rapid pace.^[27]
In machine learning terms, Billy invented regression – he predicted a value (price) based on known historical data.^[28]
Three components of machine learning Without all the AI-bullshit, the only goal of machine learning is to predict results based on incoming data.^[28]
All ML tasks can be represented this way, or it's not an ML problem from the beginning.^[28]
That's why selecting the right features usually takes way longer than all the other ML parts.^[28]
The supply of able ML designers has yet to catch up to this demand.^[29]
Determining which inputs to use is an important part of ML design.^[29]
We stick to simple problems in this post for the sake of illustration, but the reason ML exists is because, in the real world, the problems are much more complex.^[29]
On this flat screen we can draw you a picture of, at most, a three-dimensional data set, but ML problems commonly deal with data with millions of dimensions, and very complex predictor functions.^[29]
Machine learning is a subset of artificial intelligence (AI) in which algorithms learn by example from historical data to predict outcomes and uncover patterns not easily spotted by humans.^[30]
For example, machine learning can reveal customers who are likely to churn, likely fraudulent insurance claims, and more.^[30]
Machine learning has practical implications across industry sectors, including healthcare, insurance, energy, marketing, manufacturing, financial technology (fintech), and more.^[30]
While most statistical analysis relies on rule-based decision-making, machine learning excels at tasks that are hard to define with exact step-by-step rules.^[30]
From Apple to Google to Toyota, companies across the world are pouring resources into developing AI systems with machine learning.^[31]
While there are different forms of AI, machine learning (ML) represents today's most widely valued mechanism for reaching intelligence.^[31]
What is machine learning?^[31]
Machine learning became popular in the 1990s, and returned to the public eye when Google's DeepMind beat the world champion of Go in 2016.^[31]
We will walk you step-by-step into the World of Machine Learning.^[32]
This course is fun and exciting, but at the same time, we dive deep into Machine Learning.^[32]
Machine Learning is making the computer learn from studying data and statistics.^[33]
In Machine Learning it is common to work with very large data sets.^[33]
Create ML lets you quickly build and train Core ML models right on your Mac with no code.^[34]
The Journal of Machine Learning Research (JMLR) provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning.^[35]
-Support vector machines, or SVMs, is a machine learning algorithm for classification.^[36]
Machine learning is speeding it up by orders of magnitude.^[37]
For instance, if you provide a machine learning model with many songs that you enjoy, along with their corresponding audio statistics (dance-ability, instrumentality, tempo, or genre).^[38]
The machine learning model looks at each picture in the diverse dataset and finds common patterns found in pictures with labels with comparable indications.^[38]
Unsupervised learning, another type of machine learning, is the family of machine learning algorithms, which have main uses in pattern detection and descriptive modeling.^[38]
Machine learning can be dazzling, particularly its advanced sub-branches, i.e., deep learning and the various types of neural networks.^[38]
Perhaps the most popular data science methodologies come from machine learning.^[39]
What distinguishes machine learning from other computer guided decision processes is that it builds prediction algorithms using data.^[39]

소스

↑ ^1.0 ^1.1 ^1.2 ^1.3 Top 5 Programming Languages and their Libraries for Machine Learning in 2020
↑ ^2.0 ^2.1 ^2.2 ^2.3 What is Machine Learning? A definition - Expert System
↑ ^3.0 ^3.1 ^3.2 ^3.3 How to become a machine learning engineer: A cheat sheet
↑ ^4.0 ^4.1 ^4.2 ^4.3 Can machine learning be self-taught?
↑ ^5.0 ^5.1 What is machine learning?
↑ ^6.0 ^6.1 ^6.2 ^6.3 What is Machine Learning?
↑ ^7.0 ^7.1 ^7.2 ^7.3 What is machine learning? Intelligence derived from data
↑ ^8.0 ^8.1 ^8.2 ^8.3 Top 10 real-life examples of Machine Learning
↑ ^9.0 ^9.1 ^9.2 ^9.3 What is Machine learning (ML)?
↑ ^10.0 ^10.1 ^10.2 ^10.3 ML Studio (classic): Machine Learning Scoring - Azure
↑ ^11.0 ^11.1 ^11.2 ^11.3 14 Different Types of Learning in Machine Learning
↑ ^12.0 ^12.1 ^12.2 ^12.3 What is machine learning? Everything you need to know
↑ Best Masters Programs in Machine Learning (ML) for 2020
↑ ^14.0 ^14.1 ^14.2 ^14.3 Applications of Machine learning
↑ ^15.0 ^15.1 ^15.2 ^15.3 Machine learning
↑ ^16.0 ^16.1 ^16.2 ^16.3 Machine Learning Glossary
↑ ^17.0 ^17.1 ^17.2 ^17.3 The Difference Between AI, Machine Learning, and Deep Learning?
↑ Machine Learning
↑ ^19.0 ^19.1 ^19.2 ^19.3 Machine Learning
↑ TensorFlow
↑ ^21.0 ^21.1 Google Cloud
↑ ^22.0 ^22.1 ^22.2 ^22.3 What is Machine Learning?
↑ Machine Learning
↑ Azure Machine Learning
↑ ^25.0 ^25.1 Introduction to Machine Learning Course
↑ ^26.0 ^26.1 What is machine learning?
↑ ^27.0 ^27.1 ^27.2 ^27.3 Learn Machine Learning with Online Courses and Classes
↑ ^28.0 ^28.1 ^28.2 ^28.3 Machine Learning for Everyone
↑ ^29.0 ^29.1 ^29.2 ^29.3 An Introduction to Machine Learning Theory and Its Applications: A Visual Tutorial with Examples
↑ ^30.0 ^30.1 ^30.2 ^30.3 DataRobot Artificial Intelligence Wiki
↑ ^31.0 ^31.1 ^31.2 ^31.3 Machine learning: A cheat sheet
↑ ^32.0 ^32.1 Machine Learning A-Z (Python & R in Data Science Course)
↑ ^33.0 ^33.1 Python Machine Learning
↑ Machine Learning
↑ Journal of Machine Learning Research
↑ Free Online Course: Machine Learning from Coursera
↑ Latest News, Photos & Videos
↑ ^38.0 ^38.1 ^38.2 ^38.3 Machine Learning (ML) vs. Artificial Intelligence (AI) — Crucial Differences
↑ ^39.0 ^39.1 Data Science: Machine Learning

[ref_b8d4-1] 1.0 ^1.1 ^1.2 ^1.3 What Is Machine Learning - A Complete Beginner's Guide

[ref_20d2-2] Introduction to Machine Learning Course

[ref_9a36-3] 3.0 ^3.1 ^3.2 ^3.3 How to Win with Machine Learning

[ref_e67f-4] 4.0 ^4.1 DataRobot Artificial Intelligence Wiki

[ref_92e2-5] 5.0 ^5.1 What is machine learning?

[ref_f584-6] 6.0 ^6.1 ^6.2 ^6.3 What is Machine Learning?

[ref_ca00-7] 7.0 ^7.1 Learn Machine Learning with Online Courses and Classes

[ref_c74f-8] 8.0 ^8.1 ^8.2 ^8.3 Machine learning: A cheat sheet

[ref_3ac7-9] Machine Learning

[ref_18d2-10] 10.0 ^10.1 ^10.2 ^10.3 What is machine learning? Intelligence derived from data

[ref_1df9-11] TensorFlow

[ref_f60c-12] 12.0 ^12.1 Google Cloud

[ref_d9f6-13] 13.0 ^13.1 ^13.2 What is machine learning? Everything you need to know

[ref_638d-14] What is Machine Learning? A definition - Expert System

[ref_3370-15] 15.0 ^15.1 ^15.2 ^15.3 Machine learning

[ref_263c-16] 16.0 ^16.1 ^16.2 ^16.3 Machine Learning Glossary

[ref_88b0-17] Machine Learning - an overview

[ref_b833-18] 18.0 ^18.1 ^18.2 Machine Learning

[ref_175c-19] 19.0 ^19.1 ^19.2 ^19.3 An Introduction to Machine Learning Theory and Its Applications: A Visual Tutorial with Examples

[ref_7927-20] What is Deep Learning?

[ref_4ea3-21] 21.0 ^21.1 What is machine learning?

[ref_45cd-22] 22.0 ^22.1 ^22.2 ^22.3 Machine Learning for Everyone

[ref_9fd4-23] 23.0 ^23.1 ^23.2 ^23.3 What is Machine learning (ML)?

[ref_802cb889-24] 1.0 ^1.1 ^1.2 ^1.3 Top 5 Programming Languages and their Libraries for Machine Learning in 2020

[ref_c417bfce-25] 2.0 ^2.1 ^2.2 ^2.3 What is Machine Learning? A definition - Expert System

[ref_b0912c82-26] 3.0 ^3.1 ^3.2 ^3.3 How to become a machine learning engineer: A cheat sheet

[ref_c495f2ec-27] 4.0 ^4.1 ^4.2 ^4.3 Can machine learning be self-taught?

[ref_eb1c5a86-28] 5.0 ^5.1 What is machine learning?

[ref_84891f0c-29] 6.0 ^6.1 ^6.2 ^6.3 What is Machine Learning?

[ref_18d285db-30] 7.0 ^7.1 ^7.2 ^7.3 What is machine learning? Intelligence derived from data

[ref_5a500f58-31] 8.0 ^8.1 ^8.2 ^8.3 Top 10 real-life examples of Machine Learning

[ref_8ed88b93-32] 9.0 ^9.1 ^9.2 ^9.3 What is Machine learning (ML)?

[ref_d57b6569-33] 10.0 ^10.1 ^10.2 ^10.3 ML Studio (classic): Machine Learning Scoring - Azure

[ref_742e774f-34] 11.0 ^11.1 ^11.2 ^11.3 14 Different Types of Learning in Machine Learning

[ref_d9f64b49-35] 12.0 ^12.1 ^12.2 ^12.3 What is machine learning? Everything you need to know

[ref_58e6771e-36] Best Masters Programs in Machine Learning (ML) for 2020

[ref_0c40f9d2-37] 14.0 ^14.1 ^14.2 ^14.3 Applications of Machine learning

[ref_07a59b21-38] 15.0 ^15.1 ^15.2 ^15.3 Machine learning

[ref_c8219c15-39] 16.0 ^16.1 ^16.2 ^16.3 Machine Learning Glossary

[ref_fad17c96-40] 17.0 ^17.1 ^17.2 ^17.3 The Difference Between AI, Machine Learning, and Deep Learning?

[ref_3ac7b374-41] Machine Learning

[ref_b8333047-42] 19.0 ^19.1 ^19.2 ^19.3 Machine Learning

[ref_1df90e27-43] TensorFlow

[ref_f60c950c-44] 21.0 ^21.1 Google Cloud

[ref_03e6f181-45] 22.0 ^22.1 ^22.2 ^22.3 What is Machine Learning?

[ref_fec60db7-46] Machine Learning

[ref_eb8d6df7-47] Azure Machine Learning

[ref_20d2e3a8-48] 25.0 ^25.1 Introduction to Machine Learning Course

[ref_92e2dd00-49] 26.0 ^26.1 What is machine learning?

[ref_f8f1fce2-50] 27.0 ^27.1 ^27.2 ^27.3 Learn Machine Learning with Online Courses and Classes

[ref_53a69c46-51] 28.0 ^28.1 ^28.2 ^28.3 Machine Learning for Everyone

[ref_175ccf03-52] 29.0 ^29.1 ^29.2 ^29.3 An Introduction to Machine Learning Theory and Its Applications: A Visual Tutorial with Examples

[ref_ae9d3dc9-53] 30.0 ^30.1 ^30.2 ^30.3 DataRobot Artificial Intelligence Wiki

[ref_c74f7a45-54] 31.0 ^31.1 ^31.2 ^31.3 Machine learning: A cheat sheet

[ref_2b67ef51-55] 32.0 ^32.1 Machine Learning A-Z (Python & R in Data Science Course)

[ref_f6cb7a50-56] 33.0 ^33.1 Python Machine Learning

[ref_29ed2b55-57] Machine Learning

[ref_4e9f9969-58] Journal of Machine Learning Research

[ref_56907392-59] Free Online Course: Machine Learning from Coursera

[ref_34f7013d-60] Latest News, Photos & Videos

[ref_343a899c-61] 38.0 ^38.1 ^38.2 ^38.3 Machine Learning (ML) vs. Artificial Intelligence (AI) — Crucial Differences

[ref_5ab14699-62] 39.0 ^39.1 Data Science: Machine Learning

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@@ 145번째 줄: / 145번째 줄: @@
 * Uses of machine learning Today, machine learning is used in a wide range of applications.<ref name="ref_9fd4" />
 * But just because some industries have seen benefits doesn't mean machine learning is without its downsides.<ref name="ref_9fd4" />
+===소스===
+ <references />
+== 노트 ==
+===위키데이터===
+* ID :  [https://www.wikidata.org/wiki/Q2539 Q2539]
+===말뭉치===
+# If you are a newbie in machine learning you may have thought that what programming language should I learn?<ref name="ref_802cb889">[https://www.geeksforgeeks.org/top-5-programming-languages-and-their-libraries-for-machine-learning-in-2020/ Top 5 Programming Languages and their Libraries for Machine Learning in 2020]</ref>
+# Machine Learning is one of the fastest-growing fields which has witnessed an exponential growth in the technical world.<ref name="ref_802cb889" />
+# Python leads all the other languages with more than 60% of machine learning developers are using and prioritising it for development because python is easy to learn.<ref name="ref_802cb889" />
+# This programming language is the “Jack of all the trade” and continues to dominate over in the ML industry also.<ref name="ref_802cb889" />
+# Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.<ref name="ref_c417bfce">[https://www.expert.ai/blog/machine-learning-definition/ What is Machine Learning? A definition - Expert System]</ref>
+# Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events.<ref name="ref_c417bfce" />
+# In contrast, unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled.<ref name="ref_c417bfce" />
+# Reinforcement machine learning algorithms is a learning method that interacts with its environment by producing actions and discovers errors or rewards.<ref name="ref_c417bfce" />
+# To help those interested in the field better understand how to break into a career in machine learning, we compiled the most important details and resources.<ref name="ref_b0912c82">[https://www.techrepublic.com/article/how-to-become-a-machine-learning-engineer-a-cheat-sheet/ How to become a machine learning engineer: A cheat sheet]</ref>
+# In terms of specific positions, 94% of job postings that contained AI or machine learning terminology were for machine learning engineers, the report found.<ref name="ref_b0912c82" />
+# "Software is eating the world and machine learning is eating software," Vitaly Gordon, vice president of data science and software engineering for Salesforce Einstein, told TechRepublic.<ref name="ref_b0912c82" />
+# "Machine learning engineering is a discipline that requires production grade coding, PhD level machine learning, and the business acumen of a product manager.<ref name="ref_b0912c82" />
+# So, can machine learning be self-taught?<ref name="ref_c495f2ec">[https://mlcorner.com/can-machine-learning-be-self-taught/ Can machine learning be self-taught?]</ref>
+# Even though there are many different skills to learn in machine learning it is possible for you to self-teach yourself machine learning.<ref name="ref_c495f2ec" />
+# There are actually so many machine learning courses available now that choosing the right path for you can be quite daunting.<ref name="ref_c495f2ec" />
+# course is that it will give you the chance to see if machine learning is something that you are actually interested in.<ref name="ref_c495f2ec" />
+# which statuses you are reacting to—and using machine learning to make a highly educated guess about what you might want next.<ref name="ref_eb1c5a86">[https://www.technologyreview.com/2018/11/17/103781/what-is-machine-learning-we-drew-you-another-flowchart/ What is machine learning?]</ref>
+# Deep learning is machine learning on steroids: it uses a technique that gives machines an enhanced ability to find—and amplify—even the smallest patterns.<ref name="ref_eb1c5a86" />
+# In machine learning, algorithms are 'trained' to find patterns and features in massive amounts of data in order to make decisions and predictions based on new data.<ref name="ref_84891f0c">[https://www.ibm.com/cloud/learn/machine-learning What is Machine Learning?]</ref>
+# There are four basic steps for building a machine learning application (or model).<ref name="ref_84891f0c" />
+# Training data is a data set representative of the data the machine learning model will ingest to solve the problem it’s designed to solve.<ref name="ref_84891f0c" />
+# Supervised machine learning trains itself on a labeled data set.<ref name="ref_84891f0c" />
+# Machine learning is a branch of artificial intelligence that includes methods, or algorithms, for automatically creating models from data.<ref name="ref_18d285db">[https://www.infoworld.com/article/3214424/what-is-machine-learning-intelligence-derived-from-data.html What is machine learning? Intelligence derived from data]</ref>
+# Unlike a system that performs a task by following explicit rules, a machine learning system learns from experience.<ref name="ref_18d285db" />
+# We hear about applications of machine learning on a daily basis, although not all of them are unalloyed successes.<ref name="ref_18d285db" />
+# Game-playing machine learning is strongly successful for checkers, chess, shogi, and Go, having beaten human world champions.<ref name="ref_18d285db" />
+# Machine learning is one modern innovation that has helped man enhance not only many industrial and professional processes but also advances everyday living.<ref name="ref_5a500f58">[https://bigdata-madesimple.com/top-10-real-life-examples-of-machine-learning/ Top 10 real-life examples of Machine Learning]</ref>
+# But what is machine learning?<ref name="ref_5a500f58" />
+# Currently, machine learning has been used in multiple fields and industries.<ref name="ref_5a500f58" />
+# The intelligent systems built on machine learning algorithms have the capability to learn from past experience or historical data.<ref name="ref_5a500f58" />
+# Machine 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.<ref name="ref_8ed88b93">[https://searchenterpriseai.techtarget.com/definition/machine-learning-ML What is Machine learning (ML)?]</ref>
+# Types of machine learning Classical machine learning is often categorized by how an algorithm learns to become more accurate in its predictions.<ref name="ref_8ed88b93" />
+# this type of machine learning, data scientists supply algorithms with training data and define the variables they want the algorithm to assess for correlations.<ref name="ref_8ed88b93" />
+# This type of machine learning involves algorithms that train on unlabeled data.<ref name="ref_8ed88b93" />
+# Similar drag and drop modules have been added to Azure Machine Learning designer.<ref name="ref_d57b6569">[https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/machine-learning-score ML Studio (classic): Machine Learning Scoring - Azure]</ref>
+# Scoring is also called prediction, and is the process of generating values based on a trained machine learning model, given some new input data.<ref name="ref_d57b6569" />
+# Machine Learning Studio (classic) supports a flexible, customizable framework for machine learning.<ref name="ref_d57b6569" />
+# In this phase of machine learning, you apply a trained model to new data, to generate predictions.<ref name="ref_d57b6569" />
+# Some machine learning algorithms are described as “supervised” machine learning algorithms as they are designed for supervised machine learning problems.<ref name="ref_742e774f">[https://machinelearningmastery.com/types-of-learning-in-machine-learning/ 14 Different Types of Learning in Machine Learning]</ref>
+# Some machine learning algorithms do not just experience a fixed dataset.<ref name="ref_742e774f" />
+# Fitting a machine learning model is a process of induction.<ref name="ref_742e774f" />
+# In the context of machine learning, once we use induction to fit a model on a training dataset, the model can be used to make predictions.<ref name="ref_742e774f" />
+# A very important group of algorithms for both supervised and unsupervised machine learning are neural networks.<ref name="ref_d9f64b49">[https://www.zdnet.com/article/what-is-machine-learning-everything-you-need-to-know/ What is machine learning? Everything you need to know]</ref>
+# For instance, consider the example of using machine learning to recognize handwritten numbers between 0 and 9.<ref name="ref_d9f64b49" />
+# As the use of machine learning has taken off, so companies are now creating specialized hardware tailored to running and training machine-learning models.<ref name="ref_d9f64b49" />
+# In 2020, Google said its fourth-generation TPUs were 2.7 times faster than previous gen TPUs in MLPerf, a benchmark which measures how fast a system can carry out inference using a trained ML model.<ref name="ref_d9f64b49" />
+# 📚 Check out our editorial recommendations on the best machine learning books.<ref name="ref_58e6771e">[https://medium.com/towards-artificial-intelligence/top-universities-to-pursue-a-masters-in-machine-learning-ml-in-the-us-ai-d4a461229fbb Best Masters Programs in Machine Learning (ML) for 2020]</ref>
+# next → ← prev Applications of Machine learning Machine learning is a buzzword for today's technology, and it is growing very rapidly day by day.<ref name="ref_0c40f9d2">[https://www.javatpoint.com/applications-of-machine-learning Applications of Machine learning]</ref>
+# We are using machine learning in our daily life even without knowing it such as Google Maps, Google assistant, Alexa, etc.<ref name="ref_0c40f9d2" />
+# Below are some most trending real-world applications of Machine Learning: 1.<ref name="ref_0c40f9d2" />
+# While using Google, we get an option of "Search by voice," it comes under speech recognition, and it's a popular application of machine learning.<ref name="ref_0c40f9d2" />
+# A subset of machine learning is closely related to computational statistics, which focuses on making predictions using computers; but not all machine learning is statistical learning.<ref name="ref_07a59b21">[https://en.wikipedia.org/wiki/Machine_learning Machine learning]</ref>
+# The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning.<ref name="ref_07a59b21" />
+# Machine learning involves computers discovering how they can perform tasks without being explicitly programmed to do so.<ref name="ref_07a59b21" />
+# The discipline of machine learning employs various approaches to teach computers to accomplish tasks where no fully satisfactory algorithm is available.<ref name="ref_07a59b21" />
+# Note: Unfortunately, as of April 2019 we no longer update non-English versions of Machine Learning Crash Course.<ref name="ref_c8219c15">[https://developers.google.com/machine-learning/glossary Machine Learning Glossary]</ref>
+# Bias (also known as the bias term) is referred to as b or w 0 in machine learning models.<ref name="ref_c8219c15" />
+# A type of machine learning model for distinguishing among two or more discrete classes.<ref name="ref_c8219c15" />
+# Machine learning developers may inadvertently collect or label data in ways that influence an outcome supporting their existing beliefs.<ref name="ref_c8219c15" />
+# Machine learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world.<ref name="ref_fad17c96">[https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/ The Difference Between AI, Machine Learning, and Deep Learning?]</ref>
+# Machine learning came directly from minds of the early AI crowd, and the algorithmic approaches over the years included decision tree learning, inductive logic programming.<ref name="ref_fad17c96" />
+# As it turned out, one of the very best application areas for machine learning for many years was computer vision, though it still required a great deal of hand-coding to get the job done.<ref name="ref_fad17c96" />
+# Deep learning has enabled many practical applications of machine learning and by extension the overall field of AI.<ref name="ref_fad17c96" />
+# ML is one of the most exciting technologies that one would have ever come across.<ref name="ref_3ac7b374">[https://www.geeksforgeeks.org/machine-learning/ Machine Learning]</ref>
+# Machine learning is the concept that a computer program can learn and adapt to new data without human intervention.<ref name="ref_b8333047">[https://www.investopedia.com/terms/m/machine-learning.asp Machine Learning]</ref>
+# The various data applications of machine learning are formed through a complex algorithm or source code built into the machine or computer.<ref name="ref_b8333047" />
+# Machine learning is used in different sectors for various reasons.<ref name="ref_b8333047" />
+# Lending institutions can incorporate machine learning to predict bad loans and build a credit risk model.<ref name="ref_b8333047" />
+# A diverse community of developers, enterprises and researchers are using ML to solve challenging, real-world problems.<ref name="ref_1df90e27">[https://www.tensorflow.org/ TensorFlow]</ref>
+# Natural Language Derive insights from unstructured text using Google machine learning.<ref name="ref_f60c950c">[https://cloud.google.com/products/ai Google Cloud]</ref>
+# Translation Dynamically translate between languages using Google machine learning.<ref name="ref_f60c950c" />
+# At Emerj, the AI Research and Advisory Company, many of our enterprise clients feel as though they should be investing in machine learning projects, but they don’t have a strong grasp of what it is.<ref name="ref_03e6f181">[https://emerj.com/ai-glossary-terms/what-is-machine-learning/ What is Machine Learning?]</ref>
+# The above definition encapsulates the ideal objective or ultimate aim of machine learning, as expressed by many researchers in the field.<ref name="ref_03e6f181" />
+# The purpose of this article is to provide a business-minded reader with expert perspective on how machine learning is defined, and how it works.<ref name="ref_03e6f181" />
+# As with any concept, machine learning may have a slightly different definition, depending on whom you ask.<ref name="ref_03e6f181" />
+# Machine Learning is an international forum for research on computational approaches to learning.<ref name="ref_fec60db7">[https://www.springer.com/journal/10994 Machine Learning]</ref>
+# Rapidly build and deploy machine learning models using tools that meet your needs regardless of skill level.<ref name="ref_eb8d6df7">[https://azure.microsoft.com/en-us/services/machine-learning/ Azure Machine Learning]</ref>
+# Machine learning brings together computer science and statistics to harness that predictive power.<ref name="ref_20d2e3a8">[https://www.udacity.com/course/intro-to-machine-learning--ud120 Introduction to Machine Learning Course]</ref>
+# This is a class that will teach you the end-to-end process of investigating data through a machine learning lens.<ref name="ref_20d2e3a8" />
+# Machine learning could also be used for security applications, such as analysing email communications or internet usage.<ref name="ref_92e2dd00">[https://royalsociety.org/topics-policy/projects/machine-learning/videos-and-background-information/ What is machine learning?]</ref>
+# The project will focus on current and near-term (5-10 years) applications of machine learning.<ref name="ref_92e2dd00" />
+# Machine Learning can play a pivotal role in a range of applications such as Deep Learning, Reinforcement Learning, Natural Language Processing, etc.<ref name="ref_f8f1fce2">[https://www.edx.org/learn/machine-learning Learn Machine Learning with Online Courses and Classes]</ref>
+# Microsoft, Columbia, Caltech and other major universities and institutions offer introductory courses and tutorials in machine learning and artificial intelligence.<ref name="ref_f8f1fce2" />
+# Gain a stronger understanding of the major machine learning projects with helpful examples.<ref name="ref_f8f1fce2" />
+# Funding for research and development in the fields of machine learning and artificial intelligence is growing at a rapid pace.<ref name="ref_f8f1fce2" />
+# In machine learning terms, Billy invented regression – he predicted a value (price) based on known historical data.<ref name="ref_53a69c46">[https://vas3k.com/blog/machine_learning/ Machine Learning for Everyone]</ref>
+# Three components of machine learning Without all the AI-bullshit, the only goal of machine learning is to predict results based on incoming data.<ref name="ref_53a69c46" />
+# All ML tasks can be represented this way, or it's not an ML problem from the beginning.<ref name="ref_53a69c46" />
+# That's why selecting the right features usually takes way longer than all the other ML parts.<ref name="ref_53a69c46" />
+# The supply of able ML designers has yet to catch up to this demand.<ref name="ref_175ccf03">[https://www.toptal.com/machine-learning/machine-learning-theory-an-introductory-primer An Introduction to Machine Learning Theory and Its Applications: A Visual Tutorial with Examples]</ref>
+# Determining which inputs to use is an important part of ML design.<ref name="ref_175ccf03" />
+# We stick to simple problems in this post for the sake of illustration, but the reason ML exists is because, in the real world, the problems are much more complex.<ref name="ref_175ccf03" />
+# On this flat screen we can draw you a picture of, at most, a three-dimensional data set, but ML problems commonly deal with data with millions of dimensions, and very complex predictor functions.<ref name="ref_175ccf03" />
+# Machine learning is a subset of artificial intelligence (AI) in which algorithms learn by example from historical data to predict outcomes and uncover patterns not easily spotted by humans.<ref name="ref_ae9d3dc9">[https://www.datarobot.com/wiki/machine-learning/ DataRobot Artificial Intelligence Wiki]</ref>
+# For example, machine learning can reveal customers who are likely to churn, likely fraudulent insurance claims, and more.<ref name="ref_ae9d3dc9" />
+# Machine learning has practical implications across industry sectors, including healthcare, insurance, energy, marketing, manufacturing, financial technology (fintech), and more.<ref name="ref_ae9d3dc9" />
+# While most statistical analysis relies on rule-based decision-making, machine learning excels at tasks that are hard to define with exact step-by-step rules.<ref name="ref_ae9d3dc9" />
+# From Apple to Google to Toyota, companies across the world are pouring resources into developing AI systems with machine learning.<ref name="ref_c74f7a45">[https://www.techrepublic.com/article/machine-learning-the-smart-persons-guide/ Machine learning: A cheat sheet]</ref>
+# While there are different forms of AI, machine learning (ML) represents today's most widely valued mechanism for reaching intelligence.<ref name="ref_c74f7a45" />
+# What is machine learning?<ref name="ref_c74f7a45" />
+# Machine learning became popular in the 1990s, and returned to the public eye when Google's DeepMind beat the world champion of Go in 2016.<ref name="ref_c74f7a45" />
+# We will walk you step-by-step into the World of Machine Learning.<ref name="ref_2b67ef51">[https://www.udemy.com/course/machinelearning/ Machine Learning A-Z (Python & R in Data Science Course)]</ref>
+# This course is fun and exciting, but at the same time, we dive deep into Machine Learning.<ref name="ref_2b67ef51" />
+# Machine Learning is making the computer learn from studying data and statistics.<ref name="ref_f6cb7a50">[https://www.w3schools.com/python/python_ml_getting_started.asp Python Machine Learning]</ref>
+# In Machine Learning it is common to work with very large data sets.<ref name="ref_f6cb7a50" />
+# Create ML lets you quickly build and train Core ML models right on your Mac with no code.<ref name="ref_29ed2b55">[https://developer.apple.com/machine-learning/ Machine Learning]</ref>
+# The Journal of Machine Learning Research (JMLR) provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning.<ref name="ref_4e9f9969">[https://www.jmlr.org/ Journal of Machine Learning Research]</ref>
+# -Support vector machines, or SVMs, is a machine learning algorithm for classification.<ref name="ref_56907392">[https://www.classcentral.com/course/machine-learning-835 Free Online Course: Machine Learning from Coursera]</ref>
+# Machine learning is speeding it up by orders of magnitude.<ref name="ref_34f7013d">[https://www.wired.com/tag/machine-learning/ Latest News, Photos & Videos]</ref>
+# For instance, if you provide a machine learning model with many songs that you enjoy, along with their corresponding audio statistics (dance-ability, instrumentality, tempo, or genre).<ref name="ref_343a899c">[https://towardsai.net/p/machine-learning/differences-between-ai-and-machine-learning-1255b182fc6 Machine Learning (ML) vs. Artificial Intelligence (AI) — Crucial Differences]</ref>
+# The machine learning model looks at each picture in the diverse dataset and finds common patterns found in pictures with labels with comparable indications.<ref name="ref_343a899c" />
+# Unsupervised learning, another type of machine learning, is the family of machine learning algorithms, which have main uses in pattern detection and descriptive modeling.<ref name="ref_343a899c" />
+# Machine learning can be dazzling, particularly its advanced sub-branches, i.e., deep learning and the various types of neural networks.<ref name="ref_343a899c" />
+# Perhaps the most popular data science methodologies come from machine learning.<ref name="ref_5ab14699">[https://online-learning.harvard.edu/course/data-science-machine-learning Data Science: Machine Learning]</ref>
+# What distinguishes machine learning from other computer guided decision processes is that it builds prediction algorithms using data.<ref name="ref_5ab14699" />
 ===소스===
   <references />

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