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[[Papers]]
[[Papers]]


==Introduction==
==Introduction to Machine Learning==
Machine learning is a branch of [[artificial intelligence]] that deals with the design and development of [[algorithm]]s and [[model]]s that allow computers to learn from [[data]] and make [[prediction]]s or decisions without being explicitly programmed. The aim is to create systems and models which can automatically improve their performance over time based on experience, thus solving real-world problems.
Machine learning is a branch of [[artificial intelligence]] that deals with the design and development of [[algorithm]]s and [[model]]s that allow computers to learn from [[data]] and make [[prediction]]s or decisions without being explicitly programmed. The aim is to create systems and models which can automatically improve their performance over time based on experience, thus solving real-world problems.


==Types of Machine Learning==
==Types of Machine Learning==
Machine learning consists of three primary approaches:
Machine learning consists of three primary approaches:
#Supervised Learning: In supervised learning, an algorithm is trained on a labeled dataset where each input and its associated label are known. The goal is to learn a mapping between input and output so that the model can accurately predict future data without being informed beforehand.
#[[Supervised learning]]: In supervised learning, an algorithm is trained on a [[label]]ed [[dataset]] where each [[input]] and its associated label are known. The goal is to learn a mapping between input and [[output]] so that the model can accurately predict future data without being informed beforehand.
#Unsupervised Learning: Unsupervised learning involves training an algorithm on an unlabeled dataset, where the output or label for each input is unknown. The goal is to uncover patterns or structure within the data by grouping similar examples together.
#[[Unsupervised learning]]: Unsupervised learning involves training an algorithm on an [[unlabeled]] dataset, where the output or [[label]] for each input is unknown. The goal is to uncover patterns or structures within the data by grouping similar [[examples]] together.
#Reinforcement Learning: Reinforcement learning is an approach in which an algorithm learns to make decisions by taking actions in an environment to maximize a reward signal. It receives feedback in the form of rewards or penalties, which it uses to adjust its decision-making strategy accordingly.
#[[Reinforcement learning]]: Reinforcement learning is an approach in which an algorithm learns to make decisions by taking actions in an environment to maximize a reward signal. It receives feedback in the form of rewards or penalties, which it uses to adjust its decision-making strategy accordingly.


==Algorithms and Models==
==Algorithms and Models==
Machine learning involves many algorithms and models, such as linear regression: a straightforward method for predicting continuous output based on input features.
Machine learning involves many algorithms and models:
2. Logistic regression: an approach used to predict binary outcomes from given input features.
 
3. Decision trees: A tree-like model for making decisions or predictions based on input features.
#[[Linear regression]]: a straightforward method for predicting continuous output based on input [[features]].
4. Random forests: An ensemble of decision trees used for both regression and classification tasks.
#[[Logistic regression]]: an approach used to predict [[binary]] outcomes from given input features.
5. Neural networks: Models inspired by the structure and function of the brain, used for various tasks such as image and speech recognition.
#[[Decision tree]]: a tree-like model for making decisions or predictions based on input features.
6. Support vector machines (SVMs): Classification method that finds boundary that best separates different classes in data set.
#[[Random forest]]: an ensemble of decision trees used for both [[regression]] and [[classification]] [[tasks]].
7. K-nearest neighbors (KNN): A classification algorithm that predicts the label of a new data point by looking at its closest neighbors in training data.
#[[Neural network]]: [[models]] inspired by the structure and function of the brain with [[neurons]] and [[hidden layers]], used for various complex tasks.
#[[Support vector machine]] (SVM): Classification method that finds boundary that best separates different [[classes]] in data set.
#[[K-nearest neighbor]] (KNN): A classification algorithm that predicts the label of a new data point by looking at its closest neighbors in [[training data]].


==Applications==
==Applications==
Machine learning has many applications, such as:
Machine learning has many applications, such as:
1. Image recognition and computer vision
#[[Image recognition]] and [[computer vision]]
2. Natural language processing
#[[Natural language processing]]
3. Speech recognition and synthesis
#[[Speech recognition]] and [[speech syntehsis|synthesis]]
4. Recommender systems
#[[Recommendation system]]s
5. Fraud detection
#[[Fraud detection]]
6. Predictive maintenance
#[[Predictive maintenance]]
7. Healthcare 8. Financial services.
#[[Healthcare]]
 
#[[Financial service]]s
==Explain Like I'm 5 (ELI5)==
Machine learning is the process by which computers learn from data without being explicitly instructed what to do. It's like having a robot that can look at pictures of dogs and cats and figure out on its own how to tell them apart without ever seeing previous images. And this kind of intelligent robot could be employed for many different tasks such as comprehending conversations better, suggesting movies you might like, or even helping doctors make better decisions.


==Explain Like I'm 5 (ELI5)==
==Explain Like I'm 5 (ELI5)==
Let's say you have a toy box full of different toys. Every day, you play with some of them and eventually decide which ones are your favorites - much like how computers learn to do things on their own.
Let's say you have a toy box full of different toys. Every day, you play with some of them and eventually decide which ones are your favorites - much like how computers learn to do things on their own.


Imagine you have a computer program trying to figure out which toys you like best. At first, it doesn't know anything and therefore makes guesses based on rules given. However, as more toys are played with and feedback given about what works and doesn't works, the program gets better at anticipating future preferences - this process is known as "machine learning".
Imagine you have a computer program trying to figure out which toys you like best. At first, it doesn't know anything and therefore makes guesses based on the rules given. However, as more toys are played with, and feedback is given about what works and doesn't work, the program gets better at anticipating future preferences. This process is known as "machine learning".


Just as you keep learning and discovering new favorites, your computer program can keep improving at figuring out what appeals to you. That is why machine learning is so powerful - it allows the computer to improve itself without being explicitly programmed for this task.
Just as you keep learning and discovering new favorites, your computer program can keep improving at figuring out what appeals to you. That is why machine learning is so powerful - it allows the computer to improve itself without being explicitly programmed for this task.




[[Category:Terms]] [[Category:Machine learning terms]]
[[Category:Terms]] [[Category:Machine learning terms]] [[Category:not updated]]

Latest revision as of 21:01, 17 March 2023

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See also: Machine learning terms and artificial intelligence

Models

Models

Datasets

Papers

Papers

Introduction to Machine Learning

Machine learning is a branch of artificial intelligence that deals with the design and development of algorithms and models that allow computers to learn from data and make predictions or decisions without being explicitly programmed. The aim is to create systems and models which can automatically improve their performance over time based on experience, thus solving real-world problems.

Types of Machine Learning

Machine learning consists of three primary approaches:

  1. Supervised learning: In supervised learning, an algorithm is trained on a labeled dataset where each input and its associated label are known. The goal is to learn a mapping between input and output so that the model can accurately predict future data without being informed beforehand.
  2. Unsupervised learning: Unsupervised learning involves training an algorithm on an unlabeled dataset, where the output or label for each input is unknown. The goal is to uncover patterns or structures within the data by grouping similar examples together.
  3. Reinforcement learning: Reinforcement learning is an approach in which an algorithm learns to make decisions by taking actions in an environment to maximize a reward signal. It receives feedback in the form of rewards or penalties, which it uses to adjust its decision-making strategy accordingly.

Algorithms and Models

Machine learning involves many algorithms and models:

  1. Linear regression: a straightforward method for predicting continuous output based on input features.
  2. Logistic regression: an approach used to predict binary outcomes from given input features.
  3. Decision tree: a tree-like model for making decisions or predictions based on input features.
  4. Random forest: an ensemble of decision trees used for both regression and classification tasks.
  5. Neural network: models inspired by the structure and function of the brain with neurons and hidden layers, used for various complex tasks.
  6. Support vector machine (SVM): Classification method that finds boundary that best separates different classes in data set.
  7. K-nearest neighbor (KNN): A classification algorithm that predicts the label of a new data point by looking at its closest neighbors in training data.

Applications

Machine learning has many applications, such as:

  1. Image recognition and computer vision
  2. Natural language processing
  3. Speech recognition and synthesis
  4. Recommendation systems
  5. Fraud detection
  6. Predictive maintenance
  7. Healthcare
  8. Financial services

Explain Like I'm 5 (ELI5)

Let's say you have a toy box full of different toys. Every day, you play with some of them and eventually decide which ones are your favorites - much like how computers learn to do things on their own.

Imagine you have a computer program trying to figure out which toys you like best. At first, it doesn't know anything and therefore makes guesses based on the rules given. However, as more toys are played with, and feedback is given about what works and doesn't work, the program gets better at anticipating future preferences. This process is known as "machine learning".

Just as you keep learning and discovering new favorites, your computer program can keep improving at figuring out what appeals to you. That is why machine learning is so powerful - it allows the computer to improve itself without being explicitly programmed for this task.

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