"Machine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive."
The process of teaching computers to learn from data using various algorithms and statistical models.
Linear algebra: A branch of mathematics that deals with linear equations and matrix operations. This is a fundamental topic for machine learning as most algorithms use linear algebra for matrix manipulations.
Calculus: A branch of mathematics that deals with the study of rates of change and their applications to problem-solving. Calculus is important for understanding optimization algorithms used in machine learning.
Probability and statistics: A branch of mathematics that deals with the study of data and probability theory. Probability and statistics are crucial for understanding the likelihood of events and the distribution of data.
Data preprocessing: The process of cleaning, transforming, and preparing data for analysis. This includes tasks such as data normalization, data cleaning, and feature engineering.
Supervised learning: A machine learning technique where the algorithm learns from labeled data with a pre-specified output. This includes algorithms like linear regression, logistic regression, and neural networks.
Unsupervised learning: A machine learning technique where the algorithm learns from unlabeled data without a pre-specified output. This includes algorithms like clustering, dimensionality reduction, and anomaly detection.
Deep learning: A subset of machine learning that involves artificial neural networks capable of complex computations. This includes algorithms such as convolutional neural networks and recurrent neural networks.
Convolutional neural networks: A type of neural network commonly used in computer vision tasks such as image recognition and classification.
Recurrent neural networks: A type of neural network commonly used in natural language processing and speech recognition tasks.
Tensorflow: A powerful open-source software library for developing and training machine learning models.
Keras: A high-level neural network library written in Python that provides an interface for building and training models.
PyTorch: A popular open-source machine learning library written in Python that provides a flexible deep learning platform.
Natural language processing: A branch of machine learning that deals with the manipulation and interpretation of human language. This includes tasks such as language translation, sentiment analysis, and speech recognition.
Computer vision: A field of study that deals with how computers can be made to perceive, interpret, and understand visual information from the world around them.
Object detection: A computer vision technique used to locate and identify objects within an image or video stream.
Image segmentation: A computer vision technique used to separate or partition an image into multiple segments or regions.
Reinforcement learning: A machine learning technique where the algorithm learns through trial and error in an interactive environment. This includes algorithms like Q-learning and deep reinforcement learning.
Optimization algorithms: Mathematical techniques used to minimize errors and maximize accuracy in machine learning models. This includes algorithms like gradient descent and stochastic gradient descent.
Supervised Learning: In this type of machine learning, the model learns by having a pre-labeled training data set. The model predicts the output for input using the training data.
Unsupervised Learning: In this type of machine learning, the model learns without the need for pre-labeled training data. The algorithm finds patterns and structures in the data on its own.
Reinforcement Learning: In this type of machine learning, the model learns by receiving feedback or reward signals based on its actions in a particular environment.
Semi-Supervised Learning: In this type of machine learning, the model learns using a combination of labeled and unlabeled data.
Deep Learning: This type of machine learning is based on artificial neural networks with multiple hidden layers. It is used for complex tasks such as image or voice recognition.
Convolutional Neural Networks (CNN): This type of machine learning is used for tasks involving images and videos. It takes in image data and is able to identify objects within the image.
Recurrent Neural Networks (RNN): This type of machine learning is used for tasks that involve sequential data such as stock prices, text, and music.
Generative Adversarial Networks (GAN): This type of machine learning is used for generating synthetic data for tasks such as image and video creation.
Transfer Learning: This type of machine learning is used when the model trained on one task is used for a different but related task.
One-Shot Learning: In this type of machine learning, the model learns to recognize a new class with only one example of that class.
Online Learning: In this type of machine learning, the model learns continuously as new data is added to the system.
Multi-Task Learning: In this type of machine learning, the model learns to perform multiple tasks simultaneously.
Ensemble Learning: This type of machine learning involves combining multiple models to improve accuracy and performance.
"the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms."
"Recently, generative artificial neural networks have been able to surpass results of many previous approaches."
"Machine-learning approaches have been applied to large language models, computer vision, speech recognition, email filtering, agriculture and medicine."
"where it is too costly to develop algorithms to perform the needed tasks."
"The mathematical foundations of ML are provided by mathematical optimization (mathematical programming) methods."
"Data mining is a related (parallel) field of study, focusing on exploratory data analysis through unsupervised learning."
"ML is known in its application across business problems under the name predictive analytics."
"Although not all machine learning is statistically based, computational statistics is an important source of the field's methods."
"the problems are solved by helping machines 'discover' their 'own' algorithms without needing to be explicitly told what to do by any human-developed algorithms."
"Machine-learning approaches have been applied to large language models, computer vision, speech recognition, email filtering, agriculture and medicine."
"development of algorithms by human programmers would be cost-prohibitive"
"generative artificial neural networks have been able to surpass results of many previous approaches."
"Data mining is a related (parallel) field of study, focusing on exploratory data analysis through unsupervised learning."
"Machine-learning approaches have been applied to...medicine."
"helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms."
"the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms."
"The mathematical foundations of ML are provided by mathematical optimization (mathematical programming) methods."
"where it is too costly to develop algorithms to perform the needed tasks."
"Although not all machine learning is statistically based, computational statistics is an important source of the field's methods."