"Machine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive."
This is a subset of artificial intelligence that focuses on the development of algorithms that can learn from and make predictions on data.
Data preprocessing: Preparation and cleaning of data before feeding it to Machine Learning models.
Linear algebra: Concepts like matrices and vectors that are used in Machine Learning algorithms.
Probability and Statistics: Foundations for probabilistic models that are used in Machine Learning.
Regression: A type of supervised learning that models relationships between dependent and independent variables.
Classification: A type of supervised learning that categorizes data into pre-defined classes.
Clustering: A type of unsupervised learning that categorizes data based on their similarities.
Artificial Neural Networks: A model inspired by human neural networks that are used for classification, regression, and clustering.
Deep Learning: Artificial neural networks with multiple hidden layers that allow for more complex patterns to be learned.
Convolutional Neural Networks: Neural Networks specifically used for image recognition tasks.
Recurrent Neural Networks: Neural Networks that handle sequential or time-series data.
Reinforcement Learning: A type of Machine Learning that involves learning through trial and error in an environment with rewards and punishments.
Ensemble methods: The combination of multiple Machine Learning models to improve accuracy and performance.
Model Selection and Evaluation: Methods to measure and compare the performance of different Machine Learning models.
Overfitting and underfitting: Challenges that arise while training Machine Learning models and their remedies.
Regularization: Techniques to prevent overfitting while training ML models.
Bias-variance tradeoff: A tradeoff between modeling complexity and overfitting that affects the performance of Machine Learning models.
Validation techniques: Methods to evaluate the predictive power of a Machine Learning model on unseen data.
Feature Engineering: Selection and creation of relevant features that affect Machine Learning models' performance.
Natural Language Processing: Processing and understanding human language using Machine Learning techniques.
Recommender Systems: Systems that suggest items or products based on user's preferences and historical data.
Time Series Analysis: Analysis of data that is indexed by time to extract meaningful insights.
Dimensionality reduction: Techniques to reduce the number of features in high-dimensional data.
Supervised Learning: In supervised learning, a machine learning algorithm learns from labeled training data to make predictions on unseen data. This type is widely used for classification and regression tasks.
Unsupervised Learning: In unsupervised learning, the algorithm learns from unlabeled data and groups similar data together based on their features or attributes. Clustering is a common application of unsupervised learning.
Semi-Supervised Learning: In semi-supervised learning, a machine learning algorithm uses both labeled and unlabeled data to learn patterns and make predictions.
Reinforcement Learning: Reinforcement learning is a type of machine learning where an agent learns to take certain actions in an environment to maximize a cumulative reward. This type is often used for applications such as game playing and robotics.
Deep Learning: Deep learning is a type of machine learning that uses artificial neural networks with multiple hidden layers to analyze and process large amounts of complex data. It is widely used for image and speech recognition, natural language processing, and other applications.
Transfer Learning: Transfer learning is a type of machine learning that involves using knowledge gained from one task to improve the performance of a related task. It has practical applications in image recognition, object detection, and natural language processing.
Bayesian Learning: Bayesian learning is a type of statistical inference that uses Baye's theorem to update probability estimates based on new data. It is used for various applications such as spam filtering and medical diagnosis.
Ensemble Learning: Ensemble learning involves combining multiple models to achieve better accuracy and performance. Popular ensemble methods include bagging, boosting, and stacking.
Online Learning: Online learning involves making predictions based on continuously streaming data. It is used for applications such as real-time fraud detection and stock market forecasting.
Instance-Based Learning: Instance-based learning involves using similarity measures to make predictions based on past experiences. It is used for applications such as recommender systems and personalized marketing.
"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."