"Machine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive."
The branch of artificial intelligence that studies algorithms and statistical models that allow computer systems to improve their performance on a specific task through experience.
Probability and Statistics: Probability theory and statistical analysis are fundamental to machine learning as these aid in making predictions and understanding the uncertainty inherent in the data.
Linear Algebra: Linear Algebra forms the heart of Machine Learning as it is used to model data, compute distances between data points, reduce dimensions, and understand eigenvalues.
Data preprocessing and cleaning: Before starting any machine learning project, it's essential to clean and preprocess the data as real-world datasets are usually messy.
Supervised Learning: Supervised Learning is an approach in which the algorithm is trained on a dataset with labeled features to predict or classify new data.
Regressions: Regression analysis is a statistical technique used for modeling relationships between the target variable and one or more predictor variables.
Unsupervised Learning: Unsupervised Learning is an approach in which the algorithm learns from an unlabeled dataset with no specific output variable.
Clustering: Clustering is a technique used in Unsupervised Learning to group data points into similar clusters based on selected features.
Natural Language Processing: Natural Language Processing (NLP) is the ability of machines to understand and process human language using various techniques like Text Mining, Sentiment Analysis, and Named Entity Recognition.
Neural Networks: Neural Networks are a class of machine learning models inspired by the human brain and can be used for tasks such as image recognition, speech recognition, and natural language processing.
Deep Learning: Deep Learning is an advanced form of Neural Networks that use multiple layers to learn and extract more complex features from the data.
Reinforcement Learning: Reinforcement Learning is an approach in which an agent interacts with an environment to learn optimal actions that maximize a reward function.
Feature extraction: Feature extraction is the process of identifying a subset of relevant features from the dataset to improve the accuracy of the model.
Model evaluation and selection: Model evaluation is the process of measuring how well the model performs on the unseen data. Model selection is the process of choosing the best algorithm among different models.
The Bayesian approach: The Bayesian approach applies Bayes' theorem to compute the likelihood of the hypothesis given the data, and this approach is widely used to make decisions and forecasts in various industries.
Supervised Learning: In supervised learning, a labeled dataset is used to train the machine learning model. The algorithm learns from the pre-defined categories of data and is then able to classify new data into those categories.
Unsupervised Learning: In unsupervised learning, the machine learning algorithm discovers patterns in the data without being given any pre-defined labels. It is used when no prior knowledge of data can be assumed.
Semi-supervised Learning: Semi-supervised learning is a mixture of supervised and unsupervised learning. The algorithm uses labeled and unlabeled data to learn.
Reinforcement Learning: In reinforcement learning, the machine learning algorithm receives feedback in the form of rewards or penalties based on its actions, which help it to learn from mistakes and improve over time.
Deep Learning: Deep Learning, a subset of machine learning, uses neural networks with multiple layers to learn from large, complex data sets.
Convolutional Neural Networks (CNNs): A specific type of deep learning technique used for image, video and voice recognition.
Recurrent Neural Networks (RNNs): A specific type of deep learning technique used for language translation, speech recognition, and time-series data analysis.
Adversarial Learning: Adversarial learning uses two or more neural networks working against each other, trying to outsmart each other to optimize results.
Natural Language Processing (NLP): It is the subset of machine learning that deals with the processing and understanding of human language.
Text analytics: A specific type of natural language processing used to extract insights from text data.
Sentiment analysis: A specific type of text analytics used to determine the emotions or opinions expressed in textual data.
Chatbots: A form of machine learning used to build intelligent conversational agents that can interact with humans.
Speech Recognition: A subset of machine learning and NLP which involves converting spoken words into text.
Optical Character Recognition (OCR): A specific type of machine learning used to recognize and extract text from digital images.
Machine Translation: A type of natural language processing used to automatically translate text from one language to another.
Predictive Analytics & Forecasting: Machine learning (computational linguistics) is used to detect patterns and trends in data, and make predictions about what might happen in the future.
Regression Analysis: An algorithmic approach to identify relationships between the dependent variable and other independent variables.
"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."