"Machine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive."
The study of how machines can learn and apply intelligence to accomplish tasks.
Linear Algebra: Linear algebra is a branch of mathematics that is used to represent and solve systems of linear equations, which form the basis of many machine learning algorithms.
Calculus: Calculus is a branch of mathematics that deals with continuous change and is used in optimization methods, such as gradient descent, which is used in machine learning.
Statistics: Statistics is a branch of mathematics that deals with the analysis of data, including the methods used to collect, analyze, interpret and present data.
Probability: Probability is a branch of mathematics that deals with the study of random events and their outcomes, which is essential for understanding uncertainty in machine learning algorithms.
Data Structures and Algorithms: Data structures and algorithms are used to store and process large data sets, which are a critical part of machine learning.
Python Programming Language: Python is a popular programming language for data science and machine learning because of its simplicity, ease of use, and widespread support.
Deep Learning: Deep learning is a subset of machine learning that involves the use of neural networks with multiple layers, which can learn complex models from large data sets.
Natural Language Processing: Natural language processing is a field of computer science that deals with the interaction between humans and computers using natural language.
Computer Vision: Computer vision is a field of computer science that deals with enabling computers to extract useful information from visual data sets.
Supervised Learning: Supervised learning is a type of machine learning that involves training a model on labeled data to predict new, unseen data accurately.
Unsupervised Learning: Unsupervised learning is a type of machine learning that involves finding patterns in unlabeled data.
Reinforcement Learning: Reinforcement learning is a type of machine learning that involves training a model to make decisions based on feedback from the environment.
Data Visualization: Data visualization is an essential part of machine learning, as it helps to understand and communicate complex data sets in a meaningful and intuitive way.
Big Data: Big data refers to data sets that are too large and complex to be processed by traditional data processing systems and require specialized tools and techniques to analyze.
Cloud Computing: Cloud computing provides access to computing resources and storage over the internet, which is essential for handling large data sets and running complex machine learning algorithms.
Ethics and Bias in AI: Ethics and bias are essential considerations in AI and machine learning systems, as they can have considerable effects on individuals and society as a whole.
Supervised Learning: This type of machine learning is used for training algorithms with labeled data. The system trains on a set of inputs and the corresponding outputs, and is then able to make predictions on new data.
Unsupervised Learning: In this type of machine learning, an AI algorithm tries to identify patterns in unlabeled data. The system groups similar examples together and discovers underlying patterns.
Reinforcement Learning: This type involves training an algorithm by having it learn through trial and error. The system makes a series of decisions and receives feedback in real-time to improve its decision-making ability.
Deep Learning: A subset of machine learning, deep learning involves using artificial neural networks with multiple layers to perform complex tasks like image and speech recognition.
Natural Language Processing: A technique used to enable machines to understand, interpret and generate human language. It allows for chatbots, voice assistants to be developed.
Computer Vision: A subset of AI, used to process and analyze digital images and videos to enable machines to "see" and recognize objects, faces, and visual patterns.
Fuzzy Logic: Fuzzy Logic is used for uncertainty reasoning which provides output, i.e., the degree of a specific result in between 0 to 1.
Support Vector Machines: SVM is to classify data points into pre-defined classes. In other words, model data in a way that the categories are distinctly divided.
Decision Trees: Used for predictive modeling tasks. Decision trees are used to identify the patterns recursively and make decisions in a hierarchical model.
Genetic Algorithms: It is a problem-solving algorithm which is based on the evolution of artificial population. The classes which have old error rates are removed based on specific rules and the new class is added. The aim of the algorithm is to reduce the classification error and increase its efficiency.
Ensemble Methods: The technique that combines several machine learning algorithms to obtain better predictive performance than could be obtained from any single method alone.
Bayesian Networks: It is a probabilistic graphical model and is used to define probabilistic relationships among variables. It is used to correctly identify the cause-effect relationship among variables.
Expert System: An expert system is used to capture knowledge from human experts and can make decisions in the same way an expert in the given domain would.
Case-Based Reasoning: This technique uses past data or events as a template for solving new problems.
Machine Learning as a Service (MLaaS): A service provided by cloud providers for businesses that intend to leverage the benefits of machine learning without investing in hardware, software or in-house expertise.
Transfer Learning: Used to annotate images with limited data, transfer learning is used to teach a model on a similar task and then use this knowledge to improve performance on a new task.
Deep Reinforcement Learning: Enhancing reinforcement learning, deep reinforcement learning combines the benefits of deep learning in making quick decisions with reinforcement learning's decision-making ability.
Time Series Analysis: This technique is used to identify patterns in time-series data to understand trends, patterns, and to forecast future events.
Anomaly Detection: This technique is used to identify unusual patterns in data that do not conform to expectations – these patterns may be fraudulent, defective or exceptional.
Self-Supervised Learning: In self-supervised learning, the algorithm trains on an unlabeled dataset and tries to extract information on its own by making predictions on certain characteristics of the data.
Few-Shot Learning: Few-shot learning refers to a technique that enables AI algorithms to learn and identify items from just a few examples.
Active Learning: This technique involves having a machine learning algorithm select portions of training data to be labeled by an expert.
Multi-Agent Systems: Multi-Agent Systems refer to the development of multiple AI agents to work in tandem in completing one or more complicated tasks.
Quantum Machine Learning: Quantum machine learning combines classical algorithms with quantum computer architecture, allowing for more advanced computation in a shorter amount of time.
Synthetic Data: Synthetic data is artificially created data that is based off real-world trends. It is often used when there is an insufficient amount of data to train machine learning models.
AutoML: AutoML is a machine learning technique that seeks to automate the machine learning process from training through deployment, easing the entry barrier.
GANs: Generative Adversarial Networks (GANs) are a type of deep learning model that learns to generate synthetic data by pitting two neural networks against each other.
"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."