"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 algorithms, statistical models, and computational systems that enable computers to learn from and improve with experience and perform tasks that require human-like intelligence.
Probability and Statistics: Understanding probability and statistics concepts is essential for machine learning as data is all about probability and using statistical methods to draw inferences.
Linear Algebra: Linear algebra helps in understanding the mathematical basis of a wide range of machine learning algorithms such as regression, support vector machines, and neural networks.
Calculus: Calculus helps you understand the functions and relationships between data and algorithms. It helps in optimization and finding the best-fit line or curve for your data.
Optimization: Optimization techniques are used to minimize the error or cost function used in most machine learning algorithms.
Data preprocessing: Data preprocessing steps include cleaning, normalization, dimensionality reduction, and feature extraction.
Machine learning algorithms: Understanding the basic concepts of machine learning algorithms such as supervised and unsupervised learning, regression, classification, clustering, and reinforcement learning is essential.
Deep learning: Deep learning algorithms are a subset of machine learning and involve artificial neural networks with multiple layers of computation.
Natural Language Processing: Natural Language Processing (NLP) is a branch of AI that deals with human language and includes text classification, sentiment analysis, and machine translation.
Computer Vision: Computer Vision (CV) is a subset of AI that focuses on image and video recognition and includes object detection, facial recognition, and image segmentation.
Reinforcement Learning: Reinforcement learning is a type of machine learning in which the algorithms act in an environment and learn from their actions and rewards.
Tensorflow: TensorFlow is an open-source machine learning framework widely used for building and training neural networks.
Keras: Keras is a high-level neural network API that simplifies the process of building deep learning models.
PyTorch: PyTorch is an open-source machine learning library popular for its dynamic computational graph and ease of use.
Scikit-learn: Scikit-learn is a popular machine learning library for Python that includes a range of supervised and unsupervised learning algorithms.
Data visualization: Data visualization techniques are used to present data in a clear and understandable manner.
Cloud computing: Cloud computing services such as Amazon Web Services (AWS) and Google Cloud Platform (GCP) allow for large-scale data processing and machine learning.
Ethics of AI: There is a growing need for understanding the ethical implications of AI systems and their impact on society, privacy, and inequality.
Neural Network Architectures: Understanding the architecture of neural networks and choosing the right one for a specific problem is crucial.
Transfer Learning: Transfer Learning is a concept that involves using a pre-trained model to solve a new problem in a different domain.
Data Augmentation: Data augmentation techniques are used to artificially increase the size of the dataset by adding noise, flipping, or rotating the images.
Ensemble Learning: Ensemble learning is the use of multiple machine learning models to improve accuracy and reduce variance.
Hyperparameter Optimization: Choosing the right values for hyperparameters is important to maximize the performance of machine learning algorithms.
Time Series Analysis: Time series analysis involves analyzing data over time and is used in applications such as forecasting, trend analysis, and anomaly detection.
Anomaly Detection: Anomaly detection is the process of identifying unusual data points that do not conform to expected patterns.
Autoencoders: Autoencoders are neural networks that can learn to compress information and reconstruct it with minimal loss.
Convolutional Neural Networks (CNNs): CNNs are a type of neural network used for image recognition and classification.
Generative Adversarial Networks (GANs): GANs are a type of unsupervised learning that involves training two neural networks to compete against each other.
Recurrent Neural Networks (RNNs): RNNs are a type of neural network used for sequential data such as time series or text.
Support Vector Machines (SVMs): SVMs are a type of machine learning algorithm used for classification and regression.
Decision Trees: Decision trees are a type of algorithm used for classification and regression. They are easy to understand and interpret.
Supervised Learning: In supervised learning, an algorithm is trained on labeled data. The labeled data contains both input and output data so that the algorithm can learn how to make predictions for new data.
Unsupervised Learning: In unsupervised learning, the algorithm is trained on unlabeled data, and its goal is to find patterns and relationships in the data.
Semi-Supervised Learning: Semi-Supervised Learning is a hybrid of supervised and unsupervised learning where the algorithm is fed both labeled and unlabeled data.
Reinforcement Learning: Reinforcement learning is a type of machine learning algorithm that is used to train an agent to make decisions by interacting with an environment.
Deep Learning: Deep learning is a subset of machine learning where artificial neural networks learn from vast amounts of data.
Natural Language Processing (NLP): NLP is an area of AI that focuses on the interaction between computers and humans using natural language.
Computer Vision: Computer vision is an area of AI that focuses on enabling machines to see, interpret, and understand their surroundings.
Robotics: Robotics involves developing machines that can perform tasks without human intervention through programming and physical abilities.
Decision Trees: A decision tree maps decisions and their possible consequences, including chance events.
Bayesian Networks: Bayesian networks are probabilistic graphical models that are used to represent uncertain knowledge.
Clustering: Clustering is the grouping of similar data points into a single cluster, while dissimilar points are placed in separate clusters.
Regression: Regression analyses the relationship between one dependent variable and one or more independent variables.
Ensemble Learning: Ensemble Learning combines multiple algorithms to improve overall performance.
Fuzzy Logic: Fuzzy Logic is a mathematical technique for dealing with uncertain or vague information.
Genetic Algorithms: Genetic algorithms use the principle of natural selection to generate a solution to a problem.
Support Vector Machines: SVMs are used to classify data by finding the hyperplane that best separates the training data in the feature space.
"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."