Classification and tracking can mostly used in dynamic environments. Digital image processing field focuses on embedded systems incorporates with the use of industrial imaging camera to perform real time classification.
Clustering is a type of unsupervised learning method in which we set references from datasets consisting of input data without responses. Its main task is to divide the population or large data into number of groups. It is basically a collection of objects on the basis of similarity and dissimilarity between them.
Data mining is a process which includes extract, transform and load data into warehouse. It also store and manage data in multidimensional databases. Data mining is gathering data critical for business either transactional, non-operational. It handles large amount of data in the databases which stores the information of goods , inventory etc.
Ensemble models in machine learning combines the decisions from multiple models to improve the performance. Ensemble methods are designed to minimize the noise, bias and variance to increase the stability and the accuracy of machine learning algorithms. Boosting in ensemble methods is an iterative technique which adjusts the weight of an observation based on the last classification. If an observation was misclassified, it tries to increase the weight of observation and vice versa.
Learning Theory works on main three essentials to understand the concept algorithms, theory and applications. Learning theory is fastest growing area and it covers mathematics and techniques that power machine learning. Learning theory with a mix of statistics, probability, information theory and optimization.
Neural Networks are algorithms, modeled just like a human brain and that are designed to recognize patterns on the basis of predefined training. The patterns they recognize are matrix form contained in vectors, into which all real world data can be images, sound or text. It helps us to group unlabelled data according to features matches among the inputs.
Optimization techniques help us to minimize or maximize the error function which dependent function on the model internal learnable parameters which are used in computing the target values from the set of inputs.
Regression is a set of statistical process for estimating the relationship among the input and output variables. It includes many techniques for analyzing dependent and independent variables. Regression analysis is widely used for prediction and forecasting.
Deep Learning is a part of machine learning methods based on layers used in ANN. Learning can be supervised or unsupervised. Deep learning architectures such as Convolution neural network have been applied to fields including computer vision, speech processing. Neural network tends to be static and symbolic.