The Ultimate Glossary of Terms About Machine Learning

March 18, 2019
Machine Learning Course


Machine learning is an artificial intelligence application which provides computer systems with the ability to learn on its own and improve with experience without any explicit requirement of additional programming. Machine learning has its focus on developing computer programs whole can access data and utilize the data to learn on its own.

Some of the commonly used terminology used in Machine Learning are as follows:

  • Adam Optimisation

It is an algorithm utilized to train models of deep learning and is an extension of the Stochastic Gradient Descent. In this algorithm, the average is run employing both gradients and using the gradient’s second moments. It is useful for computing the rate of adaptive learning for every parameter.

  • Bootstrapping

It is a form of the sequential process wherein each subsequent model tries to correct the errors in the earlier models. Each model is dependent on its previous model.

  • Clustering

It is a form of unsupervised learning utilized for discovering inherent groupings within a set of data. For instance, a grouping of consumers on the basis of their buying behavior which can be further used to segment the customers. It provides useful data which the companies can exploit to generate more revenues and profits.

  • Dashboard

It is an informative tool which aids in the visual tracking, analysis of data by displaying key indicators, metrics and data points on a single screen in an organized manner. Dashboards are often customizable and can be altered based upon the preference of the user or according to the requirement so of a project.

  • Deep Learning

It is a form of a Machine Learning algorithm which utilized the concepts of the human brain towards facilitation of the modeling of arbitrary functions. It requires a large volume of data, and the flexibility of this algorithm enables multiple outputs of different models at the same time.

  • Early Stopping

It is a technique of avoiding overfitting while training an ML model using iterative methods. Early stoppings are set in such a manner that it halts the performance of improvement on validation sets.

  • Goodness of Fit

It is a model which explains a proper fitment with a set of observations. Its measurements can be summarised into the discrepancies between its observed values with that of the expected values using a certain model.

This Machine Learning Course is a good fir when the errors on the models which are on training data along with the minimum test data. With time, this algorithm learns the errors in a model and corrects the same.

  • Iteration

It is the number of times the parameters of an algorithm is updated during training of a dataset on a model.

  • Market Basket Analysis

It is a popular technique utilized by marketers for identification of the best combination of services and products which are frequently purchased by consumers. It is also known as product association analysis.

  • MIS

Also known as Management Information System, it is a computerized system comprising of software and hardware which serve as the heart of a corporation’s operations. It compiles data from various online and integrated systems, conducts an analysis on the gathered data, and generates reports which enable the management to make informed and educated business decisions.

  • One Shot Learning

This form of machine learning trains the model which a single example. These are generally utilized for product classification.

  • Pattern Recognition

It is a form of machine learning which focuses on recognizing regularities and patterns in data. Some examples of pattern recognition used in many daily applications include face detection, optical character recognition, object detection, facial recognition, classification of objects etc.

  • Range

It is the difference between the lowest and the highest value in a data set.

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