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Data Science Glossary- Machine Learning Tools and Terminologies

This post presents a collection of Data Science related key terms with concise definitions. It is a known fact that familiarising with data science terminologies is time-consuming, as these words are not part of the routine. However, once you start studying and hearing about these terminologies, you will comprehend the importance of these terms in data science and eventually be interested to know more.  I, in this article, presenting a bunch of key data science terminologies, grouped into various categories. Let’s now study these categories the terminologies in them, one by one in detail.

  • The Fundamentals of Data Science
  • Sectors Involving Data Science
  • Statistical Tools and Terminologies
  • Machine Learning Tools and Terminologies
  • Deep Learning Key Terms

Machine Learning Tools and Terminologies

The unconventional and ever-evolving machine learning algorithms are a cluster of various innovative technologies like deep learning, neural networks, natural-language processing, so on and so forth. Machine Learning terminologies are primarily classified as supervised and unsupervised learning. This glossary outlines most of the machine learning terms and technologies. Initially, it’s a hardship to understand and remember these terminologies, however as aspiring data scientists if you prefer to ace in your field these terminologies are of great help.

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Adagrad

AdaGrad is a gradient-succession optimization algorithm. This algorithm automatically tunes the learning frequency depending on its inspection of the data’s geometry. AdaGrad is built to integrate with datasets bearing inconsistent features and accomplish the set tasks.

Association Rule Learning

Association rule learning is a rule-built machine learning technique to determine fascinating familiarities between variables of large databases. It recognizes stout guidelines in databases using procedures of interest.

Backpropagation

Backpropagation, also known as “backward propagation of errors,” is an algorithm for supervised learning. It uses gradient descent to execute backpropagation. Taken into consideration an artificial neural network and a fault function, the procedure computes the gradient of error function regarding the mass of the neural network.

Bagging

Bootstrap accumulation, alias bagging, is a machine learning collaborative meta-algorithm. It enhances the solidity and precision of machine learning algorithms. Statistical classification and regression mostly use bagging. It also decreases variance and helps in evading overfitting.

Beam Search

Beam search is an optimization of best-first search to reduce memory requirements. It is usually seen in the process of machine translation or other literature where sequence learning is recurrent. Beam search basically lets the neural network to contemplate multiple candidate replies as a substitute for selecting the highest-scoring token at every stage.

Clustering

It is the process of distributing the population or data points across various groups. Categorisation generally happens due to the similarities and dissimilarities of the data points. Clustering can have a definite shape, or it can be shapeless.

 

Image Source: geeksforgeeks.org

Image Source: geeksforgeeks.org

Catastrophic Forgetting

Catastrophic forgetting otherwise known as catastrophic interference is a serious issue in machine learning. It occurs when a model overlooks a prevailing learned pattern while learning a new pattern. This is because the model uses identical parameters to identify both patterns and learning as that of the first to the second pattern. Thus, the overwriting of the parameters’ configuration occurs.

Data Augmentation

It is a procedure to engage computer algorithms or other synthetic ways to upsurge the size of the assembled dataset. Machine learning algorithms characteristically restrict overfitting during training with data abundance and over the collection of data is expensive. Data Augmentation is done via simple transformation wherein the machine is taught to retain the original labels.

Distributed Representation

As per the machine learning algorithm, the data with a native representation characteristically has 1 unit per component. A 5-word vocabulary is thus has  5-dimensional vector, with [1,0,0,0,0] T [1,0,0,0,0] T signifying the first word, [0,1,0,0,0]T[0,1,0,0,0]T representing the second word, and so on and so forth.

In distributed representations, the connotation of the data is spread across the complete vector.

Feature Learning

Feature learning also is called “representation learning” is a collection of practices that permits a system to automatically determine the representations desirable for feature detection or classification from raw data.

Gradient Boosting

Gradient boosting is a machine learning procedure meant for regression and cataloging complications. It creates an estimation model in the form of collaborative weak prediction representations. Mostly like decision trees.

Inceptionism

Inceptionism is a creative technique to understand the concept of neural networks. The network is fed as an image, and it must then detect the fed image, and later the feature in the image will be amplified.

K-nearest Neighbors

It is a machine learning algorithm that categorizes materials depending on their likeliness towards the adjoining neighbors. This method is non-parametric and mainly seen in cataloging and regression. It is a subset of supervised learning and is also used in pattern identification, data mining and interruption detection.

Machine Learning

Machine Learning is the process of assisting computers to gain acquaintance and behave like human beings. The learning usually improves in an involuntary manner over time. The support for machines to learn is through data and information, observations and real-world interactions.

METEOR

METEOR is an involuntary assessment metric for machine translation. The purpose of METEOR is to alleviate apparent feebleness in BLEU (Bilingual Evaluation Understudy). METEOR ranks the machine translation hypotheses by lining them up to orientation translations, just like how BLEU organizes.

 Multilayer Perceptron

A multilayer perceptron (MLP) is a feedforward artificial neural network that produces a definite cluster of output from a given set of inputs. An MLP is categorized into multiple layers of input nodes. They are bound together and led through a graph of input and output layers. MLP uses backpropagation to train networks and a sub category of deep learning technique.

Image Source: ouhk.edu.hk

 Neural Network

A neural network is a sequence of algorithms that recognize the fundamental associations in datasets. This technique impersonates human brain operation. Neural networks can adapt to changing input, so the network produces the best possible result without the need of redesigning the output criteria.

Object Localization

Object localization is the machine learning glitch that involves object recognition–to find out whether an object is present in an image– and if present, what is the site of that object in the image. The title for the position of the object in the image is “bounding box”.

Reinforcement Learning

Reinforcement learning is the process of making appropriate arrangements for given circumstance to capitalize on the returns. These algorithms do not avail obvious goals; as a substitute, they are obligatory to learn optimum goals by trial and error.

Random Initialization

Random initialization is the practice where weights are initialized nearing zero, however it is random. This process breaks the symmetry, in a way preventing all the weights of machine learning model from being uniform and thus delivers better accuracy.

Similarity Learning

Similarity learning is the concept of machine learning that focusses on learning the similarities and differences between two objects. It finds its place in parts of machine learning where there are no constraints on the number of classes for objects to fit in. Face verification is one of the examples of similarity learning.

Supervised Learning

Machine learning algorithms are categorized into supervised and unsupervised learning. Supervised algorithms necessitate human intervention to designate the input and the relevant output. Further, feedback must be fed to obtain precise estimates during training. After the training, the algorithm will apply the learning on its own to novice data.

Support Vector Machines

A Support Vector Machine (SVM) is part of the supervised machine learning algorithm. It is seen in both classification and regression. SVM is to primarily find a hyperplane that ideally splits the dataset into two modules, as shown in the image below.

Image Source: kdnuggets.com

 Unsupervised Learning

Unsupervised algorithms do not require both training and reference about the desirable output. As a substitute, they use an iterative tactic known deep learning to evaluate data and draw inferences. Unsupervised learning algorithms find its use in complex dispensing tasks when in comparison with supervised learning systems.

Weight Sharing

In neural networks, weight sharing is a method to reduce the space for trivial parameter and support necessary robust feature recognition. The name for the process of parameter minimizing is model compression.

To Be Continued…

I hope the listicle about “Machine Learning Tools and Terminologies” will serve you as a cheat sheet whenever you’re in need of it. In my next article, I will discuss another set of data science terminologies called “Deep Learning Key Terms”. Also, for more information about data science and related courses visit Acadgild.

Pavithra Vasist

Pavithra Vasist is a Content Writer working with Aeon Learning Pvt Ltd. She was previously working with MetricFox, a marketing outsourcing firm as a Copy Writer. She holds a bachelor's degree in Electrical and Electronics Engineering. Besides writing, she's fascinated with electronic gadgets and mostly spends her spare time drawing or traveling. She resides in Bangalore.

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