This post presents a collection of Data Science related key terms with concise definitions ordered into separate topics. It takes time to familiarize with data science terminologies as these words may not be used in your routine. However, once you start reading about it and hearing about the terminologies, you will comprehend the importance of these terms in data science and eventually you’d be interested to know more. I, in this article, have grouped key data science terminologies into categories. Let’s now have a look at these sub-categories into which these terminologies are grouped into.
- 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 grouped under unsupervised and supervised learning. This glossary outlines overall 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 of study these terminologies are of great help.
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 accomplish and integrate with datasets bearing inconsistent features.
Association Rule Learning
Association rule learning is a rule-built machine learning technique to determine fascinating familiarities amidst variables belonging to large databases. It is meant to recognize stout guidelines found in databases using procedures of interest.
Backpropagation, also known as “backward propagation of errors,” is an algorithm for supervised learning that employs gradient descent. 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.
Bootstrap accumulating, also famous as bagging, is a machine learning collaborative meta-algorithm that 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 is an optimization of best-first search that reduces its 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.
It is the process of distributing the population or data points across various groups. The categorization is in a way that the data points belonging to a single group is very much like each other and dissimilar to the data points in other groups. The grouping generally happens due to the similarities and dissimilarities. Clustering can have a definite shape, or it can be shapeless.
Image Source: geeksforgeeks.org
Image Source: geeksforgeeks.org
Catastrophic forgetting otherwise known as catastrophic interference is an imminent issue in machine learning. It occurs when a model overlooks a prevailing learned pattern during the process of learning a new pattern.
The model employs the identical parameters to identify both patterns and learning of the first to the second pattern as well. Thus, the overwriting of the parameters’ configuration occurs.
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.
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 has another title “representation learning”. It 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 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 is a conception technique to understand the concept of neural networks. The network is fed an image, and it must then detect the fed image, and later the feature in the image will be amplified.
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 is the science of assisting computers to gain acquaintance and behave like human beings. The learning usually improves in an involuntary manner over time. The nourishment of machines is through data and information, observations and real-world interactions.
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.
A multilayer perceptron (MLP) is a feedforward artificial neural network that produces a definite cluster of output through a set of inputs. An MLP is categorized into multiple layers of input nodes that are bound together and led through a graph among the input and output layers. MLP uses backpropagation to train networks. It is a part of deep learning technique.
Image Source: ouhk.edu.hk
A neural network is a sequence of algorithms that try to recognize fundamental associations in datasets. The techniques that are in use impersonate 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 is the machine learning snag 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 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 is the practice of through which random numbers adjust weights of the machine learning prototype. This is the process of to break the symmetry, in a way preventing all the weights of machine learning model from being uniform and deliver improved accuracy.
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 instances for similarity learning.
Machine learning algorithms are categorized into supervised or unsupervised learning. Supervised algorithms necessitate human intervention to designate the input and the relevant output. Further, feedback must be fed with regards to the precision of the estimates during training. After the training, the algorithm will apply the learnings on its own to the fresh data.
Support Vector Machines
A Support Vector Machine (SVM) is part of the supervised machine learning algorithm used in both classification and regression. The primary motive of SVM is to find a hyperplane that ideally splits dataset into two modules, as shown in the image below.
Image Source: kdnuggets.com
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.
In neural networks, weight sharing is a method to reduce the space for trivial parameter and entertain 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 be accessible to serve as a cheat sheet whenever you’re in need of it. In my next article, I will discuss another set of data science terminologies with the heading “Deep Learning Key Terms”. Also, for more information about data science and related courses visit Acadgild.