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cloud computing is the delivery of computing services—including servers, storage,
databases, networking, software, analytics, and intelligence—over the Internet (“the
cloud”) to offer faster innovation, flexible resources, and economies of scale.
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Matrix factorization is a class of collaborative filtering algorithms used in
recommender systems. Matrix factorization algorithms work by decomposing the
user-item interaction matrix into the product of two lower dimensionality rectangular
matrices.
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Deep learning is a type of machine learning and artificial intelligence (AI) that
imitates the way humans gain certain types of knowledge.
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Machine learning is a subfield of artificial intelligence, which is broadly defined as
the capability of a machine to imitate intelligent human behavior. Artificial intelligence
systems are used to perform complex tasks in a way that is similar to how humans
solve problems.
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The most commonly used Supervised Learning algorithms are decision tree,
logistic regression, linear regression, support vector machine. The most commonly
used Unsupervised Learning algorithms are k-means clustering, hierarchical
clustering, and apriori algorithm.
+ The main difference between supervised and unsupervised learning: Labeled
data. The main distinction between the two approaches is the use of labeled
datasets. To put it simply, supervised learning uses labeled input and output
data, while an unsupervised learning algorithm does not.
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What is decision tree and example?
A decision tree is one of the supervised machine learning algorithms. This algorithm can be
used for regression and classification problems — yet, is mostly used for classification
problems. A decision tree follows a set of if-else conditions to visualize the data and classify
it according to the conditions.
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What is logistic regression and its example?
Logistic Regression is used when the dependent variable(target) is categorical. For example,
To predict whether an email is spam (1) or (0) Whether the tumor is malignant (1) or not (0)
Logistic regression is commonly used for prediction and classification problems. Some of
these use cases include: Fraud detection: Logistic regression models can help teams
identify data anomalies, which are predictive of fraud.
● What is linear regression explain with example?
Linear regression is commonly used for predictive analysis and modeling. For example, it
can be used to quantify the relative impacts of age, gender, and diet (the predictor variables)
on height (the outcome variable).
● What is clustering algorithm with example?
The clustering algorithm is an unsupervised method, where the input is not a labeled one
and problem solving is based on the experience that the algorithm gains out of solving
similar problems as a training schedule.
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A convolutional neural network (CNN or ConvNet) is a network architecture for
deep learning that learns directly from data. CNNs are particularly useful for finding
patterns in images to recognize objects, classes, and categories. They can also be
quite effective for classifying audio, time-series, and signal data.
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What is biometric data?
Data relating to the physical, physiological or behavioural characteristics of an individual
which allow their unique identification, such as facial images or dactyloscopic data.
● What is the artificial intelligence?
Artificial intelligence is the simulation of human intelligence processes by machines,
especially computer systems. Specific applications of AI include expert systems, natural
language processing, speech recognition and machine vision.
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The k-nearest neighbors algorithm, also known as KNN or k-NN, is a
non-parametric, supervised learning classifier, which uses proximity to make
classifications or predictions about the grouping of an individual data point.
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Overfitting is a concept in data science, which occurs when a statistical model fits
exactly against its training data. When this happens, the algorithm unfortunately
cannot perform accurately against unseen data, defeating its purpose.
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recommender system, or a recommendation system, is a subclass of information
filtering system that seeks to predict the “rating” or “preference” a user would give to
an item.