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Machine learning correct answers AI that learns predictive models from data; automates analytical model building Machine learning correct answers Uses algorithms that iteratively learn from data to allow computers to find hidden insight without being explicitly programmed Machine learning cor...

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BUAL 5650 Final Exam || with A+ Guaranteed Solutions.
Machine learning correct answers AI that learns predictive models from data; automates
analytical model building

Machine learning correct answers Uses algorithms that iteratively learn from data to allow
computers to find hidden insight without being explicitly programmed

Machine learning correct answers A computer program is said to learn from experience E with
respect to some task T and some performance measure P. if its performance on T, as measured
by P, improves with experience E

Supervised learning correct answers Labeled data, direct feedback; used to predict
outcome/future

Unsupervised learning correct answers No labels or feedback; used to "find hidden structures"

Supervised: Regression correct answers Linear or polynomial; y is the regressand and x is a
vector of the regressors

Supervised: Classification correct answers Logistic regression, neural networks. support vector
machines (SVM), Naive-Bayes

Neural networks correct answers Based on how we think the human brain works; collection of
software "neurons" are created and connected and the network is asked to solve a problem over
and over to strengthen connections leading to success

Support vector machines (SVM) correct answers Maximize the width of the decision boundary

Naive-Bayes correct answers Family of simple probabilistic classifiers based on Bayes' theorem;
commonly used for spam detection, baseline technique

Unsupervised: Clustering correct answers K-means, principal component analysis (PCA),
singular value decomposition (SVD), hierarchical clustering, time-series clustering

K-means correct answers Iterative, 2 steps; cluster assignment based on Voronoi diagram, mean
distance boundaries; new centroid stepm calculate the new centroid based on assigned points

Unsupervised, continuous correct answers Clustering and dimensionality reduction; SVD, PCA,
K-means

Unsupervised, categorical correct answers `Association analysis, Hidden Markov Model

Supervised, continuous correct answers Regression, decision trees, random forests

, Supervised, categorical correct answers Classification; KNN, trees, logistic regression, Naive-
Bayes, SVM

Deep learning correct answers Type of machine learning that allows computers to learn complex
concepts by learning simpler concepts and combining them; uses layers

Deep neural network (DNN) correct answers Neural network with more than one hidden layer

Convolutional neural network (CNN) correct answers Type of deep neural network; used for
image and video classification, image captioning, and natural language processing (NLP);
utilizes kernels

Recurrent neural network (RNN) correct answers Type of deep neural network; used for NLP
and sequential data; advanced RNNs include recursive neural networks, GRUs, and LSTMs

Combining RNNs and CNNs correct answers Image captioning, video classification, and video
generation

Hadoop correct answers HDFS and MapReduce; invented by Google and first used by Yahoo

MapReduce correct answers Programming paradigm for big data; divides data into smaller
chunks for simultaneous analysis

Map -> Sort -> Reduce

Apache Spark correct answers Hadoop based solution for high-speed big data analytics; runs on
top of HDFS

Expensive but up to 100x faster than Hadoop; runs in memory using Python, Scala, Jave, and R

Key feature of Spark correct answers Resilient distributed dataset: fundamental data structure
- Immutable distributed collections of objects
- Fault-tolerant
- Works well for unstructured data

ETL correct answers Extract. Transform, Load; traditionally tied to integration of organizational
data, not appropriate for real-time integration

ELT correct answers Extract, Load, Transform; can involve unstructured data and supports near
real-time integration across platforms and sources

Volume correct answers The scale of data

Velocity correct answers The speed of data that arrives

Variety correct answers Different forms of data

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