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Numerical Computing with Python : Harness the Power of Python to Analyze and Find Hidden Patterns in the Data.

Author: Pratap Dangeti; Allen Yu; Claire Chung; Aldrin Yim; Theodore Petrou
Publisher: Birmingham : Packt Publishing Ltd, 2018.
Series: Learning path.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
Data mining, or parsing the data to extract useful insights, is a niche skill that can transform your career as a data scientist Python is a flexible programming language that is equipped with a strong suite of libraries and toolkits, and gives you the perfect platform to sift through your data and mine the insights you seek.
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Dangeti, Pratap.
Numerical Computing with Python : Harness the Power of Python to Analyze and Find Hidden Patterns in the Data.
Birmingham : Packt Publishing Ltd, ©2018
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Pratap Dangeti; Allen Yu; Claire Chung; Aldrin Yim; Theodore Petrou
ISBN: 9781789957228 1789957222
OCLC Number: 1080999098
Notes: Modeling Blackjack example of Monte Carlo methods using Python
Description: 1 online resource (676 pages)
Contents: Cover; Title Page; Copyright; Contributors; About Packt; Table of Contents; Preface; Chapter 1: Journey from Statistics to Machine Learning; Statistical terminology for model building and validation; Machine learning; Statistical fundamentals and terminology for model building and validation; Bias versus variance trade-off; Train and test data; Summary; Chapter 2: Tree-Based Machine Learning Models; Introducing decision tree classifiers; Terminology used in decision trees; Decision tree working methodology from first principles; Comparison between logistic regression and decision trees Comparison of error components across various styles of modelsRemedial actions to push the model towards the ideal region; HR attrition data example; Decision tree classifier; Tuning class weights in decision tree classifier; Bagging classifier; Random forest classifier; Random forest classifier --
grid search; AdaBoost classifier; Gradient boosting classifier; Comparison between AdaBoosting versus gradient boosting; Extreme gradient boosting --
XGBoost classifier; Ensemble of ensembles --
model stacking; Ensemble of ensembles with different types of classifiers Ensemble of ensembles with bootstrap samples using a single type of classifierSummary; Chapter 3: K-Nearest Neighbors and Naive Bayes; K-nearest neighbors; KNN voter example; Curse of dimensionality; Curse of dimensionality with 1D, 2D, and 3D example; KNN classifier with breast cancer Wisconsin data example; Tuning of k-value in KNN classifier; Naive Bayes; Probability fundamentals; Joint probability; Understanding Bayes theorem with conditional probability; Naive Bayes classification; Laplace estimator; Naive Bayes SMS spam classification example; Summary; Chapter 4: Unsupervised Learning K-means clusteringK-means working methodology from first principles; Optimal number of clusters and cluster evaluation; The elbow method; K-means clustering with the iris data example; Principal Component Analysis --
PCA; PCA working methodology from first principles; PCA applied on handwritten digits using scikit-learn; Singular value decomposition --
SVD; SVD applied on handwritten digits using scikit-learn; Deep auto encoders; Model building technique using encoder-decoder architecture; Deep auto encoders applied on handwritten digits using Keras; Summary; Chapter 5: Reinforcement Learning Reinforcement learning basicsCategory 1 --
value based ; Category 2 --
policy based ; Category 3 --
actor-critic; Category 4 --
model-free; Category 5 --
model-based; Fundamental categories in sequential decision making; Markov decision processes and Bellman equations; Dynamic programming; Algorithms to compute optimal policy using dynamic programming; Grid world example using value and policy iteration algorithms with basic Python; Monte Carlo methods; Monte Carlo prediction; The suitability of Monte Carlo prediction on grid-world problems
Series Title: Learning path.

Abstract:

Data mining, or parsing the data to extract useful insights, is a niche skill that can transform your career as a data scientist Python is a flexible programming language that is equipped with a  Read more...

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