Advanced Data Analytics Using Python_ With Machine Learning, Deep Learning and NLP Examples ( 2023)

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About the AuthorSayan Mukhopadhyay has more than13 years of industry experience and has beenassociated with companies such as CreditSuisse, PayPal, CA Technologies, CSC, andMphasis. He has a deep understanding ofapplications for data analysis in domains suchas investment banking, online payments,online advertisement, IT infrastructure, andretail. His area of expertise is in applyinghigh-performance computing in distributedand data-driven environments such as real-time analysis, high-frequencytrading, and so on.He earned his engineering degree in electronics and instrumentationfrom Jadavpur University and his master’s degree in research incomputational and data science from IISc in Bangalore.xi
About the Technical ReviewerSundar Rajan Raman has more than 14 yearsof full stack IT experience in machinelearning, deep learning, and naturallanguage processing. He has six yearsof big data development and architectexperience, including working with Hadoopand its ecosystems as well as other NoSQLtechnologies such as MongoDB andCassandra. In fact, he has been the technicalreviewer of several books on these topics.He is also interested in strategizing using Design Thinking principlesand in coaching and mentoring people.xiii
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Page 5 and 6: Table of ContentsAbout the Authorxi
Page 7 and 8: Table of ContentsNaive Bayes Classi
Page 9: Table of ContentsTime-Series Analys
Page 13 and 14: CHAPTER 1IntroductionIn this book,
Page 15 and 16: Chapter 1IntroductionOOP in PythonB
Page 17 and 18: Chapter 1Introductionif pageFile.ge
Page 19 and 20: Chapter 1Introductiondef parseSoupT
Page 21 and 22: Chapter 1Introductionsuper(AirLineR
Page 23 and 24: Chapter 1Introductionif "Value" in
Page 25 and 26: Chapter 1Introductionavailable in R
Page 27 and 28: Chapter 1Introductiong.scores = Tab
Page 29 and 30: Chapter 1Introductionif f == 'clien
Page 31 and 32: Chapter 1Introductionmetadata1 = Me
Page 33 and 34: Chapter 1Introduction(pred4,prob4)
Page 35 and 36: CHAPTER 2ETL with Python(Structured
Page 37 and 38: Chapter 2ETL with Python (Structure
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CHAPTER 3Supervised LearningUsing P
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Chapter 3Supervised Learning Using
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Decision TreeChapter 3 Supervised L
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CHAPTER 4UnsupervisedLearning: Clus
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Chapter 4Unsupervised Learning: Clu
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General and Euclidean DistanceThe d
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Chapter 4Graph Theoretical Approach
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CHAPTER 5Deep Learningand Neural Ne
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Chapter 5Deep Learning and Neural N
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Chapter 6Time SeriesFigure 6-1. A t
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Chapter 6Time SeriesA trend can be
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Chapter 6Time SeriesThe simple movi
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Chapter 6Time SeriesIrregular Fluct
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Chapter 6Time SeriesFigure 6-2 show
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Chapter 6Time Seriesìïïr ( k)=í
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Chapter 6Time SeriesThe autocovaria
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Chapter 6Time SeriesIn this case, r
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Chapter 6Time SeriesHere is how to
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Chapter 6 Time SeriesThe Fourier Tr
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Chapter 6Time Seriesmodel provides
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CHAPTER 7Analytics at ScaleIn recen
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Chapter 7Analytics at Scalealphabet
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Chapter 7Analytics at Scalepublic a
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Chapter 7Analytics at ScaleHere is
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Chapter 7Analytics at Scaleimport o
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Chapter 7Analytics at ScaleRootBDAS
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Chapter 7Analytics at ScaleTo test
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Chapter 7Analytics at ScaleHDFS Fil
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Chapter 7Analytics at ScaleJoin Pat
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Chapter 7Analytics at Scale}{}Strin
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Chapter 7Analytics at Scale}}String
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Chapter 7Analytics at ScaleSpark Co
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Chapter 7Analytics at Scalespeech p
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Chapter 7Analytics at Scaley=height
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Chapter 7Analytics at Scaleif not s
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Chapter 7Analytics at Scaleelse:(pr
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Chapter 7Analytics at Scalefor futu
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Chapter 7Analytics at ScaleYou can
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IndexCollaborative filtering, 52Com
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IndexNeo4j, 34Neo4j REST, 35Neural
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IndexTime series (cont.)transformat
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Advanced Data Analytics Using Python_ With Machine Learning, Deep Learning and NLP Examples ( 2023)

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