Advanced data analytics using Python: with machine learning, deep learning and NLP examples
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Apress
[2018]
|
| Schlagworte: | |
| Online-Zugang: | Inhaltstext http://www.springer.com/ Inhaltsverzeichnis |
| Beschreibung: | xv, 186 Seiten Diagramme, Portraits |
| ISBN: | 9781484234495 1484234499 |
Internformat
MARC
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Datensatz im Suchindex
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| adam_text | Table of Contents
About the Author............................................................ xi
About the Technical Reviewer.................................................xiii
Acknowledgments....................................................... xv
Chapter 1: Introduction.......................................................1
Why Python?............................................................ 1
When to Avoid Using Python..................................................2
OOP in Python................................................... ...........3
Calling Other Languages in Python...............................................12
Exposing the Python Model as a Microservice.................................14
High-Performance API and Concurrent Programming.................. .........17
Chapter 2: ETL with Python (Structured Data)..................................23
MySQL..................................................................... 23
How to Install MySQLdb?............................. .................23
Database Connection............................................... 24
INSERT Operation............................................ ..........24
READ Operation................................... .....................25
DELETE Operation................................................ 26
UPDATE Operation.............................................. ......¿.27
COMMIT Operation............................................ .........28
ROLL-BACK Operation................................................. 28
v
TABLE OF CONTENTS
Elasticsearch..............................................................31
Connection Layer API....................................................33
Neo4j Python Driver........................................................34
neo4j-rest-client..........................................................35
In-Memory Database..........................................................35
MongoDB (Python Edition)....................................................36
Import Data into the Collection.........................................36
Create a Connection Using pymongo.......................................37
Access Database Objects.................................................37
Insert Data.............................................................38
Update Data.............................................................38
Remove Data.............................................................38
Pandas.....................................................................38
ETL with Python (Unstructured Data).........................................40
E-mail Parsing..........................................................40
Topical Crawling..................................................... 42
Chapter 3: Supervised Learning Using Python................................. 49
Dimensionality Reduction with Python........................................49
Correlation Analysis....................................................50
Principal Component Analysis............................................53
Mutual Information......................................................56
Classifications with Python.................................................57
Semisupervised Learning.....................................................58
Decision Tree...............................................................59
Which Attribute Comes First?.......................................... 59
Random Forest Classifier............................................ 60
vi
TABLE OF CONTENTS
Naive Bayes Classifier................................................................61
Support Vector Machine..................................................................... .........62
Nearest Neighbor Classifier.............................................. ..........................64
Sentiment Analysis................................................................................ 65
Image Recognition....................................................................... ..........67
Regression with Python................................................................................67
Least Square Estimation......................................................................... 68
Logistic Regression..................................................................................69
Classification and Regression.........................................................................70
Intentionally Bias the Model to Over-Fit or Under-Fit...................................................71
Dealing with Categorical Data............................................. ..........................73
Chapter 4: Unsupervised Learning: Clustering............................................................77
K-Means Clustering............................................... ...................................78
Choosing K:The Elbow Method............................................................................82
Distance or Similarity Measure........................................................................82
Properties.............................................................. ...................82
General and Euclidean Distance......................................................... ......83
Squared Euclidean Distance............................................ ........................84
Distance Between String-Edit Distance....................................... ....................85
Similarity in the Context of Document.................................................................87
Types of Similarity...............................................................................87
What Is Hierarchical Clustering?....................................................................88
Bottom-Up Approach.......................................................................... 89
Distance Between Clusters.......................................................................90
Top-Down Approach......................................................... ......................92
Graph Theoretical Approach........................................................................97
How Do You Know If the Clustering Result Is Good?...............................................97
Vll
TABLE OF CONTENTS
Chapter 5: Deep Learning and Neural Networks................................99
Backpropagation.........................................................100
Backpropagation Approach.............................................100
Generalized Delta Rule...............................................100
Update of Output Layer Weights.......................................101
Update of Hidden Layer Weights.......................................102
BPN Summary..........................................................103
Backpropagation Algorithm...............................................104
Other Algorithms........................................................106
TensorFlow..............................................................106
Recurrent Neural Network............................................ 113
Chapter 6: Time Series................................................. 121
Classification of Variation.............................................121
Analyzing a Series Containing a Trend...................................121
Curve Fitting........................................................122
Removing Trends from a Time Series...................................123
Analyzing a Series Containing Seasonality............................. 124
Removing Seasonality from a Time Series............................... 125
By Filtering....................................................... 125
By Differencing......................................................126
Transformation...........................................................126
To Stabilize the Variance............................................126
To Make the Seasonal Effect Additive.................................127
To Make the Data Distribution Normal.................................127
Stationary Time Series................................................ 128
Stationary Process...................................................128
Autocorrelation and the Correlogram..................................129
Estimating Autocovariance and Autocorrelation Functions..............129
viii
TABLE OF CONTENTS
Time-Series Analysis with Python.....................................130
Useful Methods...................................................131
Autoregressive Processes.........................................133
Estimating Parameters of an AR Process...........................134
Mixed ARMA Models....................................................137
Integrated ARMA Models............................................. 138
The Fourier Transform................................................140
An Exceptional Scenario..............................................141
Missing Data.........................................................143
Chapter 7: Analytics at Scale..........................................145
Hadoop.............................................................. 145
MapReduce Programming............................................145
Partitioning Function............................................146
Combiner Function................................................147
HDFS File System.................................................159
MapReduce Design Pattern.........................................159
Spark................................................................166
Analytics in the Cloud............................................. 168
Internet of Things...................................................179
Index................................................................ 181
ix
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| spellingShingle | Mukhopadhyay, Sayan Advanced data analytics using Python with machine learning, deep learning and NLP examples Python Programmiersprache (DE-588)4434275-5 gnd Big Data (DE-588)4802620-7 gnd Datenanalyse (DE-588)4123037-1 gnd |
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| title | Advanced data analytics using Python with machine learning, deep learning and NLP examples |
| title_auth | Advanced data analytics using Python with machine learning, deep learning and NLP examples |
| title_exact_search | Advanced data analytics using Python with machine learning, deep learning and NLP examples |
| title_full | Advanced data analytics using Python with machine learning, deep learning and NLP examples Sayan Mukhopadhyay |
| title_fullStr | Advanced data analytics using Python with machine learning, deep learning and NLP examples Sayan Mukhopadhyay |
| title_full_unstemmed | Advanced data analytics using Python with machine learning, deep learning and NLP examples Sayan Mukhopadhyay |
| title_short | Advanced data analytics using Python |
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| title_sub | with machine learning, deep learning and NLP examples |
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