Linked e-resources
Details
Table of Contents
Intro
Table of Contents
About the Authors
About the Technical Reviewer
Acknowledgments
Introduction
Chapter 1: A Birds Eye View to AI System
OOP in Python
Calling Other Languages in Python
Exposing the Python Model as a Microservice
High-Performance API and Concurrent Programming
Choosing the Right Database
Summary
Chapter 2: ETL with Python
MySQL
How to Install MySQLdb?
Database Connection
INSERT Operation
READ Operation
DELETE Operation
UPDATE Operation
COMMIT Operation
ROLL-BACK Operation
Normal Forms
First Normal Form
Second Normal Form
Third Normal Form
Elasticsearch
Connection Layer API
Neo4j Python Driver
neo4j-rest-client
In-Memory Database
MongoDB (Python Edition)
Import Data into the Collection
Create a Connection Using pymongo
Access Database Objects
Insert Data
Update Data
Remove Data
Cloud Databases
Pandas
ETL with Python (Unstructured Data)
Email Parsing
Topical Crawling
Crawling Algorithms
Summary
Chapter 3: Feature Engineering and Supervised Learning
Dimensionality Reduction with Python
Correlation Analysis
Principal Component Analysis
Mutual Information
Classifications with Python
Semi-Supervised Learning
Decision Tree
Which Attribute Comes First?
Random Forest Classifier
Naïve Bayes Classifier
Support Vector Machine
Nearest Neighbor Classifier
Sentiment Analysis
Image Recognition
Regression with Python
Least Square Estimation
Logistic Regression
Classification and Regression
Intentionally Bias the Model to Over-Fit or Under-Fit
Dealing with Categorical Data
Summary
Chapter 4: Unsupervised Learning: Clustering
K-Means Clustering
Choosing K: The Elbow Method
Silhouette Analysis
Distance or Similarity Measure
Properties
General and Euclidean Distance
Squared Euclidean Distance
Distance Between String-Edit Distance
Levenshtein Distance
Needleman-Wunsch Algorithm
Similarity in the Context of a Document
Types of Similarity
Example of K-Means in Images
Preparing the Cluster
Thresholding
Time to Cluster
Revealing the Current Cluster
Hierarchical Clustering
Bottom-Up Approach
Distance Between Clusters
Single Linkage Method
Complete Linkage Method
Table of Contents
About the Authors
About the Technical Reviewer
Acknowledgments
Introduction
Chapter 1: A Birds Eye View to AI System
OOP in Python
Calling Other Languages in Python
Exposing the Python Model as a Microservice
High-Performance API and Concurrent Programming
Choosing the Right Database
Summary
Chapter 2: ETL with Python
MySQL
How to Install MySQLdb?
Database Connection
INSERT Operation
READ Operation
DELETE Operation
UPDATE Operation
COMMIT Operation
ROLL-BACK Operation
Normal Forms
First Normal Form
Second Normal Form
Third Normal Form
Elasticsearch
Connection Layer API
Neo4j Python Driver
neo4j-rest-client
In-Memory Database
MongoDB (Python Edition)
Import Data into the Collection
Create a Connection Using pymongo
Access Database Objects
Insert Data
Update Data
Remove Data
Cloud Databases
Pandas
ETL with Python (Unstructured Data)
Email Parsing
Topical Crawling
Crawling Algorithms
Summary
Chapter 3: Feature Engineering and Supervised Learning
Dimensionality Reduction with Python
Correlation Analysis
Principal Component Analysis
Mutual Information
Classifications with Python
Semi-Supervised Learning
Decision Tree
Which Attribute Comes First?
Random Forest Classifier
Naïve Bayes Classifier
Support Vector Machine
Nearest Neighbor Classifier
Sentiment Analysis
Image Recognition
Regression with Python
Least Square Estimation
Logistic Regression
Classification and Regression
Intentionally Bias the Model to Over-Fit or Under-Fit
Dealing with Categorical Data
Summary
Chapter 4: Unsupervised Learning: Clustering
K-Means Clustering
Choosing K: The Elbow Method
Silhouette Analysis
Distance or Similarity Measure
Properties
General and Euclidean Distance
Squared Euclidean Distance
Distance Between String-Edit Distance
Levenshtein Distance
Needleman-Wunsch Algorithm
Similarity in the Context of a Document
Types of Similarity
Example of K-Means in Images
Preparing the Cluster
Thresholding
Time to Cluster
Revealing the Current Cluster
Hierarchical Clustering
Bottom-Up Approach
Distance Between Clusters
Single Linkage Method
Complete Linkage Method