1Preface
755.3 Creation of a Bot
21 The Fourth Industrial Revolution
765.4 Library to Command Line Tool Conversion
31.1 Introduction
775.5 Bot Development using AWS Step Functions
41.2 The First Three Industrial Revolution
785.6 Setting Up IAM Credentials & Working with Chalice: 5.6.1 Working with Chalice
51.3 What is the Fourth Industrial Revolution?
795.7 Step Function Building
61.4 Impacts of 4th IR on Business, People & Government
805.8 Summary
71.5 Challenges & Opportunities of the 4th IR
815.9 References
81.6 Technologies Assisting the 4th IR
826 NBA: Social Media Influence Prediction
91.7 AI Influence on the 4th IR
836.1 Introduction
101.8 How to Prepare for the 4th IR?
846.2 Problem Phrasing and Data Gathering
111.9 Summary
856.3 Data Sources Collecting Challenges
121.10 References
866.4 Athletes: Wikipedia Pageview Collection
132 The Basics of Practical AI
876.5 Athletes: Twitter Engagement Collection
142.1 Introduction: 2.1.1 Differences between Machine Learning and Deep Learning
886.6 Data Analysis of NBA Athletes
152.2 Practical Artificial Intelligence
896.7 NBA Players & Unsupervised Machine Learning
162.2.1 Importance of Practical AI
906.8 R: Faceting Cluster Plotting for NBA Players
172.2.2 Examples of Practical AI
916.9 Combining Data of Teams, Power, Endorsements & Players
182.2.3 Getting Started on AI
926.10 Further Learnings & Practical Steps
192.3 Development Timeline of Python
936.11 Summary
202.4 Practical Overview of Python: 2.4.1 Characteristics of Python
946.12 References
212.5 Procedural Statements, Compound Statements & Printing
957 Optimizing EC2 Cases on AWS
222.6 Variable: Creation & Utilization
967.1 Introduction
232.6.1 Creating Variables
977.2 AWS: Running Jobs and Spot Instances: 7.2.1 Concepts
242.6.2 Variable Names
987.3 Real Estate Value Exploring in the US
252.6.3 Assign Value to Multiple Variables
997.4 Python: Interactive Data Visualization
262.7 Adding Numbers: 2.7.1 Mathematical Constants
1007.4.1 Matplotlib
272.8 Number Addition & Subtraction
1017.4.2 All about Bokeh
282.9 Decimal Multiplication
1027.4.3 Benefits of Bokeh
292.10 What are Strings?: 2.10.1 How to Format Them?
1037.5 Clustering on Price & Size Rank: 7.5.1 The Idea of Clustering Analysis
302.11 Exponents Use & Rounding Numbers
1047.6 Summary
312.11.1 Exponents Use
1057.7 References
322.11.2 Rounding Numbers
1067.8 Websites
332.12 Changing Numerical Types
1078 GitHub Organization: Project Management Insights Finding
342.13 Dictionaries
1088.1 Introduction
352.14 Data Structure
1098.2 Software Projrct Management Issues
362.15 Functions & Lists
1108.3 SPM Exploratory Questions
372.16 Python: Control Structures
1118.4 Project Skeleton Creation for Primary Data Science
382.17 Summary
1128.5 Collection and Transformation of Data
392.18 References
1138.6 Talking to a Whole GitHub Organization
403 Cloud AI Development: Google Cloud Platform & AWS
1148.7 Domain Specific Stats Creation
413.1 Introduction
1158.8 CLI: Wiring Data Science Projects
423.2 What is Google Cloud Platform?
1168.9 Github Organization Exploring with Jupyter Notebook
433.2.1 Different Elements of GCP
1178.10 Pallets GitHub Project
443.2.2 History of GCP
1188.11 The CPython Project: File Metadata Consideration: 8.11.1 The CPython Project: Deleted Files Considerations
453.2.3 GCP Services
1198.12 Python Package Index: Deploying a Project
463.3 Colaboratory
1208.13 Summary
473.4 What is Data Lab?
1218.14 Websites
483.4.1 Data Lab Extension using Google & Docker Container Registry
1229 Production AI: Content Generated by Users
493.4.2 Data Lab: Starting with Powerful Machines
1239.1 Introduction
503.4.3 Launching Powerful Machines with Data Lab
1249.2 Production, AI Implementation & the Netflix Prize
513.5 What is BigQuery?: 3.5.1 Data Movement from Command Line to BigQuery
1259.3 Recommendation Systems: Key Concepts
523.6 AI Services Based on Google Cloud
1269.3.1 Collaborative Recommendation System
533.7 Tensor: 3.7.1 Tensorflow and Cloud TPU
1279.3.2 Demographic Recommendation System
543.8 Cloud TPUS: Running MNIST
1289.3.3 Utility-Based Recommendation System
553.9 AWS for Virtual Reality and Augmented Reality Solutions
1299.3.4 Knowlegde-Based Recommendation System
563.10 EFS & Flask for AR/VR Pipeline: 3.10.1 Flask, Pandas and EFS for Data Engineering Pipeline
1309.3.5 Hybrid Recommendation System
573.11 Summary
1319.4 Surprise Framework Utilization in Python
583.12 References
1329.4.1 Software Framework
594 AI Toolchain, ML Toolchain & Lifecycle of Spartan AI
1339.4.2 Python Surprise
604.1 Introduction
1349.5 Recommendation Systems Using Cloud Solutions
614.2 Data Science System of Python
1359.6 Recommendation for Real World Production
624.3 R, Shiny, GGPlot & Rstudio
1369.7 Integrating Using Production API
634.4 Google and Excel Sheets
1379.7.1 API
644.5 AWS for Cloud AI Development: 4.5.1 AWS: DevOps
1389.7.2 Alchemy API
654.6 Data Science: Setup of Basic Docker: 4.6.1 Why Choose Docker?
1399.7.3 Aylien
664.7 Build Servers: CircleCI, Travis, and Jenkins
1409.7.4 Lexalytics/Semantria
674.8 Practical Production Feedback Loop
1419.8 Cloud Sentiment & NLP Analysis
684.9 AWS Batch, Glue Feedback Loop & Sagemaker
1429.9 NLP on Google Cloud Platform & Azure
694.10 Feedback Loops Based on Docker
1439.10 Entity API Exploration
704.11 Summary
1449.11 AWS: Production Serverless Artificial Intelligence for NLP
714.12 References
1459.12 Summary
725 Intelligent Slackbot Creation on AWS
1469.13 References
735.1 Introduction
147Index
745.2 What is an Intelligent Slackbot?
