𝔖 Scriptorium
✦   LIBER   ✦

📁

The Ultimate Data and AI Guide: 150 FAQs About Artificial Intelligence, Machine Learning and Data

✍ Scribed by Alexander Thamm, Michael Gramlich, Dr. Alexander Borek


Publisher
Data AI Press
Year
2020
Tongue
English
Leaves
578
Category
Library

⬇  Acquire This Volume

No coin nor oath required. For personal study only.

✦ Synopsis


Everything you need to know about data and AI.

Are you lost in the buzzword jungle around artificial intelligence, machine learning and big data? This book is here to help.

The Ultimate Data and AI Guide provides you with a complete overview and solid understanding of the most important concepts around data, machine learning and AI. It is the result of our work as data practitioners implementing and consulting on 500+ data projects at 100+ companies. Everything you need to know simply explained and organized into 150 FAQs that allow you to navigate the buzzword jungle at ease.

Learn how:

- AI systems are created with machine learning methods

- Big data and AI are driving digital transformation

- Databases and data architectures, including data warehouses and data lakes, work

- Cloud computing has been a real game changer for data storage and processing

- Machine learning and data science projects are managed within companies to create data-driven products and services

- …and much more; expect to have no blind spot left in the field of data and AI after reading this book!

What you can expect from this book:

- A complete overview of the most important concepts in AI, data and ML

- Simple, hands-on explanations of complex topics, accompanied by 246 clarifying visualizations and tables

- Content organized into 150 FAQs that you can refer to individually or read as a whole

- 63 real-world-inspired case studies based on our experience of 500+ implemented data projects

- Information from practising experts for (aspiring) data practitioners or simply interested readers

Who this book is for:

- (Aspiring) data practitioners, who want to gain a complete overview of the buzzwords and concepts around data, machine learning and AI

- Interested readers who want to understand how AI, machine learning and data are increasingly shaping our economies and societies - no previous knowledge required

- Professionals who are looking for a reference book and who want to know how to leverage data and machine learning methods in their company

What others are saying:

“A unique book that is the go-to reference in the AI and data space. If you are in any way interested in the world of data and AI you owe it to yourself to read this book, there is something to learn for everyone.”

-Harvinder Atwal, Chief Data Officer at Moneysupermarket.com and Author of Practical DataOps

“Read this! If you are at A = ‘Slides and Stickynotes’ and want to go to B = ‘Real Data and Impact’, this can be your travel guide. If you are a practitioner already creating impact, read this to get structure and perspective into the flow of topics you are handling.”

-Marcel Kling, Senior Director Data Driven Customer Journey, Lufthansa Group

“Much like a puzzle, you have to fit a lot of pieces together to succeed with AI - But how could you even know what pieces you need? This volume provides the answers.”

-Thomas C. Redman, the Data Doc, HBR Blogger and Book Author

“This book is a veritable smorgasbord of insight into the world of data and analytics, singularly lucid and accessible. Recommended to anyone with an interest in the field.”

-Ryan den Rooijen, Group Head of Data & Analytics, Chalhoub Group

“If you found yourself asking ‘what is AI?’ or ‘what is data?’ then you'll find the exact answer in this book, together with 150 FAQs and multiple case studies as a clarifying, coherent framework for your initiative.”

-Stijn Christiaens, Co-founder and CTO of Collibra

What are you waiting for?

Buy the book and start your journey through the exciting data and AI galaxy - this is the guide for your trip!

✦ Table of Contents


Title Page
Copyright
Dedication
Contents
Foreword
Preface
Acknowledgments
Introduction
Part I | Why Do We Care: The Digital Transformation Train
1 Digital Transformation: The Role of Data and Artificial Intelligence
1_1 Digital transformation
1 | What is digital transformation?
2 | What is the impact of digital transformation on companies and society?
3 | What are the drivers of digital transformation?
1_2 The role of data and AI in digital transformation
4 | AI – Why is it the engine of digital transformation?
5 | Data – why is it the fuel of digital transformation?
6 | How are data and AI applied to generate valueacross industries?
1_3 Buzzwords in digital transformation, data and AI
7 | What is an overview of buzzwords in data and AI?
8 | What is the IoT and what does it have to do with big data?
9 | What are data lakes, data warehouses,data architectures, Hadoop and NoSQL databases?
10 | What are data governance and data democratization?
11 | What is the cloud?
12 | What are data science, data analytics, businessintelligence, data mining and predictive analytics?
13 | What are machine learning, neural networks and deep learning?
14 | What are AI, natural language processing,computer vision and robotics?
Part II | The Fuel: Data
2 Understanding Data: The Fuel of Digital andArtificial Intelligence Transformation
2_1 Understanding data
15 | What is data?
16 | Why collect data and what arethe different types of data analytics?
17 | How is data created?
18 | What are the factors that have enabled anera of mass data creation and storage?
19 | What is data quality and what kind of dataquality issues are there?
20 | How much data quality do you need?
2_2 Types of data
21 | What are unstructured, semi-structured andstructured data?
22 | What are master data and transactional data?
23 | What is streaming data and what is the differencebetween batch and streaming processing?
24 | What is big data?
3 Data Storage Technologies
3_1 Understanding data storage
25 | Why can’t a company store its structureddata in an Excel file like we do on PCs?
26 | What is a database and how does it work?
27 | What are the advantages of storing data in a database?
28 | What types of databases are there andhow are they classified?
3_2 Relational (SQL) databases
29 | What is a relational database system andhow does it work?
30 | How does the relational model work?
31 | What is a key attribute and why is it indispensable?
32 | How is data accessed and manipulated ina relational database system (SQL)?
33 | What are the strengths of relationaldatabase systems?
34 | What are the limitations of relationaldatabase systems and how were they revealedwith the dawn of big data?
3_3 Distributed file systems and non-relational(NoSQL) databases
35 | What are computer clusters and how did theidea of “scaling out” form the basis for storing andprocessing big data?
36 | What are distributed file systems andhow do we store data with them?
37 | What are non-relational (NoSQL) databases andwhat does the CAP theorem have to do with them?
38 | How do relational and non-relational databasescompare and when is it best to use each one?
3_4 Popular data storage technologies
39 | What are the types of data storage technologies?
40 | What are Hadoop and the Hadoop Ecosystem (e.g. Hive, HBase, Flume, Kafka)?
41 | What is Spark?
42 | What are MySQL, PostgreSQL, Oracle,Microsoft SQL Server, SAP HANA, IBM Db2 andTeradata Database?
43 | What are MongoDB, Neo4j, Amazon DynamoDB,CouchDB and Redis?
4 Architecting Data: Data Warehouses, Data Lakes and the Cloud
4_1 Understanding data architectures
44 | What is a data architecture and whydo companies need it?
45 | What are the most populararchitectural blueprints?
4_2 Data warehouse architectures
46 | What is a data warehouse (DWH) architecture?
47 | How does a DWH work?
48 | What does a typical data pipeline in a DWH look like?
49 | What are the limitations of a DWH?
50 | What are popular ETL tools?
4_3 Data lakes and streaming architectures
51 | What is a data lake architecture?
52 | How does a data lake work and where should it be used?
53 | How do a DWH and data lake compare?
4_4 Cloud architectures
54 | What is the cloud?
55 | What types of cloud architectures are there?
56 | What types of cloud services are there?
57 | What are the advantages and disadvantages ofusing cloud services?
58 | What is a serverless architecture?
59 | What are the popular cloud providers and services?
5 Managing Data in a Company
5_1 People and job roles
60 | What does a chief data and analytics officer do?
61 | What does a data architect do?
62 | What does a database administrator do?
63 | What other job roles are involved in creatingand maintaining a data architecture?
5_2 Data governance and Democratization
64 | What are data governance and democratization andwhy does data need to be governed and democratized?
65 | What are the key elements of data governance anddata democratization?
66 | How can we make data more findable and accessible?
67 | How can we make data more understandable and share knowledge on data?
68 | How can we make data more trustworthy andimprove the quality of data?
69 | How can we empower the data user with self-serviceBI and analytics?
70 | How can data governance and datademocratization be implemented?
5_3 Data security and protection (privacy)
71 | What is an overview of data security, data protectionand data privacy and how do they relateto each other?
72 | What is data security and how can it be achieved?
73 | What is personal data?
74 | What is data protection (privacy) and why is thedistinction between non-personal and personaldata so important?
75 | General Data Protection Regulation (GDPR) –who, what, where and why?
Part III | The Engine: Artificial Intelligence and Machine Learning
6 Understanding Machine Learning as the KeyDriver Behind Artificial Intelligence
6_1 Understanding AI and ML
76 | What is AI?
77 | Where can AI be applied and how haveapproaches to create AI developed over time?
78 | What is currently possible with AI and what aresome top breakthroughs?
79 | Why is AI almost tantamount to ML (AI = ML + X) today?
80 | What is ML and how can it create AI?
81 | How is a machine able to learn andwhy is ML often considered “Software 2.0”?
82 | What is a machine able to learn –can it predict the future?
6_2 Types of ML
83 | What types of ML are there and how do they differ?
84 | What is supervised ML?
85 | What is the difference between regression andclassification?
86 | What is unsupervised ML?
87 | What are the most commonly used methods in unsupervised learning?
88 | What is reinforcement learning?
6_3 Popular ML tools
89 | What types of ML tools are there?
90 | What is Python?
91 | What is R and RStudio?
92 | What is scikit-learn?
93 | What are Tensorflow and Keras?
94 | What are MLLib, PySpark and SparkR?
95 | What are some popular cloud-based ML tools?
7 Creating and Testing a ML Model with SupervisedMachine Learning
7_1 Creating a machine learning modelwith supervised ML methods
96 | What ingredients do you need and what isthe recipe for creating an ML model?
97 | What is an ML model?
98 | What is a correlation and why is it necessary forML models?
99 | What is feature engineering and why isit considered “applied ML”?
100 | What is feature selection andwhy is it necessary?
101 | Why do we need to split a dataset into training, validation and test sets?
102 | What does it mean to “train an ML model” andhow do you do it?
7_2 Validating, testing and using a machine learning model
103 | What does it mean to “validate a model”, andwhy is it necessary?
104 | What is the difference between validating andtesting a model and why is the latter necessary?
105 | What are overfitting and generalization?
106 | Preventing overfitting: how doescross-validation work?
107 | Preventing overfitting: how does ensemblelearning work?
108 | How else can overfitting be prevented?
109 | How much data is needed to trainan ML model?
8 Popular Machine Learning Model Classes forSupervised Machine Learning
8_1 Some classic ML models
110 | What model classes are there in ML?
111 | How do generalized linear models work?
112 | How do decision trees work?
113 | How do ensemble methods such asthe random forest algorithm work?
114 | How do we choose the right ML model?
8_2 Neural networks and deep learning
115 | What are neural networks and deeplearning and why do they matter?
116 | How do neural networks work?
117 | What is so special about deep neuralnetworks compared to classic ML model classes?
118 | Why are neural networks so good at naturallanguage processing and computer vision?
119 | Are neural networks a universal cure for allML problems or do they also have some drawbacks?
120 | What is transfer learning?
121 | Deep neural networks – why now and whatwill their future look like?
9 Managing Machine Learning in a Company
9_1 Phases of an ML project
122 | How does the ML process work (an overview)?
123 | Phase 1: How can ML use cases be identified? 273
124 | Phase 2: What are data exploration and datapreparation and why are they necessary?
125 | Phase 3: What is model creation?
126 | Phase 4: What is (continuous) model deployment?
9_2 Lessons learned from machine-learning projects
127 | How long does a machine-learning projecttake from the conception of the idea until themodel is deployed?
128 | How many projects make it from the idea tothe end and where do they fail?
129 | What are the most common reasonswhy projects fail?
130 | Why is model deployment the bottleneck formost companies implementing ML projects?
9_3 People and job roles in ML
131 | Which roles are required to implement an ML project?
132 | What does a data scientist do?
133 | What does a data engineer do?
134 | What does an ML engineer do?
135 | What does a statistician do?
136 | What does a software engineer do?
137 | What does a business analyst do?
138 | What do other roles do?
9_4 Agile organization and ways of working
139 | What is agile project management andwhy is it appropriate for ML projects?
140 | What are DevOps and DataOps?
141 | What are the popular organizational structuresand best practices?
9_5 Data ethics in ML
142 | What is data ethics?
143 | What are the ethical considerations indata collection?
144 | What are the ethical considerations whencreating ML models?
145 | What best practices and principles can ensurethe ethical use of data?
Part IV | Where will we go?
10 The Future of Data, Machine Learning and Artificial Intelligence
146 | How are AI and its drivers going to develop?
147 | What are the implications of ML and AI for companies?
148 | We benefit a lot from AI, but will it cost me my job?
149 | Which nation will win the AI race?
150 | When are we going to see the creation of general AI?
Appendix
List of Abbreviations
List of Tables
List of Figures
List of Case Studies
Reference List
Index
About the Authors


📜 SIMILAR VOLUMES


The Ultimate Data and AI Guide: 150 FAQs
✍ Alexander Thamm, Michael Gramlich, Dr. Alexander Borek 📂 Library 📅 2020 🏛 Data AI Press 🌐 English

<p><b><u>Everything you need to know about data and AI.</u></b></p><p>Are you lost in the buzzword jungle around artificial intelligence, machine learning and big data? This book is here to help.</p><p>The Ultimate Data and AI Guide provides you with a complete overview and solid understanding of th