Machine Learning
Hands-On for Developers and Technical Professionals
(Sprache: Englisch)
Dig deep into the data with a hands-on guide to machine learning with updated examples and more!
Machine Learning: Hands-On for Developers and Technical Professionals provides hands-on instruction and fully-coded working examples for the most...
Machine Learning: Hands-On for Developers and Technical Professionals provides hands-on instruction and fully-coded working examples for the most...
Leider schon ausverkauft
versandkostenfrei
Buch (Kartoniert)
Fr. 66.90
inkl. MwSt.
- Kreditkarte, Paypal, Rechnungskauf
- 30 Tage Widerrufsrecht
Produktdetails
Produktinformationen zu „Machine Learning “
Klappentext zu „Machine Learning “
Dig deep into the data with a hands-on guide to machine learning with updated examples and more!Machine Learning: Hands-On for Developers and Technical Professionals provides hands-on instruction and fully-coded working examples for the most common machine learning techniques used by developers and technical professionals. The book contains a breakdown of each ML variant, explaining how it works and how it is used within certain industries, allowing readers to incorporate the presented techniques into their own work as they follow along. A core tenant of machine learning is a strong focus on data preparation, and a full exploration of the various types of learning algorithms illustrates how the proper tools can help any developer extract information and insights from existing data. The book includes a full complement of Instructor's Materials to facilitate use in the classroom, making this resource useful for students and as a professional reference.
At its core, machine learning is a mathematical, algorithm-based technology that forms the basis of historical data mining and modern big data science. Scientific analysis of big data requires a working knowledge of machine learning, which forms predictions based on known properties learned from training data. Machine Learning is an accessible, comprehensive guide for the non-mathematician, providing clear guidance that allows readers to:
* Learn the languages of machine learning including Hadoop, Mahout, and Weka
* Understand decision trees, Bayesian networks, and artificial neural networks
* Implement Association Rule, Real Time, and Batch learning
* Develop a strategic plan for safe, effective, and efficient machine learning
By learning to construct a system that can learn from data, readers can increase their utility across industries. Machine learning sits at the core of deep dive data analysis and visualization, which is increasingly in demand as companies discover the goldmine
... mehr
hiding in their existing data. For the tech professional involved in data science, Machine Learning: Hands-On for Developers and Technical Professionals provides the skills and techniques required to dig deeper.
... weniger
Inhaltsverzeichnis zu „Machine Learning “
Introduction xxviiChapter 1 What is Machine Learning? 1
History of Machine Learning 1
Alan Turing 1
Arthur Samuel 2
Tom M. Mitchell 2
Summary Definition 3
Algorithm Types for Machine Learning 3
Supervised Learning 3
Unsupervised Learning 4
The Human Touch 4
Uses for Machine Learning 4
Software 4
Stock Trading 5
Robotics 6
Medicine and Healthcare 6
Advertising 7
Retail and E-commerce 7
Gaming Analytics 9
The Internet of Things 10
Languages for Machine Learning 10
Python 10
R 11
Matlab 11
Scala 11
Ruby 11
Software Used in This Book 11
Checking the Java Version 12
Weka Toolkit 12
DeepLearning4J 13
Kafka 13
Spark and Hadoop 13
Text Editors and IDEs 13
Data Repositories 14
UC Irvine Machine Learning Repository 14
Kaggle 14
Summary 14
Chapter 2 Planning for Machine Learning 15
The Machine Learning Cycle 15
It All Starts with a Question 16
I Don't Have Data! 16
Starting Local 17
Transfer Learning 17
Competitions 17
One Solution Fits All? 18
Defining the Process 18
Planning 18
Developing 19
Testing 19
Reporting 19
Refining 19
Production 20
Avoiding Bias 20
Building a Data Team 20
Mathematics and Statistics 20
Programming 21
Graphic Design 21
Domain Knowledge 21
Data Processing 22
Using Your Computer 22
A Cluster of Machines 22
Cloud-Based Services 22
Data Storage 23
Physical Discs 23
Cloud-Based Storage 23
Data Privacy 23
Cultural Norms 24
Generational Expectations 24
The
... mehr
Anonymity of User Data 25
Don't Cross the "Creepy Line" 25
Data Quality and Cleaning 26
Presence Checks 26
Type Checks 27
Length Checks 27
Range Checks 28
Format Checks 28
The Britney Dilemma 28
What's in a Country Name? 31
Dates and Times 33
Final Thoughts on Data Cleaning 33
Thinking About Input Data 34
Raw Text 34
Comma-Separated Variables 34
JSON 35
YAML 37
XML 37
Spreadsheets 38
Databases 39
Thinking About Output Data 39
Don't Be Afraid to Experiment 40
Summary 40
Chapter 3 Data Acquisition Techniques 43
Scraping Data 43
Copy and Paste 44
Google Sheets 46
Using an API 47
Acquiring Weather Data 48
Migrating Data 50
Installing Embulk 51
Using the Quick Run 51
Installing Plugins 52
Migrating Files to Database 53
Bulk Converting CSV to JSON 55
Summary 56
Chapter 4 Statistics, Linear Regression, and Randomness 57
Working with a Basic Dataset 57
Loading and Converting the Dataset 58
Introducing Basic Statistics 59
Minimum and Maximum Values 60
Sum 61
Mean 62
Arithmetic Mean 62
Harmonic Mean 62
Geometric Mean 63
The Relationship Between the Three Averages 63
Mode 65
Median 66
Don't Cross the "Creepy Line" 25
Data Quality and Cleaning 26
Presence Checks 26
Type Checks 27
Length Checks 27
Range Checks 28
Format Checks 28
The Britney Dilemma 28
What's in a Country Name? 31
Dates and Times 33
Final Thoughts on Data Cleaning 33
Thinking About Input Data 34
Raw Text 34
Comma-Separated Variables 34
JSON 35
YAML 37
XML 37
Spreadsheets 38
Databases 39
Thinking About Output Data 39
Don't Be Afraid to Experiment 40
Summary 40
Chapter 3 Data Acquisition Techniques 43
Scraping Data 43
Copy and Paste 44
Google Sheets 46
Using an API 47
Acquiring Weather Data 48
Migrating Data 50
Installing Embulk 51
Using the Quick Run 51
Installing Plugins 52
Migrating Files to Database 53
Bulk Converting CSV to JSON 55
Summary 56
Chapter 4 Statistics, Linear Regression, and Randomness 57
Working with a Basic Dataset 57
Loading and Converting the Dataset 58
Introducing Basic Statistics 59
Minimum and Maximum Values 60
Sum 61
Mean 62
Arithmetic Mean 62
Harmonic Mean 62
Geometric Mean 63
The Relationship Between the Three Averages 63
Mode 65
Median 66
... weniger
Autoren-Porträt von Jason Bell
JASON BELL has worked in software development for over thirty years, now he focuses on large volume data solutions and helping retail and finance customers gain insight from that data with machine learning. He is also an active committee member for several international technology conferences.
Bibliographische Angaben
- Autor: Jason Bell
- 2020, 2. Aufl., 432 Seiten, Masse: 18,9 x 23,3 cm, Kartoniert (TB), Englisch
- Verlag: Wiley & Sons
- ISBN-10: 1119642140
- ISBN-13: 9781119642145
- Erscheinungsdatum: 10.03.2020
Sprache:
Englisch
Kommentar zu "Machine Learning"
0 Gebrauchte Artikel zu „Machine Learning“
Zustand | Preis | Porto | Zahlung | Verkäufer | Rating |
---|
Schreiben Sie einen Kommentar zu "Machine Learning".
Kommentar verfassen