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K 42
Khrais, Hussam.
Python for Offensive PenTest : : a practical guide to ethical hacking and penetration testing using Python. / Hussam. Khrais. - Birmingham : : Packt Publishing,, 2018. - 1 online resource (169 pages). - URL: https://library.dvfu.ru/lib/document/SK_ELIB/B2BB1BDE-CE30-4594-8811-BBA0EC1E08C4. - ISBN 9781788832465 (electronic bk.). - ISBN 1788832469 (electronic bk.)
Print version record.
Параллельные издания: Print version: : Khrais, Hussam. Python for Offensive PenTest : A practical guide to ethical hacking and penetration testing using Python. - Birmingham : Packt Publishing, ©2018
Содержание:
Cover; Title Page; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Warming up -- Your First Antivirus-Free Persistence Shell; Preparing the attacker machine; Setting up internet access; Preparing the target machine; TCP reverse shell; Coding a TCP reverse shell; Server side; Client side; Data exfiltration -- TCP; Server side; Client side; Exporting to EXE; HTTP reverse shell; Coding the HTTP reverse shell; Server side; Client side; Data exfiltration -- HTTP; Client side; Server side; Exporting to EXE; Persistence; Making putty.exe persistent.
Making a persistent HTTP reverse shellTuning the connection attempts; Tips for preventing a shell breakdown; Countermeasures; Summary; Chapter 2: Advanced Scriptable Shell; Dynamic DNS; DNS aware shell; Interacting with Twitter; Parsing a tweet in three lines; Countermeasures; Replicating Metasploit's screen capturing; Replicating Metasploit searching for content; Target directory navigation; Integrating low-level port scanner; Summary; Chapter 3: Password Hacking; Antivirus free keylogger; Installing pyHook and pywin; Adding code to keylogger; Hijacking KeePass password manager.
Man in the browserFirefox process; Firefox API hooking with Immunity Debugger; Python in Firefox proof of concept (PoC); Python in Firefox EXE; Dumping saved passwords out of Google Chrome; Acquiring the password remotely; Submitting the recovered password over HTTP session; Testing the file against antivirus; Password phishing -- DNS poisoning; Using Python script; Facebook password phishing; Countermeasures; Securing the online account; Securing your computer; Securing your network; Keeping a watch on any suspicious activity; Summary; Chapter 4: Catch Me If You Can!
Bypassing host-based firewallsHijacking IE; Bypassing reputation filtering in next generation firewalls; Interacting with SourceForge; Interacting with Google Forms; Bypassing botnet filtering; Bypassing IPS with handmade XOR encryption; Summary; Chapter 5: Miscellaneous Fun in Windows; Privilege escalation -- weak service file; Privilege escalation -- preparing vulnerable software; Privilege escalation -- backdooring legitimate windows service; Privilege escalation -- creating a new admin account and covering the tracks; Summary; Chapter 6: Abuse of Cryptography by Malware.
Introduction to encryption algorithmsProtecting your tunnel with AES -- stream mode; Cipher Block Chaining (CBC) mode encryption; Counter (CTR) mode encryption ; Protecting your tunnel with RSA; Hybrid encryption key; Summary; Other Books You May Enjoy; Index.
~РУБ DDC 005.133
Рубрики: Python (Computer program language)
Penetration testing (Computer security)
Application software--Testing.
COMPUTERS / Programming Languages / Python.
COMPUTERS / Software Development & Engineering / Quality Assurance & Testing.
Application software--Testing.
Аннотация: Python is an easy-to-learn and cross-platform programming language which has unlimited third-party libraries. Plenty of open source hacking tools are written in Python and can be easily integrated within your script. This book is divided into clear bite-size chunks so you can learn at your own pace and focus on the areas of most interest to ...
K 42
Khrais, Hussam.
Python for Offensive PenTest : : a practical guide to ethical hacking and penetration testing using Python. / Hussam. Khrais. - Birmingham : : Packt Publishing,, 2018. - 1 online resource (169 pages). - URL: https://library.dvfu.ru/lib/document/SK_ELIB/B2BB1BDE-CE30-4594-8811-BBA0EC1E08C4. - ISBN 9781788832465 (electronic bk.). - ISBN 1788832469 (electronic bk.)
Print version record.
Параллельные издания: Print version: : Khrais, Hussam. Python for Offensive PenTest : A practical guide to ethical hacking and penetration testing using Python. - Birmingham : Packt Publishing, ©2018
Содержание:
Cover; Title Page; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Warming up -- Your First Antivirus-Free Persistence Shell; Preparing the attacker machine; Setting up internet access; Preparing the target machine; TCP reverse shell; Coding a TCP reverse shell; Server side; Client side; Data exfiltration -- TCP; Server side; Client side; Exporting to EXE; HTTP reverse shell; Coding the HTTP reverse shell; Server side; Client side; Data exfiltration -- HTTP; Client side; Server side; Exporting to EXE; Persistence; Making putty.exe persistent.
Making a persistent HTTP reverse shellTuning the connection attempts; Tips for preventing a shell breakdown; Countermeasures; Summary; Chapter 2: Advanced Scriptable Shell; Dynamic DNS; DNS aware shell; Interacting with Twitter; Parsing a tweet in three lines; Countermeasures; Replicating Metasploit's screen capturing; Replicating Metasploit searching for content; Target directory navigation; Integrating low-level port scanner; Summary; Chapter 3: Password Hacking; Antivirus free keylogger; Installing pyHook and pywin; Adding code to keylogger; Hijacking KeePass password manager.
Man in the browserFirefox process; Firefox API hooking with Immunity Debugger; Python in Firefox proof of concept (PoC); Python in Firefox EXE; Dumping saved passwords out of Google Chrome; Acquiring the password remotely; Submitting the recovered password over HTTP session; Testing the file against antivirus; Password phishing -- DNS poisoning; Using Python script; Facebook password phishing; Countermeasures; Securing the online account; Securing your computer; Securing your network; Keeping a watch on any suspicious activity; Summary; Chapter 4: Catch Me If You Can!
Bypassing host-based firewallsHijacking IE; Bypassing reputation filtering in next generation firewalls; Interacting with SourceForge; Interacting with Google Forms; Bypassing botnet filtering; Bypassing IPS with handmade XOR encryption; Summary; Chapter 5: Miscellaneous Fun in Windows; Privilege escalation -- weak service file; Privilege escalation -- preparing vulnerable software; Privilege escalation -- backdooring legitimate windows service; Privilege escalation -- creating a new admin account and covering the tracks; Summary; Chapter 6: Abuse of Cryptography by Malware.
Introduction to encryption algorithmsProtecting your tunnel with AES -- stream mode; Cipher Block Chaining (CBC) mode encryption; Counter (CTR) mode encryption ; Protecting your tunnel with RSA; Hybrid encryption key; Summary; Other Books You May Enjoy; Index.
Рубрики: Python (Computer program language)
Penetration testing (Computer security)
Application software--Testing.
COMPUTERS / Programming Languages / Python.
COMPUTERS / Software Development & Engineering / Quality Assurance & Testing.
Application software--Testing.
Аннотация: Python is an easy-to-learn and cross-platform programming language which has unlimited third-party libraries. Plenty of open source hacking tools are written in Python and can be easily integrated within your script. This book is divided into clear bite-size chunks so you can learn at your own pace and focus on the areas of most interest to ...
2.
Подробнее
O-81
Ortega, José Manuel.
Mastering Python for networking and security [[electronic resource] :] : leverage Python scripts and libraries to overcome networking and security issues. / José Manuel. Ortega. - Birmingham : : Packt,, 2018. - 1 online resource (415 p.). - URL: https://library.dvfu.ru/lib/document/SK_ELIB/7732BE2C-1FD2-442F-AB9E-7A221315726E. - ISBN 9781788990707 (electronic bk.). - ISBN 1788990706 (electronic bk.)
Online resource; title from PDF title page (EBSCO, viewed October 17, 2018)
~РУБ DDC 005.13/3
Рубрики: Python (Computer program language)
COMPUTERS / Programming Languages / Python.
COMPUTERS / Security / General.
O-81
Ortega, José Manuel.
Mastering Python for networking and security [[electronic resource] :] : leverage Python scripts and libraries to overcome networking and security issues. / José Manuel. Ortega. - Birmingham : : Packt,, 2018. - 1 online resource (415 p.). - URL: https://library.dvfu.ru/lib/document/SK_ELIB/7732BE2C-1FD2-442F-AB9E-7A221315726E. - ISBN 9781788990707 (electronic bk.). - ISBN 1788990706 (electronic bk.)
Online resource; title from PDF title page (EBSCO, viewed October 17, 2018)
Рубрики: Python (Computer program language)
COMPUTERS / Programming Languages / Python.
COMPUTERS / Security / General.
3.
Подробнее
M 35
Marvin, Ryan.
Python Fundamentals : : a Practical Guide for Learning Python, Complete with Real-World Projects for You to Explore / / Ryan Marvin, Mark Ng'ang'a, Amos Omondi. - Birmingham : : Packt Publishing Ltd,, 2018. - 1 online resource (324 pages). - URL: https://library.dvfu.ru/lib/document/SK_ELIB/2C973D9D-9936-4E38-B7A9-DF3BECC89ADC. - ISBN 1789809940 (electronic bk.). - ISBN 9781789809947 (electronic bk.)
Activity 19: Function Arguments. Print version record.
Параллельные издания: Print version: : Marvin, Ryan. Python Fundamentals : A Practical Guide for Learning Python, Complete with Real-World Projects for You to Explore. - Birmingham : Packt Publishing Ltd, ©2018. - ISBN 9781789807325
Содержание:
Intro; Preface; Introducing Python; Introduction; Python 2 Versus Python 3; Working with the Python Interactive Shell; Exercise 1: Checking our Python Installation; Exercise 2: Working with the Python Interpreter; Activity 1: Working with the Python Shell; Writing and Running Simple Scripts; Exercise 3: Creating a Script; Running a File Containing Invalid Commands; Exercise 4: Passing User Arguments to Scripts; Activity 2: Running Simple Python Scripts; Python Syntax; Variables; Values; Exercise 5: Checking the Type of a Value; Type Conversion; Exercise 6: Assigning Variables.
Exercise 7: Using VariablesMultiple Assignment; Activity 3: Using Variables and Assign Statements; Naming Identifiers and Reserved Words; Exercise 8: Python Keywords; Python Naming Conventions; Activity 4: Variable Assignment and Variable Naming Conventions; User Input, Comments, and Indentations; User Input from the Keyboard; Passing in a Prompt to the input Function; Using Different Input Data Types in your Program; Exercise 9: Fetching and Using User Input; Comments; Indentation; Exercise 10: The Importance of Proper Indentation; Activity 5: Fixing Indentations in a Code Block.
Activity 6: Implementing User Input and Comments in a ScriptSummary; Data Types; Introduction; Numerical Data; Types of Numbers; Exercise 11: Converting Between Different Types of Number Systems; Operators; Order of Operations; Activity 7: Order of Operations; Activity 8: Using Different Arithmetic Operators; Strings; String Operations and Methods; Indexing; Slicing; Activity 9: String Slicing; Length; String Formatting; String Methods; Activity 10: Working with Strings; Escape Sequences; Exercise 12: Using Escape Sequences; Activity 11: Manipulating Strings; Lists; List Operations.
Exercise 13: List ReferencesActivity 12: Working with Lists; Booleans; Comparison Operators; Logical Operators; Membership Operators; Activity 13: Using Boolean Operators; Summary; Control Statements; Introduction; Control Statements; Program Flow; Control Statement; The if Statement; Exercise 14: Using the if Statement; Activity 14: Working with the if Statement; The while Statement; Exercise 15: Using the while Statement; Exercise 16: Using while to Keep a Program Running; Activity 15: Working with the while Statement; while Versus if; Loops; The for Loop; Exercise 17: Using the for Loop.
Using elseThe range Function; Activity 16: The for loop and the range Function; Nesting Loops; Exercise 18: Using Nested Loops; Activity 17: Nested Loops; Breaking Out of Loops; The break Statement; The continue Statement; The pass Statement; Activity 18: Breaking out of Loops; Summary; Functions; Introduction; Built-In Functions; User-Defined Functions; Calling a Function; Global and Local Variables; Exercise 19: Defining Global and Local Variables; Function Return; Using main(); Function Arguments; Required Arguments; Keyword Arguments; Default Arguments; Variable Number of Arguments.
~РУБ DDC 005.133
Рубрики: Python (Computer program language)
Python (Computer program language)
Аннотация: Python Fundamentals takes you from zero experience to a complete understanding of key concepts, edge cases, and using Python for real-world application development. You'll move progressively from the basics to working with larger complex applications. After completing this book, you'll have the skills you need to dive into an existing ...
Доп.точки доступа:
Ng'ang'a, Mark.
Omondi, Amos R.
M 35
Marvin, Ryan.
Python Fundamentals : : a Practical Guide for Learning Python, Complete with Real-World Projects for You to Explore / / Ryan Marvin, Mark Ng'ang'a, Amos Omondi. - Birmingham : : Packt Publishing Ltd,, 2018. - 1 online resource (324 pages). - URL: https://library.dvfu.ru/lib/document/SK_ELIB/2C973D9D-9936-4E38-B7A9-DF3BECC89ADC. - ISBN 1789809940 (electronic bk.). - ISBN 9781789809947 (electronic bk.)
Activity 19: Function Arguments. Print version record.
Параллельные издания: Print version: : Marvin, Ryan. Python Fundamentals : A Practical Guide for Learning Python, Complete with Real-World Projects for You to Explore. - Birmingham : Packt Publishing Ltd, ©2018. - ISBN 9781789807325
Содержание:
Intro; Preface; Introducing Python; Introduction; Python 2 Versus Python 3; Working with the Python Interactive Shell; Exercise 1: Checking our Python Installation; Exercise 2: Working with the Python Interpreter; Activity 1: Working with the Python Shell; Writing and Running Simple Scripts; Exercise 3: Creating a Script; Running a File Containing Invalid Commands; Exercise 4: Passing User Arguments to Scripts; Activity 2: Running Simple Python Scripts; Python Syntax; Variables; Values; Exercise 5: Checking the Type of a Value; Type Conversion; Exercise 6: Assigning Variables.
Exercise 7: Using VariablesMultiple Assignment; Activity 3: Using Variables and Assign Statements; Naming Identifiers and Reserved Words; Exercise 8: Python Keywords; Python Naming Conventions; Activity 4: Variable Assignment and Variable Naming Conventions; User Input, Comments, and Indentations; User Input from the Keyboard; Passing in a Prompt to the input Function; Using Different Input Data Types in your Program; Exercise 9: Fetching and Using User Input; Comments; Indentation; Exercise 10: The Importance of Proper Indentation; Activity 5: Fixing Indentations in a Code Block.
Activity 6: Implementing User Input and Comments in a ScriptSummary; Data Types; Introduction; Numerical Data; Types of Numbers; Exercise 11: Converting Between Different Types of Number Systems; Operators; Order of Operations; Activity 7: Order of Operations; Activity 8: Using Different Arithmetic Operators; Strings; String Operations and Methods; Indexing; Slicing; Activity 9: String Slicing; Length; String Formatting; String Methods; Activity 10: Working with Strings; Escape Sequences; Exercise 12: Using Escape Sequences; Activity 11: Manipulating Strings; Lists; List Operations.
Exercise 13: List ReferencesActivity 12: Working with Lists; Booleans; Comparison Operators; Logical Operators; Membership Operators; Activity 13: Using Boolean Operators; Summary; Control Statements; Introduction; Control Statements; Program Flow; Control Statement; The if Statement; Exercise 14: Using the if Statement; Activity 14: Working with the if Statement; The while Statement; Exercise 15: Using the while Statement; Exercise 16: Using while to Keep a Program Running; Activity 15: Working with the while Statement; while Versus if; Loops; The for Loop; Exercise 17: Using the for Loop.
Using elseThe range Function; Activity 16: The for loop and the range Function; Nesting Loops; Exercise 18: Using Nested Loops; Activity 17: Nested Loops; Breaking Out of Loops; The break Statement; The continue Statement; The pass Statement; Activity 18: Breaking out of Loops; Summary; Functions; Introduction; Built-In Functions; User-Defined Functions; Calling a Function; Global and Local Variables; Exercise 19: Defining Global and Local Variables; Function Return; Using main(); Function Arguments; Required Arguments; Keyword Arguments; Default Arguments; Variable Number of Arguments.
Рубрики: Python (Computer program language)
Python (Computer program language)
Аннотация: Python Fundamentals takes you from zero experience to a complete understanding of key concepts, edge cases, and using Python for real-world application development. You'll move progressively from the basics to working with larger complex applications. After completing this book, you'll have the skills you need to dive into an existing ...
Доп.точки доступа:
Ng'ang'a, Mark.
Omondi, Amos R.
4.
Подробнее
S 17
Saleh, Hyatt,.
Machine learning fundamentals : : Use Python and scikit-learn to get up and running with the hottest developments in machine learning / / Hyatt Saleh. - Birmingham : : Packt Publishing,, ©2018. - 1 online resource (240 p.). - URL: https://library.dvfu.ru/lib/document/SK_ELIB/CE373E47-94E7-44E9-B874-2FCA503EFF44. - ISBN 1789801761 (electronic bk.). - ISBN 9781789801767 (electronic bk.)
Description based upon print version of record. Supervised Learning Algorithms: Predict Annual Income
Параллельные издания: Print version: : Saleh, Hyatt Machine Learning Fundamentals : Use Python and Scikit-Learn to Get up and Running with the Hottest Developments in Machine Learning. - Birmingham : Packt Publishing Ltd,c2018. - ISBN 9781789803556
Содержание:
Intro; Preface; Introduction to Scikit-Learn; Introduction; Scikit-Learn; Advantages of Scikit-Learn; Disadvantages of Scikit-Learn; Data Representation; Tables of Data; Features and Target Matrices; Exercise 1: Loading a Sample Dataset and Creating the Features and Target Matrices; Activity 1: Selecting a Target Feature and Creating a Target Matrix; Data Preprocessing; Messy Data; Exercise 2: Dealing with Messy Data; Dealing with Categorical Features; Exercise 3: Applying Feature Engineering over Text Data; Rescaling Data; Exercise 4: Normalizing and Standardizing Data
Activity 2: Preprocessing an Entire DatasetScikit-Learn API; How Does It Work?; Supervised and Unsupervised Learning; Supervised Learning; Unsupervised Learning; Summary; Unsupervised Learning: Real-Life Applications; Introduction; Clustering; Clustering Types; Applications of Clustering; Exploring a Dataset: Wholesale Customers Dataset; Understanding the Dataset; Data Visualization; Loading the Dataset Using Pandas; Visualization Tools; Exercise 5: Plotting a Histogram of One Feature from the Noisy Circles Dataset; Activity 3: Using Data Visualization to Aid the Preprocessing Process
K-means AlgorithmUnderstanding the Algorithm; Exercise 6: Importing and Training the k-means Algorithm over a Dataset; Activity 4: Applying the k-means Algorithm to a Dataset; Mean-Shift Algorithm; Understanding the Algorithm; Exercise 7: Importing and Training the Mean-Shift Algorithm over a Dataset; Activity 5: Applying the Mean-Shift Algorithm to a Dataset; DBSCAN Algorithm; Understanding the Algorithm; Exercise 8: Importing and Training the DBSCAN Algorithm over a Dataset; Activity 6: Applying the DBSCAN Algorithm to the Dataset; Evaluating the Performance of Clusters
Available Metrics in Scikit-LearnExercise 9: Evaluating the Silhouette Coefficient Score and Calinski-Harabasz Index; Activity 7: Measuring and Comparing the Performance of the Algorithms; Summary; Supervised Learning: Key Steps; Introduction; Model Validation and Testing; Data Partition; Split Ratio; Exercise 10: Performing Data Partition over a Sample Dataset; Cross Validation; Exercise 11: Using Cross-Validation to Partition the Train Set into a Training and a Validation Set; Activity 8: Data Partition over a Handwritten Digit Dataset; Evaluation Metrics
Evaluation Metrics for Classification TasksExercise 12: Calculating Different Evaluation Metrics over a Classification Task; Choosing an Evaluation Metric; Evaluation Metrics for Regression Tasks; Exercise 13: Calculating Evaluation Metrics over a Regression Task; Activity 9: Evaluating the Performance of the Model Trained over a Handwritten Dataset; Error Analysis; Bias, Variance, and Data Mismatch; Exercise 14: Calculating the Error Rate over Different Sets of Data; Activity 10: Performing Error Analysis over a Model Trained to Recognize Handwritten Digits; Summary
~РУБ DDC 005.133
Рубрики: Python (Computer program language)
Machine learning.
Artificial intelligence.
COMPUTERS / Programming Languages / Python.
Аннотация: As machine learning algorithms become popular, new tools that optimize these algorithms are also developed. Machine Learning Fundamentals explains the scikit-learn API, which is a package created to facilitate the process of building machine learning applications. By explaining the differences between supervised and unsupervised models and by ...
S 17
Saleh, Hyatt,.
Machine learning fundamentals : : Use Python and scikit-learn to get up and running with the hottest developments in machine learning / / Hyatt Saleh. - Birmingham : : Packt Publishing,, ©2018. - 1 online resource (240 p.). - URL: https://library.dvfu.ru/lib/document/SK_ELIB/CE373E47-94E7-44E9-B874-2FCA503EFF44. - ISBN 1789801761 (electronic bk.). - ISBN 9781789801767 (electronic bk.)
Description based upon print version of record. Supervised Learning Algorithms: Predict Annual Income
Параллельные издания: Print version: : Saleh, Hyatt Machine Learning Fundamentals : Use Python and Scikit-Learn to Get up and Running with the Hottest Developments in Machine Learning. - Birmingham : Packt Publishing Ltd,c2018. - ISBN 9781789803556
Содержание:
Intro; Preface; Introduction to Scikit-Learn; Introduction; Scikit-Learn; Advantages of Scikit-Learn; Disadvantages of Scikit-Learn; Data Representation; Tables of Data; Features and Target Matrices; Exercise 1: Loading a Sample Dataset and Creating the Features and Target Matrices; Activity 1: Selecting a Target Feature and Creating a Target Matrix; Data Preprocessing; Messy Data; Exercise 2: Dealing with Messy Data; Dealing with Categorical Features; Exercise 3: Applying Feature Engineering over Text Data; Rescaling Data; Exercise 4: Normalizing and Standardizing Data
Activity 2: Preprocessing an Entire DatasetScikit-Learn API; How Does It Work?; Supervised and Unsupervised Learning; Supervised Learning; Unsupervised Learning; Summary; Unsupervised Learning: Real-Life Applications; Introduction; Clustering; Clustering Types; Applications of Clustering; Exploring a Dataset: Wholesale Customers Dataset; Understanding the Dataset; Data Visualization; Loading the Dataset Using Pandas; Visualization Tools; Exercise 5: Plotting a Histogram of One Feature from the Noisy Circles Dataset; Activity 3: Using Data Visualization to Aid the Preprocessing Process
K-means AlgorithmUnderstanding the Algorithm; Exercise 6: Importing and Training the k-means Algorithm over a Dataset; Activity 4: Applying the k-means Algorithm to a Dataset; Mean-Shift Algorithm; Understanding the Algorithm; Exercise 7: Importing and Training the Mean-Shift Algorithm over a Dataset; Activity 5: Applying the Mean-Shift Algorithm to a Dataset; DBSCAN Algorithm; Understanding the Algorithm; Exercise 8: Importing and Training the DBSCAN Algorithm over a Dataset; Activity 6: Applying the DBSCAN Algorithm to the Dataset; Evaluating the Performance of Clusters
Available Metrics in Scikit-LearnExercise 9: Evaluating the Silhouette Coefficient Score and Calinski-Harabasz Index; Activity 7: Measuring and Comparing the Performance of the Algorithms; Summary; Supervised Learning: Key Steps; Introduction; Model Validation and Testing; Data Partition; Split Ratio; Exercise 10: Performing Data Partition over a Sample Dataset; Cross Validation; Exercise 11: Using Cross-Validation to Partition the Train Set into a Training and a Validation Set; Activity 8: Data Partition over a Handwritten Digit Dataset; Evaluation Metrics
Evaluation Metrics for Classification TasksExercise 12: Calculating Different Evaluation Metrics over a Classification Task; Choosing an Evaluation Metric; Evaluation Metrics for Regression Tasks; Exercise 13: Calculating Evaluation Metrics over a Regression Task; Activity 9: Evaluating the Performance of the Model Trained over a Handwritten Dataset; Error Analysis; Bias, Variance, and Data Mismatch; Exercise 14: Calculating the Error Rate over Different Sets of Data; Activity 10: Performing Error Analysis over a Model Trained to Recognize Handwritten Digits; Summary
Рубрики: Python (Computer program language)
Machine learning.
Artificial intelligence.
COMPUTERS / Programming Languages / Python.
Аннотация: As machine learning algorithms become popular, new tools that optimize these algorithms are also developed. Machine Learning Fundamentals explains the scikit-learn API, which is a package created to facilitate the process of building machine learning applications. By explaining the differences between supervised and unsupervised models and by ...
5.
Подробнее
D 50
Dey, Sandipan,.
Hands-on image processing with Python : : expert techniques for advanced image analysis and effective interpretation of image data / / Sandipan Dey. - Birmingham, UK : : Packt Publishing,, 2018. - 1 online resource (1 volume) : : il. - Includes bibliographical references. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/72DBFB5B-5E81-4D50-AD8B-BA1D2CDF0E77. - ISBN 178934185X. - ISBN 9781789341850 (electronic bk.)
Description based on online resource; title from title page (Safari, viewed February 1, 2019).
Параллельные издания: Print version: :
~РУБ DDC 621.367
Рубрики: Image processing.
Python (Computer program language)
Computer vision.
Machine learning.
Computer vision.
Image processing.
Machine learning.
Python (Computer program language)
TECHNOLOGY & ENGINEERING / Mechanical.
D 50
Dey, Sandipan,.
Hands-on image processing with Python : : expert techniques for advanced image analysis and effective interpretation of image data / / Sandipan Dey. - Birmingham, UK : : Packt Publishing,, 2018. - 1 online resource (1 volume) : : il. - Includes bibliographical references. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/72DBFB5B-5E81-4D50-AD8B-BA1D2CDF0E77. - ISBN 178934185X. - ISBN 9781789341850 (electronic bk.)
Description based on online resource; title from title page (Safari, viewed February 1, 2019).
Параллельные издания: Print version: :
Рубрики: Image processing.
Python (Computer program language)
Computer vision.
Machine learning.
Computer vision.
Image processing.
Machine learning.
Python (Computer program language)
TECHNOLOGY & ENGINEERING / Mechanical.
6.
Подробнее
L 82
Liu, Yuxi (Hayden),.
Python machine learning by example : : easy-to-follow examples that get you up and running with machine learning / / Yuxi (Hayden) Liu. - Second edition. - Birmingham, UK : : Packt Publishing,, 2019. - 1 online resource. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/12AC60A3-C325-4FFA-96EE-2BEFDAB954BA. - ISBN 9781789617559 (electronic bk.). - ISBN 1789617553 (electronic bk.)
Online resource; title from PDF title page (EBSCO, viewed April 8, 2019)
~РУБ DDC 005.133
Рубрики: Python (Computer program language)
Machine learning.
COMPUTERS / Programming Languages / Python.
COMPUTERS / Data Processing.
COMPUTERS / Databases / Data Mining.
L 82
Liu, Yuxi (Hayden),.
Python machine learning by example : : easy-to-follow examples that get you up and running with machine learning / / Yuxi (Hayden) Liu. - Second edition. - Birmingham, UK : : Packt Publishing,, 2019. - 1 online resource. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/12AC60A3-C325-4FFA-96EE-2BEFDAB954BA. - ISBN 9781789617559 (electronic bk.). - ISBN 1789617553 (electronic bk.)
Online resource; title from PDF title page (EBSCO, viewed April 8, 2019)
Рубрики: Python (Computer program language)
Machine learning.
COMPUTERS / Programming Languages / Python.
COMPUTERS / Data Processing.
COMPUTERS / Databases / Data Mining.
7.
Подробнее
B 64
Blanchard, Tommy,.
Data science for marketing analytics / / Tommy Blanchard, Debasish Behera, Pranshu Bhatnagar. - Birmingham, UK : : Packt Publishing,, 2019. - 1 online resource (1 volume) : : il. - Includes bibliographical references. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/3DB5D77C-C251-4493-8806-F32E4AF8633A. - ISBN 9781789952100 (electronic bk.). - ISBN 1789952107 (electronic bk.)
Description based on online resource; title from copyright page (Safari, viewed May 15, 2019).
~РУБ DDC 658.830285
Рубрики: Marketing--Data processing.
Marketing research.
Python (Computer program language)
Information visualization.
BUSINESS & ECONOMICS / Industrial Management
BUSINESS & ECONOMICS / Management
BUSINESS & ECONOMICS / Management Science
BUSINESS & ECONOMICS / Organizational Behavior
Аннотация: Explore new and more sophisticated tools that reduce your marketing analytics efforts and give you precise results Key Features Study new techniques for marketing analytics Explore uses of machine learning to power your marketing analyses Work through each stage of data analytics with the help of multiple examples and exercises Book Description Data Science for Marketing Analytics covers every stage of data analytics, from working with a raw dataset to segmenting a population and modeling different parts of the population based on the segments. The book starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation. As you make your way through the chapters, you'll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding chapters, you'll gain an understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modeling customer product choices. By the end of this book, you will be able to build your own marketing reporting and interactive dashboard solutions. What you will learn Analyze and visualize data in Python using pandas and Matplotlib Study clustering techniques, such as hierarchical and k-means clustering Create customer segments based on manipulated data Predict customer lifetime value using linear regression Use classification algorithms to understand customer choice Optimize classification algorithms to extract maximal information Who this book is for Data Science for Marketing Analytics is designed for developers and marketing analysts looking to use new, more sophisticated tools in their marketing analytics efforts. It'll help if you have prior experience of coding in Python and knowledge of high school level mathematics. Some experience with databases, Excel, statistics, or Tableau is useful but not necessary. Downloading the example code for this book You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased ...
Доп.точки доступа:
Bhatnagar, Pranshu, \author.\
Behera, Debasish, \author.\
B 64
Blanchard, Tommy,.
Data science for marketing analytics / / Tommy Blanchard, Debasish Behera, Pranshu Bhatnagar. - Birmingham, UK : : Packt Publishing,, 2019. - 1 online resource (1 volume) : : il. - Includes bibliographical references. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/3DB5D77C-C251-4493-8806-F32E4AF8633A. - ISBN 9781789952100 (electronic bk.). - ISBN 1789952107 (electronic bk.)
Description based on online resource; title from copyright page (Safari, viewed May 15, 2019).
Рубрики: Marketing--Data processing.
Marketing research.
Python (Computer program language)
Information visualization.
BUSINESS & ECONOMICS / Industrial Management
BUSINESS & ECONOMICS / Management
BUSINESS & ECONOMICS / Management Science
BUSINESS & ECONOMICS / Organizational Behavior
Аннотация: Explore new and more sophisticated tools that reduce your marketing analytics efforts and give you precise results Key Features Study new techniques for marketing analytics Explore uses of machine learning to power your marketing analyses Work through each stage of data analytics with the help of multiple examples and exercises Book Description Data Science for Marketing Analytics covers every stage of data analytics, from working with a raw dataset to segmenting a population and modeling different parts of the population based on the segments. The book starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation. As you make your way through the chapters, you'll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding chapters, you'll gain an understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modeling customer product choices. By the end of this book, you will be able to build your own marketing reporting and interactive dashboard solutions. What you will learn Analyze and visualize data in Python using pandas and Matplotlib Study clustering techniques, such as hierarchical and k-means clustering Create customer segments based on manipulated data Predict customer lifetime value using linear regression Use classification algorithms to understand customer choice Optimize classification algorithms to extract maximal information Who this book is for Data Science for Marketing Analytics is designed for developers and marketing analysts looking to use new, more sophisticated tools in their marketing analytics efforts. It'll help if you have prior experience of coding in Python and knowledge of high school level mathematics. Some experience with databases, Excel, statistics, or Tableau is useful but not necessary. Downloading the example code for this book You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased ...
Доп.точки доступа:
Bhatnagar, Pranshu, \author.\
Behera, Debasish, \author.\
8.
Подробнее
M 30
Marin, Ivan.
Big data analysis with Python : : combine Spark and Python to unlock the powers of parallel computing and machine learning / / Ivan Marin, Ankit Shukla and Sarang VK. - Birmingham, UK : : Packt Publishing,, ©2019. - 1 online resource (276 pages). - URL: https://library.dvfu.ru/lib/document/SK_ELIB/C3233AC3-74EA-4C2D-A64D-76AE7AE62C11. - ISBN 1789950732 (electronic book). - ISBN 9781789950731 (electronic book)
Description based on online resource; title from digital title page (viewed on January 06, 2020).
Параллельные издания: Print version: : Marin, Ivan. Big Data Analysis with Python : Combine Spark and Python to Unlock the Powers of Parallel Computing and Machine Learning. - Birmingham : Packt Publishing Ltd, ©2019. - ISBN 9781789955286
Содержание:
Chapter 1: The Python Data Science Stack -- Chapter 2: Statistical Visualizations -- Chapter 3: Working with Big Data Frameworks -- Chapter 4: Diving Deeper with Spark -- Chapter 5: Handling Missing Values and Correlation Analysis -- Chapter 6: Exploratory Data Analysis -- Chapter 7: Reproducibility in Big Data Analysis -- Chapter 8: Creating a Full Analysis Report
~РУБ DDC 005.7
Рубрики: Big data.
Python (Computer program language)
Cloud computing.
Machine learning.
Big data.
Cloud computing.
Machine learning.
Python (Computer program language)
Аннотация: Processing big data in real time is challenging due to scalability, information inconsistency, and fault tolerance. Big Data Analysis with Python teaches you how to use tools that can control the data avalanche for you. With this book, you'll learn effective techniques to aggregate data into useful dimensions for posterior analysis, extract ...
Доп.точки доступа:
Shukla, Ankit.
VK, Sarang.
M 30
Marin, Ivan.
Big data analysis with Python : : combine Spark and Python to unlock the powers of parallel computing and machine learning / / Ivan Marin, Ankit Shukla and Sarang VK. - Birmingham, UK : : Packt Publishing,, ©2019. - 1 online resource (276 pages). - URL: https://library.dvfu.ru/lib/document/SK_ELIB/C3233AC3-74EA-4C2D-A64D-76AE7AE62C11. - ISBN 1789950732 (electronic book). - ISBN 9781789950731 (electronic book)
Description based on online resource; title from digital title page (viewed on January 06, 2020).
Параллельные издания: Print version: : Marin, Ivan. Big Data Analysis with Python : Combine Spark and Python to Unlock the Powers of Parallel Computing and Machine Learning. - Birmingham : Packt Publishing Ltd, ©2019. - ISBN 9781789955286
Содержание:
Chapter 1: The Python Data Science Stack -- Chapter 2: Statistical Visualizations -- Chapter 3: Working with Big Data Frameworks -- Chapter 4: Diving Deeper with Spark -- Chapter 5: Handling Missing Values and Correlation Analysis -- Chapter 6: Exploratory Data Analysis -- Chapter 7: Reproducibility in Big Data Analysis -- Chapter 8: Creating a Full Analysis Report
Рубрики: Big data.
Python (Computer program language)
Cloud computing.
Machine learning.
Big data.
Cloud computing.
Machine learning.
Python (Computer program language)
Аннотация: Processing big data in real time is challenging due to scalability, information inconsistency, and fault tolerance. Big Data Analysis with Python teaches you how to use tools that can control the data avalanche for you. With this book, you'll learn effective techniques to aggregate data into useful dimensions for posterior analysis, extract ...
Доп.точки доступа:
Shukla, Ankit.
VK, Sarang.
9.
Подробнее
B 61
Bird, Andrew.
The the Python Workshop [[electronic resource] :] : A Practical, No-Nonsense Introduction to Python Development. / Andrew. Bird, Han, Lau Cher., Jiménez, Mario Corchero., Lee, Graham., Wade, Corey. - Birmingham : : Packt Publishing, Limited,, 2019. - 1 online resource (606 p.). - URL: https://library.dvfu.ru/lib/document/SK_ELIB/6726E9A2-50AF-45A8-AA31-F0ECA23CA6BF. - ISBN 1838984534. - ISBN 9781838984533 (electronic bk.)
Description based upon print version of record.
Параллельные издания: Print version: : Bird, Andrew The the Python Workshop : A Practical, No-Nonsense Introduction to Python Development. - Birmingham : Packt Publishing, Limited,c2019. - ISBN 9781839218859
~РУБ DDC 005.133
Рубрики: Python (Computer program language)
Аннотация: Cut through the noise and get real results with a step-by-step approach to learning Python 3.X programming.
Доп.точки доступа:
Han, Lau Cher.
Jiménez, Mario Corchero.
Lee, Graham.
Wade, Corey.
B 61
Bird, Andrew.
The the Python Workshop [[electronic resource] :] : A Practical, No-Nonsense Introduction to Python Development. / Andrew. Bird, Han, Lau Cher., Jiménez, Mario Corchero., Lee, Graham., Wade, Corey. - Birmingham : : Packt Publishing, Limited,, 2019. - 1 online resource (606 p.). - URL: https://library.dvfu.ru/lib/document/SK_ELIB/6726E9A2-50AF-45A8-AA31-F0ECA23CA6BF. - ISBN 1838984534. - ISBN 9781838984533 (electronic bk.)
Description based upon print version of record.
Параллельные издания: Print version: : Bird, Andrew The the Python Workshop : A Practical, No-Nonsense Introduction to Python Development. - Birmingham : Packt Publishing, Limited,c2019. - ISBN 9781839218859
Рубрики: Python (Computer program language)
Аннотация: Cut through the noise and get real results with a step-by-step approach to learning Python 3.X programming.
Доп.точки доступа:
Han, Lau Cher.
Jiménez, Mario Corchero.
Lee, Graham.
Wade, Corey.
10.
Подробнее
S 43
SEBASTIAN, RASCHKA;VAHID MIRJALILI.
PYTHON MACHINE LEARNING;MACHINE LEARNING AND DEEP LEARNING WITH PYTHON, SCIKIT-LEARN, AND TENSORFLOW 2, 3RD EDITION [[electronic resource].] / RASCHKA;VAHID MIRJALILI. SEBASTIAN. - [Б. м.] : PACKT PUBLISHING,, 2019. - 1 online resource. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/FD8E7055-AA80-49B3-8726-679A8945BA83. - ISBN 1789958296 (electronic bk.). - ISBN 9781789958294 (electronic bk.)
~РУБ DDC 005.133
Рубрики: Python (Computer program language)
Machine learning.
S 43
SEBASTIAN, RASCHKA;VAHID MIRJALILI.
PYTHON MACHINE LEARNING;MACHINE LEARNING AND DEEP LEARNING WITH PYTHON, SCIKIT-LEARN, AND TENSORFLOW 2, 3RD EDITION [[electronic resource].] / RASCHKA;VAHID MIRJALILI. SEBASTIAN. - [Б. м.] : PACKT PUBLISHING,, 2019. - 1 online resource. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/FD8E7055-AA80-49B3-8726-679A8945BA83. - ISBN 1789958296 (electronic bk.). - ISBN 9781789958294 (electronic bk.)
Рубрики: Python (Computer program language)
Machine learning.
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