Einf hrung in Python

Einf  hrung in Python Author Mark Lutz
ISBN-10 9783897214880
Release 2007
Pages 624
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Einf hrung in Python has been writing in one form or another for most of life. You can find so many inspiration from Einf hrung in Python also informative, and entertaining. Click DOWNLOAD or Read Online button to get full Einf hrung in Python book for free.



Machine Learning mit Python

Machine Learning mit Python Author Sebastian Raschka
ISBN-10 9783958454248
Release 2016-11-22
Pages 424
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Machine Learning mit Python has been writing in one form or another for most of life. You can find so many inspiration from Machine Learning mit Python also informative, and entertaining. Click DOWNLOAD or Read Online button to get full Machine Learning mit Python book for free.



Learning Python

Learning Python Author Mark Lutz
ISBN-10 9781449355692
Release 2013-06-12
Pages 1600
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Get a comprehensive, in-depth introduction to the core Python language with this hands-on book. Based on author Mark Lutz’s popular training course, this updated fifth edition will help you quickly write efficient, high-quality code with Python. It’s an ideal way to begin, whether you’re new to programming or a professional developer versed in other languages. Complete with quizzes, exercises, and helpful illustrations, this easy-to-follow, self-paced tutorial gets you started with both Python 2.7 and 3.3— the latest releases in the 3.X and 2.X lines—plus all other releases in common use today. You’ll also learn some advanced language features that recently have become more common in Python code. Explore Python’s major built-in object types such as numbers, lists, and dictionaries Create and process objects with Python statements, and learn Python’s general syntax model Use functions to avoid code redundancy and package code for reuse Organize statements, functions, and other tools into larger components with modules Dive into classes: Python’s object-oriented programming tool for structuring code Write large programs with Python’s exception-handling model and development tools Learn advanced Python tools, including decorators, descriptors, metaclasses, and Unicode processing



Einf hrung in Machine Learning mit Python

Einf  hrung in Machine Learning mit Python Author Sarah Guido
ISBN-10 9783960101123
Release 2017-07-21
Pages 378
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Machine Learning ist zu einem wichtigen Bestandteil vieler kommerzieller Anwendungen und Forschungsprojekte geworden, von der medizinischen Diagnostik bis hin zur Suche nach Freunden in sozialen Netzwerken. Um Machine-Learning-Anwendungen zu entwickeln, braucht es keine großen Expertenteams: Wenn Sie Python-Grundkenntnisse mitbringen, zeigt Ihnen dieses Praxisbuch, wie Sie Ihre eigenen Machine-Learning-Lösungen erstellen. Mit Python und der scikit-learn-Bibliothek erarbeiten Sie sich alle Schritte, die für eine erfolgreiche Machine-Learning-Anwendung notwendig sind. Die Autoren Andreas Müller und Sarah Guido konzentrieren sich bei der Verwendung von Machine-Learning-Algorithmen auf die praktischen Aspekte statt auf die Mathematik dahinter. Wenn Sie zusätzlich mit den Bibliotheken NumPy und matplotlib vertraut sind, hilft Ihnen dies, noch mehr aus diesem Tutorial herauszuholen. Das Buch zeigt Ihnen: - grundlegende Konzepte und Anwendungen von Machine Learning - Vor- und Nachteile weit verbreiteter maschineller Lernalgorithmen - wie sich die von Machine Learning verarbeiteten Daten repräsentieren lassen und auf welche Aspekte der Daten Sie sich konzentrieren sollten - fortgeschrittene Methoden zur Auswertung von Modellen und zum Optimieren von Parametern - das Konzept von Pipelines, mit denen Modelle verkettet und Arbeitsabläufe gekapselt werden - Arbeitsmethoden für Textdaten, insbesondere textspezifische Verarbeitungstechniken - Möglichkeiten zur Verbesserung Ihrer Fähigkeiten in den Bereichen Machine Learning und Data Science Dieses Buch ist eine fantastische, super praktische Informationsquelle für jeden, der mit Machine Learning in Python starten möchte – ich wünschte nur, es hätte schon existiert, als ich mit scikit-learn anfing! Hanna Wallach, Senior Researcher, Microsoft Research



Python Machine Learning

Python Machine Learning Author Sebastian Raschka
ISBN-10 9781783555147
Release 2015-09-23
Pages 454
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Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is For If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will Learn Explore how to use different machine learning models to ask different questions of your data Learn how to build neural networks using Keras and Theano Find out how to write clean and elegant Python code that will optimize the strength of your algorithms Discover how to embed your machine learning model in a web application for increased accessibility Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Organize data using effective pre-processing techniques Get to grips with sentiment analysis to delve deeper into textual and social media data In Detail Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. Style and approach Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.



Learning Python

Learning Python Author Fabrizio Romano
ISBN-10 9781785284571
Release 2015-12-24
Pages 442
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Learn to code like a professional with Python – an open source, versatile, and powerful programming language About This Book Learn the fundamentals of programming with Python – one of the best languages ever created Develop a strong set of programming skills that you will be able to express in any situation, on every platform, thanks to Python's portability Create outstanding applications of all kind, from websites to scripting, and from GUIs to data science Who This Book Is For Python is the most popular introductory teaching language in U.S. top computer science universities, so if you are new to software development, or maybe you have little experience, and would like to start off on the right foot, then this language and this book are what you need. Its amazing design and portability will help you become productive regardless of the environment you choose to work with. What You Will Learn Get Python up and running on Windows, Mac, and Linux in no time Grasp the fundamental concepts of coding, along with the basics of data structures and control flow. Write elegant, reusable, and efficient code in any situation Understand when to use the functional or the object oriented programming approach Create bulletproof, reliable software by writing tests to support your code Explore examples of GUIs, scripting, data science and web applications Learn to be independent, capable of fetching any resource you need, as well as dig deeper In Detail Learning Python has a dynamic and varied nature. It reads easily and lays a good foundation for those who are interested in digging deeper. It has a practical and example-oriented approach through which both the introductory and the advanced topics are explained. Starting with the fundamentals of programming and Python, it ends by exploring very different topics, like GUIs, web apps and data science. The book takes you all the way to creating a fully fledged application. The book begins by exploring the essentials of programming, data structures and teaches you how to manipulate them. It then moves on to controlling the flow of a program and writing reusable and error proof code. You will then explore different programming paradigms that will allow you to find the best approach to any situation, and also learn how to perform performance optimization as well as effective debugging. Throughout, the book steers you through the various types of applications, and it concludes with a complete mini website built upon all the concepts that you learned. Style and approach This book is an easy-to-follow guide that will take you from a novice to the proficient level at a comfortable pace, using a lot of simple but effective examples. Each topic is explained thoroughly, and pointers are left for the more inquisitive readers to dig deeper and expand their knowledge.



Learning Python with Raspberry Pi

Learning Python with Raspberry Pi Author Alex Bradbury
ISBN-10 9781118717035
Release 2014-02-11
Pages 288
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The must-have companion guide to the Raspberry Pi User Guide! Raspberry Pi chose Python as its teaching language of choice to encourage a new generation of programmers to learn how to program. This approachable book serves as an ideal resource for anyone wanting to use Raspberry Pi to learn to program and helps you get started with the Python programming language. Aimed at first-time developers with no prior programming language assumed, this beginner book gets you up and running. Covers variables, loops, and functions Addresses 3D graphics programming Walks you through programming Minecraft Zeroes in on Python for scripting Learning Python with Raspberry Pi proves itself to be a fantastic introduction to coding.



Learning Python for Forensics

Learning Python for Forensics Author Preston Miller
ISBN-10 9781783285242
Release 2016-05-31
Pages 488
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Learn the art of designing, developing, and deploying innovative forensic solutions through Python About This Book This practical guide will help you solve forensic dilemmas through the development of Python scripts Analyze Python scripts to extract metadata and investigate forensic artifacts Master the skills of parsing complex data structures by taking advantage of Python libraries Who This Book Is For If you are a forensics student, hobbyist, or professional that is seeking to increase your understanding in forensics through the use of a programming language, then this book is for you. You are not required to have previous experience in programming to learn and master the content within this book. This material, created by forensic professionals, was written with a unique perspective and understanding of examiners who wish to learn programming What You Will Learn Discover how to perform Python script development Update yourself by learning the best practices in forensic programming Build scripts through an iterative design Explore the rapid development of specialized scripts Understand how to leverage forensic libraries developed by the community Design flexibly to accommodate present and future hurdles Conduct effective and efficient investigations through programmatic pre-analysis Discover how to transform raw data into customized reports and visualizations In Detail This book will illustrate how and why you should learn Python to strengthen your analysis skills and efficiency as you creatively solve real-world problems through instruction-based tutorials. The tutorials use an interactive design, giving you experience of the development process so you gain a better understanding of what it means to be a forensic developer. Each chapter walks you through a forensic artifact and one or more methods to analyze the evidence. It also provides reasons why one method may be advantageous over another. We cover common digital forensics and incident response scenarios, with scripts that can be used to tackle case work in the field. Using built-in and community-sourced libraries, you will improve your problem solving skills with the addition of the Python scripting language. In addition, we provide resources for further exploration of each script so you can understand what further purposes Python can serve. With this knowledge, you can rapidly develop and deploy solutions to identify critical information and fine-tune your skill set as an examiner. Style and approach The book begins by instructing you on the basics of Python, followed by chapters that include scripts targeted for forensic casework. Each script is described step by step at an introductory level, providing gradual growth to demonstrate the available functionalities of Python.



Learning Python for data mining

Learning Python for data mining Author Valentina Porcu
ISBN-10 9788822803955
Release 2017-07-29
Pages
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My goal is to accompany a reader who is starting to study this programming language, showing her through basic concepts and then move to data mining. We will begin by explaining how to use Python and its structures, how to install Python, which tools are best suited for a data analyst work, and then switch to an introduction to data mining packages. The book is in any case an introduction. Its aim is not, for instance, to fully explain topics such as machine learning or statistics with this programming language, which would take at least twice or three times as much as this entire book. The aim is to provide a guidance from the first programming steps with Python to manipulation and import of datasets, to some examples of data analysis. To be more precise, in the Getting Started section, we will run through some basic installation concepts, tools available for programming on Python, differences between Python2 and Python3, and setting up a work folder. In Chapter 1, we will begin to see some basic concepts about creating objects, entering comments, reserved words for the system, and on the various types of operators that are part of the grammar of this programming language. In Chapter 2, we will carry on with the basic Python structures, such as tuples, lists, dictionaries, sets, strings, and files, and learn how to create and convert them. In Chapter 3 we will see the basics for creating small basic functions, and how to save them. Chapter 4 deals with conditional instructions that allow us to extend the power of a function as well as some important functions. In Chapter 5 we will keep talking about some basic concepts related to object-oriented programming, concept of module, method, and error handling. Chapter 6 is dedicated to importing files with some of the basic features. We will see how to open and edit text files, in .csv format, and in various other formats. Chapters 7 to 10 will deal with Python's most important data mining packages: Numpy and Scipy for mathematical functions and random data generation, pandas for dataframe management and data import, Matplotlib for drawing charts and scikit-learn for machine learning. With regard to scikit-learn, we will limit ourselves to provide a basic idea of the code of the various algorithms, without going, given the complexity of the subject, into details for the various techniques. Finally, in Conclusions, we will summarize the topics and concepts of the book and see the management of dates and some of the data sources for our tests with Python. This book is intended for those who want to get closer to the Python programming language from a data analysis perspective. We will therefore focus on the most used packages for data analysis, after the introduction to Python's basic concepts.



Machine Learning in Python

Machine Learning in Python Author Michael Bowles
ISBN-10 9781118961742
Release 2015-03-30
Pages 336
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This book shows readers how they can successfully analyze data using only two core machine learning algorithms---and how to do so using the popular Python programming language. These algorithms deal with common scenarios faced by all data analysts and data scientists. This book focuses on two algorithm families (linear methods and ensemble methods) that effectively predict outcomes. This type of problem covers a multitude of use cases (what ad to place on a web page, predicting prices in securities markets, detecting credit card fraud, etc.). The focus on two families gives enough room for full descriptions of the mechanisms at work in the algorithms. Then the code examples serve to illustrate the workings of the machinery with specific hackable code. The author will explain in simple terms, using no complex math, how these algorithms work, and will then show how to apply them in Python. He will also provide advice on how to select from among these algorithms, and will show how to prepare the data, and how to use the trained models in practice. The author begins with an overview of the two core algorithms, explaining the types of problems solved by each one. He then introduces a core set of Python programming techniques that can be used to apply these algorithms. The author shows various techniques for building predictive models that solve a range of problems, from simple to complex; he also shows how to measure the performance of each model to ensure you use the right one. The following chapters provide a deep dive into each of the two algorithms: penalized linear regression and ensemble methods. Chapters will show how to apply each algorithm in Python. Readers can directly use the sample code to build their own solutions.



Building Machine Learning Systems with Python Second Edition

Building Machine Learning Systems with Python   Second Edition Author Luis Pedro Coelho
ISBN-10 9781784392888
Release 2015-03-26
Pages 326
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This book primarily targets Python developers who want to learn and use Python's machine learning capabilities and gain valuable insights from data to develop effective solutions for business problems.



Large Scale Machine Learning with Python

Large Scale Machine Learning with Python Author Bastiaan Sjardin
ISBN-10 9781785888021
Release 2016-08-03
Pages 420
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Learn to build powerful machine learning models quickly and deploy large-scale predictive applications About This Book Design, engineer and deploy scalable machine learning solutions with the power of Python Take command of Hadoop and Spark with Python for effective machine learning on a map reduce framework Build state-of-the-art models and develop personalized recommendations to perform machine learning at scale Who This Book Is For This book is for anyone who intends to work with large and complex data sets. Familiarity with basic Python and machine learning concepts is recommended. Working knowledge in statistics and computational mathematics would also be helpful. What You Will Learn Apply the most scalable machine learning algorithms Work with modern state-of-the-art large-scale machine learning techniques Increase predictive accuracy with deep learning and scalable data-handling techniques Improve your work by combining the MapReduce framework with Spark Build powerful ensembles at scale Use data streams to train linear and non-linear predictive models from extremely large datasets using a single machine In Detail Large Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle. But finding algorithms and designing and building platforms that deal with large sets of data is a growing need. Data scientists have to manage and maintain increasingly complex data projects, and with the rise of big data comes an increasing demand for computational and algorithmic efficiency. Large Scale Machine Learning with Python uncovers a new wave of machine learning algorithms that meet scalability demands together with a high predictive accuracy. Dive into scalable machine learning and the three forms of scalability. Speed up algorithms that can be used on a desktop computer with tips on parallelization and memory allocation. Get to grips with new algorithms that are specifically designed for large projects and can handle bigger files, and learn about machine learning in big data environments. We will also cover the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python. Style and Approach This efficient and practical title is stuffed full of the techniques, tips and tools you need to ensure your large scale Python machine learning runs swiftly and seamlessly. Large-scale machine learning tackles a different issue to what is currently on the market. Those working with Hadoop clusters and in data intensive environments can now learn effective ways of building powerful machine learning models from prototype to production. This book is written in a style that programmers from other languages (R, Julia, Java, Matlab) can follow.



Advanced Machine Learning with Python

Advanced Machine Learning with Python Author John Hearty
ISBN-10 9781784393830
Release 2016-07-28
Pages 278
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Solve challenging data science problems by mastering cutting-edge machine learning techniques in Python About This Book Resolve complex machine learning problems and explore deep learning Learn to use Python code for implementing a range of machine learning algorithms and techniques A practical tutorial that tackles real-world computing problems through a rigorous and effective approach Who This Book Is For This title is for Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. If you've ever considered building your own image or text-tagging solution, or of entering a Kaggle contest for instance, this book is for you! Prior experience of Python and grounding in some of the core concepts of machine learning would be helpful. What You Will Learn Compete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms Apply your new found skills to solve real problems, through clearly-explained code for every technique and test Automate large sets of complex data and overcome time-consuming practical challenges Improve the accuracy of models and your existing input data using powerful feature engineering techniques Use multiple learning techniques together to improve the consistency of results Understand the hidden structure of datasets using a range of unsupervised techniques Gain insight into how the experts solve challenging data problems with an effective, iterative, and validation-focused approach Improve the effectiveness of your deep learning models further by using powerful ensembling techniques to strap multiple models together In Detail Designed to take you on a guided tour of the most relevant and powerful machine learning techniques in use today by top data scientists, this book is just what you need to push your Python algorithms to maximum potential. Clear examples and detailed code samples demonstrate deep learning techniques, semi-supervised learning, and more - all whilst working with real-world applications that include image, music, text, and financial data. The machine learning techniques covered in this book are at the forefront of commercial practice. They are applicable now for the first time in contexts such as image recognition, NLP and web search, computational creativity, and commercial/financial data modeling. Deep Learning algorithms and ensembles of models are in use by data scientists at top tech and digital companies, but the skills needed to apply them successfully, while in high demand, are still scarce. This book is designed to take the reader on a guided tour of the most relevant and powerful machine learning techniques. Clear descriptions of how techniques work and detailed code examples demonstrate deep learning techniques, semi-supervised learning and more, in real world applications. We will also learn about NumPy and Theano. By this end of this book, you will learn a set of advanced Machine Learning techniques and acquire a broad set of powerful skills in the area of feature selection & feature engineering. Style and approach This book focuses on clarifying the theory and code behind complex algorithms to make them practical, useable, and well-understood. Each topic is described with real-world applications, providing both broad contextual coverage and detailed guidance.



Learn Python

Learn Python Author Ryan Smith
ISBN-10 1532815735
Release 2016-04-18
Pages 36
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Discover how to learn python and start coding easily You're about to discover how to how to learn python and start coding easily. Today's websites contain powerful and dynamic content that can quickly adapt to different conditions. Even as mere users, we have would have heard of one of the most famous programming languages used to make this happen: Javascript.The thing is that Javascript is a client-side language - meaning that all the heavy lifting of creating the content lies within the user's browser. This may not be very friendly for older systems that do not have the processing power to benchpress all the factors that make an interactive page. Enter Python - one of the most powerful programming languages around. This language allows you to program content that can be produced by Javascript, and place it server-side. It is also one of the cleanest and friendliest languages around. This book was designed to give you all the basics needed to jumpstart you into the world of Python programming. Here Is A Preview Of What You'll Learn... What is python? What are basic python functions? What are functions and loops? What are strings? What are lists, tuples and dictionaries? What are exceptions and errors Much, much more! Download your copy today! Check Out What Others Are Saying... Highly recommended! Well written - Christine, San Diego Excellent book for beginners - Goldie, NY Tags:Python, Python course, Python book, learning Python, Python language, Python examples, Python tutorials, Python programming language, Python coding, Python programming for beginners, Python for Dummies



Learning Python Application Development

Learning Python Application Development Author Ninad Sathaye
ISBN-10 9781785885709
Release 2016-09-07
Pages 454
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Take Python beyond scripting to build robust, reusable, and efficient applications About This Book Get to grips with Python techniques that address commonly encountered problems in general application development. Develop, package, and deploy efficient applications in a fun way. All-practical coverage of the major areas of application development, including best practices, exception handling, testing, refactoring, design patterns, performance, and GUI application development. Who This Book Is For Do you know the basics of Python and object oriented programming? Do you want to go an extra mile and learn techniques to make your Python application robust, extensible, and efficient? Then this book is for you. What You Will Learn Build a robust application by handling exceptions. Modularize, package, and release the source distribution. Document the code and implement coding standards. Create automated tests to catch bugs in the early development stage. Identify and re-factor badly written code to improve application life. Detect recurring problems in the code and apply design patterns. Improve code efficiency by identifying performance bottlenecks and fixing them. Develop simple GUI applications using Python. In Detail Python is one of the most widely used dynamic programming languages, supported by a rich set of libraries and frameworks that enable rapid development. But fast paced development often comes with its own baggage that could bring down the quality, performance, and extensibility of an application. This book will show you ways to handle such problems and write better Python applications. From the basics of simple command-line applications, develop your skills all the way to designing efficient and advanced Python apps. Guided by a light-hearted fantasy learning theme, overcome the real-world problems of complex Python development with practical solutions. Beginning with a focus on robustness, packaging, and releasing application code, you'll move on to focus on improving application lifetime by making code extensible, reusable, and readable. Get to grips with Python refactoring, design patterns and best practices. Techniques to identify the bottlenecks and improve performance are covered in a series of chapters devoted to performance, before closing with a look at developing Python GUIs. Style and approach The book uses a fantasy game theme as a medium to explain various topics. Specific aspects of application development are explained in different chapters. In each chapter the reader is presented with an interesting problem which is then tackled using hands-on examples with easy-to-follow instructions.



How to Think Like a Computer Scientist

How to Think Like a Computer Scientist Author Allen Downey
ISBN-10 1441419071
Release 2009
Pages 248
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"How to Think Like a Computer Scientist: Learning with Python" is an introduction to computer science using the Python programming language. It covers the basics of computer programming, including variables and values, functions, conditionals and control flow, program development and debugging. Later chapters cover basic algorithms and data structures. *** Published under the terms of the GNU Free Documentation License. Money raised from the sale of this book supports the development of free software and documentation.



Introduction to Machine Learning with Python

Introduction to Machine Learning with Python Author Andreas C. Müller
ISBN-10 9781449369897
Release 2016-09-26
Pages 394
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Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book. With this book, you’ll learn: Fundamental concepts and applications of machine learning Advantages and shortcomings of widely used machine learning algorithms How to represent data processed by machine learning, including which data aspects to focus on Advanced methods for model evaluation and parameter tuning The concept of pipelines for chaining models and encapsulating your workflow Methods for working with text data, including text-specific processing techniques Suggestions for improving your machine learning and data science skills