Python Interviews : Discussions with Python Experts

Mike Driscoll takes you on a journey talking to a hall-of-fame list of truly remarkable Python experts. You'll be inspired every time by their passion for the Python language, as they share with you their experiences, contributions, and careers in Python. Key Features Hear from these key Python thinkers about the current status of Python, and where it's heading in the future Listen to their close thoughts on significant Python topics, such as Python's role in scientific computing, and machine learning Understand the direction of Python, and what needs to change for Python 4 Book Description Each of these twenty Python Interviews can inspire and refresh your relationship with Python and the people who make Python what it is today. Let these interviews spark your own creativity, and discover how you also have the ability to make your mark on a thriving tech community. This book invites you to immerse in the Python landscape, and let these remarkable programmers show you how you too can connect and share with Python programmers around the world. Learn from their opinions, enjoy their stories, and use their tech tips. \* Brett Cannon - former director of the PSF, Python core developer, led the migration to Python 3. \* Steve Holden - tireless Python promoter and former chairman and director of the PSF. \* Carol Willing - former director of the PSF and Python core developer, Project Jupyter Steering Council member. \* Nick Coghlan - founding member of the PSF's Packaging Working Group and Python core developer. \* Jessica McKellar - former director of the PSF and Python activist. \* Marc-André Lemburg - Python core developer and founding member of the PSF. \* Glyph Lefkowitz - founder of Twisted and fellow of the PSF \* Doug Hellmann - fellow of the PSF, creator of the Python Module of the Week blog, Python community member since 1998. \* Massimo Di Pierro - fellow of the PSF, data scientist and the inventor of web2py. \* Alex Martelli - fellow of the PSF and co-author of Python in a Nutshell. \* Barry Warsaw - fellow of the PSF, Python core developer since 1995, and original member of PythonLabs. \* Tarek Ziadé - founder of Afpy and author of Expert Python Programming. \* Sebastian Raschka - data scientist and author of Python Machine Learning. \* Wesley Chun - fellow of the PSF and author of the Core Python Programming books. \* Steven Lott - Python blogger and author of Python for Secret Agents. \* Oliver Schoenborn - author of Pypubsub and wxPython mailing list contributor. \* Al Sweigart - bestselling author of Automate the Boring Stuff with Python and creator of the Python modules Pyperclip and PyAutoGUI. \* Luciano Ramalho - fellow of the PSF and the author of Fluent Python. \* Mike Bayer - fellow of the PSF, creator of open source libraries including SQLAlchemy. \* Jake Vanderplas - data scientist and author of Python Data Science Handbook. What you will learn How successful programmers think The history of Python Insights into the minds of the Python core team Trends in Python programming Who this book is for Python programmers and students interested in the way that Python is used - past and present - with useful anecdotes. It will also be of interest to those looking to gain insights from top programmers. Key Features A practical approach to the frameworks of data science, machine learning, and deep learningUse the most powerful Python libraries to implement machine learning and deep learningLearn best practices to improve and optimize your machine learning systems and algorithms Book Description Machine learning is eating the software world, and now deep learning is extending machine learning. This book is for developers and data scientists who want to master the world of artificial intelligence, with a practical approach to understanding and implementing machine learning, and how to apply the power of deep learning with Python. This Second Edition of Sebastian Raschka's Python Machine Learning is thoroughly updated to use the most powerful and modern Python open-source libraries, so that you can understand and work at the cutting-edge of machine learning, neural networks, and deep learning. Written for developers and data scientists who want to create practical machine learning code, the authors have extended and modernized this best-selling book, to now include the influential TensorFlow library, and the Keras Python neural network library. The Scikit-learn code has also been fully updated to include recent innovations. The result is a new edition of this classic book at the cutting edge of machine learning. Readers new to machine learning will find this classic book offers the practical knowledge and rich techniques they need to create and contribute to machine learning, deep learning, and modern data analysis. Raschka and Mirjalili introduce you to machine learning and deep learning algorithms, and show you how to apply them to practical industry challenges. By the end of the book, you'll be ready to meet the new data analysis opportunities in today's world. Readers of the first edition will be delighted to find a new balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. Readers can learn and work with TensorFlow more deeply than ever before, and essential coverage of the Keras neural network library has been added, along with the most recent updates to Scikit-learn. Raschka and Mirjalili have updated this book to meet the most modern areas of machine learning, to give developers and data scientists a fresh and practical Python journey into machine learning. What you will learn Use the key frameworks of data science, machine learning, and deep learningAsk new questions of your data through machine learning models and neural networksWork with the most powerful Python open-source libraries in machine learningBuild deep learning applications using Keras and TensorFlowEmbed your machine learning model in accessible web applicationsPredict continuous target outcomes using regression analysisUncover hidden patterns and structures in data with clusteringAnalyze images using deep learning techniquesUse sentiment analysis to delve deeper into textual and social media data About the Author Sebastian Raschka, author of the best selling Python Machine Learning, has many years of experience with coding in Python and has given several seminars on the practical applications of data science and machine learning, including a machine learning tutorial at SciPy, the leading conference for scientific computing in Python. Sebastian loves to write and talk about data science, machine learning, and Python, and he's motivated to help people developing data-driven solutions without necessarily requiring a machine learning background. His work and contributions have recently been recognized by the departmental outstanding graduate student award 2016-2017. In his free time, Sebastian loves to contribute to open source projects, and methods that he implemented are now successfully used in machine learning competitions such as Kaggle. Vahid Mirjalili obtained his Ph.D. in mechanical engineering working on nove Unlock modern machine learning and deep learning techniques with Python by using the latest cutting-edge open source Python libraries.About This BookSecond edition of the bestselling book on Machine LearningA practical approach to key frameworks in data science, machine learning, and deep learningUse the most powerful Python libraries to implement machine learning and deep learningGet to know the best practices to improve and optimize your machine learning systems and algorithmsWho This Book Is ForIf you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential and unmissable resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for developers and data scientists who want to teach computers how to learn from data.What You Will LearnUnderstand the key frameworks in data science, machine learning, and deep learningHarness the power of the latest Python open source libraries in machine learningExplore machine learning techniques using challenging real-world dataMaster deep neural network implementation using the TensorFlow libraryLearn the mechanics of classification algorithms to implement the best tool for the jobPredict continuous target outcomes using regression analysisUncover hidden patterns and structures in data with clusteringDelve deeper into textual and social media data using sentiment analysisIn DetailMachine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka's bestselling book, Python Machine Learning. Thoroughly updated using the latest Python open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis.Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow deep learning library. The scikit-learn code has also been fully updated to include recent improvements and additions to this versatile machine learning library.Sebastian Raschka and Vahid Mirjalili's unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. By the end of the book, you'll be ready to meet the new data analysis opportunities in today's world.If you've read the first edition of this book, you'll be delighted to find a new balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. You'll be able to learn and work with TensorFlow more deeply than ever before, and get essential coverage of the Keras neural network library, along with the most recent updates to scikit-learn.Style and ApproachPython Machine Learning Second Edition takes a practical, hands-on coding approach so you can learn about machine learning by coding with Python. This book moves fluently between the theoretical principles of machine learning and the practical details of implementation with Python. Mike Driscoll takes you on a journey talking to a hall-of-fame list of truly remarkable Python experts. You'll be inspired every time by their passion for the Python language, as they share with you their experiences, contributions, and careers in Python. About This Book • Hear from these key Python thinkers about the current status of Python, and where it's heading in the future • Listen to their close thoughts on significant Python topics, such as Python's role in scientific computing, and machine learning • Understand the direction of Python, and what needs to change for Python 4 Who This Book Is For Python programmers and students interested in the way that Python is used – past and present – with useful anecdotes. It will also be of interest to those looking to gain insights from top programmers. What You Will Learn • How successful programmers think • The history of Python • Insights into the minds of the Python core team • Trends in Python programming In Detail Each of these twenty Python Interviews can inspire and refresh your relationship with Python and the people who make Python what it is today. Let these interviews spark your own creativity, and discover how you also have the ability to make your mark on a thriving tech community. This book invites you to immerse in the Python landscape, and let these remarkable programmers show you how you too can connect and share with Python programmers around the world. Learn from their opinions, enjoy their stories, and use their tech tips. • Brett Cannon - former director of the PSF, Python core developer, led the migration to Python 3. • Steve Holden - tireless Python promoter and former chairman and director of the PSF. • Carol Willing - former director of the PSF and Python core developer, Project Jupyter Steering Council member. • Nick Coghlan - founding member of the PSF's Packaging Working Group and Python core developer. • Jessica McKellar - former director of the PSF and Python activist. • Marc-Andre Lemburg - Python core developer and founding member of the PSF. • Glyph Lefkowitz - founder of Twisted and fellow of the PSF • Doug Hellmann - fellow of the PSF, creator of the Python Module of the Week blog, Python community member since 1998. • Massimo Di Pierro - fellow of the PSF, data scientist and the inventor of web2py. • Alex Martelli - fellow of the PSF and co-author of Python in a Nutshell. • Barry Warsaw - fellow of the PSF, Python core developer since 1995, and original member of PythonLabs. • Tarek Ziade - founder of Afpy and author of Expert Python Programming. • Sebastian Raschka - data scientist and author of Python Machine Learning. • Wesley Chun - fellow of the PSF and author of the Core Python Programming books. • Steven Lott - Python blogger and author of Python for Secret Agents. • Oliver Schoenborn - author of Pypubsub and wxPython mailing list contributor. • Al Sweigart - bestselling author of Automate the Boring Stuff with Python and creator of the Python modules Pyperclip and PyAutoGUI. • Luciano Ramalho - fellow of the PSF and the author of Fluent Python. • Mike Bayer - fellow of the PSF, creator of open source libraries including SQLAlchemy. • Jake Vanderplas - data scientist and author of Python Data Science Handbook. Style and approach This is a book of one-to-one interviews with leading Python programmers and luminaries in the field. Mike Driscoll takes you on a journey talking to a hall-of-fame list of truly remarkable Python experts. You'll be inspired every time by their passion for the Python language, as they share with you their experiences, contributions, and careers in Python. About This Book Hear from these key Python thinkers about the current status of Python, and where it's heading in the future Listen to their close thoughts on significant Python topics, such as Python's role in scientific computing, and machine learning Understand the direction of Python, and what needs to change for Python 4 Who This Book Is For Python programmers and students interested in the way that Python is used – past and present – with useful anecdotes. It will also be of interest to those looking to gain insights from top programmers. What You Will Learn How successful programmers think The history of Python Insights into the minds of the Python core team Trends in Python programming In Detail Each of these twenty Python Interviews can inspire and refresh your relationship with Python and the people who make Python what it is today. Let these interviews spark your own creativity, and discover how you also have the ability to make your mark on a thriving tech community. This book invites you to immerse in the Python landscape, and let these remarkable programmers show you how you too can connect and share with Python programmers around the world. Learn from their opinions, enjoy their stories, and use their tech tips. Brett Cannon - former director of the PSF, Python core developer, led the migration to Python 3. Steve Holden - tireless Python promoter and former chairman and director of the PSF. Carol Willing - former director of the PSF and Python core developer, Project Jupyter Steering Council member. Nick Coghlan - founding member of the PSF and Python core developer. Jessica McKellar - former director of the PSF and Python activist. Marc-André Lemburg - Python core developer and founding member of the PSF. Glyph Lefkowitz - founder of Twisted and fellow of the PSF Doug Hellmann - fellow of the PSF, creator of the Python Module of the Week blog, Python community member since 1998. Massimo Di Pierro - fellow of the PSF, data scientist and the inventor of web2py. Alex Martelli - fellow of the PSF and co-author of Python in a Nutshell. Barry Warsaw - fellow of the PSF, Python core developer since 1995, and original member of PythonLabs. Tarek Ziadé - founder of Afpy and author of Expert Python Programming. Sebastian Raschka - data scientist and author of Python Machine Learning. Wesley Chun - fellow of the PSF and author of the Core Python Programming books. Steven Lott - Python blogger and author of Python for Secret Agents. Oliver Schoenborn - author of Pypubsub and wxPython mailing list contributor. Al Sweigart - bestselling author and creator of the Python modules Pyperclip and PyAutoGUI. Luciano Ramalho - fellow of the PSF and the author of Fluent Python. Mike Bayer - fellow of the PSF, creator of open source libraries including SQLAlchemy. Jake Vanderplas - data scientist and author of Python Data Science Handbook. Style and approach This is a book of one-to-one interviews with leading Python programmers and luminaries in the field. Software legend Max Kanat-Alexander shows you how to succeed as a developer by embracing simplicity, with forty-three essays that will help you really understand the software you work with.About This BookRead and enjoy the superlative writing and insights of the legendary Max Kanat-AlexanderLearn and reflect with Max on how to bring simplicity to your software design principlesDiscover the secrets of rockstar programmers and how to also just suck less as a programmerWho This Book Is ForUnderstanding Software is for every programmer, or anyone who works with programmers. If life is feeling more complex than it should be, and you need to touch base with some clear thinking again, this book is for you. If you need some inspiration and a reminder of how to approach your work as a programmer by embracing some simplicity in your work again, this book is for you.If you're one of Max's followers already, this book is a collection of Max's thoughts selected and curated for you to enjoy and reflect on. If you're new to Max's work, and ready to connect with the power of simplicity again, this book is for you!What You Will LearnSee how to bring simplicity and success to your programming worldClues to complexity - and how to build excellent softwareSimplicity and software designPrinciples for programmersThe secrets of rockstar programmersMax's views and interpretation of the Software industryWhy Programmers suck and how to suck less as a programmerSoftware design in two sentencesWhat is a bug? Go deep into debuggingIn DetailIn Understanding Software, Max Kanat-Alexander, Technical Lead for Code Health at Google, shows you how to bring simplicity back to computer programming. Max explains to you why programmers suck, and how to suck less as a programmer. There's just too much complex stuff in the world. Complex stuff can't be used, and it breaks too easily. Complexity is stupid. Simplicity is smart.Understanding Software covers many areas of programming, from how to write simple code to profound insights into programming, and then how to suck less at what you do! You'll discover the problems with software complexity, the root of its causes, and how to use simplicity to create great software. You'll examine debugging like you've never done before, and how to get a handle on being happy while working in teams.Max brings a selection of carefully crafted essays, thoughts, and advice about working and succeeding in the software industry, from his legendary blog Code Simplicity. Max has crafted forty-three essays which have the power to help you avoid complexity and embrace simplicity, so you can be a happier and more successful developer.Max's technical knowledge, insight, and kindness, has earned him code guru status, and his ideas will inspire you and help refresh your approach to the challenges of being a developer.Style and approachUnderstanding Software is a new selection of carefully chosen and crafted essays from Max Kanat-Alexander's legendary blog call Code Simplicity. Max's writing and thoughts are great to sit and read cover to cover, or if you prefer you can drop in and see what you discover new every single time! Unlock Modern Machine Learning And Deep Learning Techniques With Python By Using The Latest Cutting-edge Open Source Python Libraries. About This Book * Second Edition Of The Bestselling Book On Machine Learning * A Practical Approach To Key Frameworks In Data Science, Machine Learning, And Deep Learning * Use The Most Powerful Python Libraries To Implement Machine Learning And Deep Learning * Get To Know The Best Practices To Improve And Optimize Your Machine Learning Systems And Algorithms Who This Book Is For If You Know Some Python And You Want To Use Machine Learning And Deep Learning, Pick Up This Book. Whether You Want To Start From Scratch Or Extend Your Machine Learning Knowledge, This Is An Essential And Unmissable Resource. Written For Developers And Data Scientists Who Want To Create Practical Machine Learning And Deep Learning Code, This Book Is Ideal For Developers And Data Scientists Who Want To Teach Computers How To Learn From Data. What You Will Learn * Understand The Key Frameworks In Data Science, Machine Learning, And Deep Learning * Harness The Power Of The Latest Python Open Source Libraries In Machine Learning * Explore Machine Learning Techniques Using Challenging Real-world Data * Master Deep Neural Network Implementation Using The Tensorflow Library * Learn The Mechanics Of Classification Algorithms To Implement The Best Tool For The Job * Predict Continuous Target Outcomes Using Regression Analysis * Uncover Hidden Patterns And Structures In Data With Clustering * Delve Deeper Into Textual And Social Media Data Using Sentiment Analysis In Detail Machine Learning Is Eating The Software World, And Now Deep Learning Is Extending Machine Learning. Understand And Work At The Cutting Edge Of Machine Learning, Neural Networks, And Deep Learning With This Second Edition Of Sebastian Raschka's Bestselling Book, Python Machine Learning. --publisher's Description. 1. Giving Computers The Ability To Learn From Data -- 2. Training Simple Machine Learning Algorithms For Classification -- 3. A Tour Of Machine Learning Classifiers Using Scikit-learn -- 4. Building Good Training Sets-data Preprocessing -- 5. Compressing Data Via Dimensionality Reduction -- 6. Learning Best Practices For Model Evaluation And Hyperpaarmeter Tuning -- 7.combining Different Models For Ensemble Learning -- 8. Applying Machine Learning To Sentiment Analysis -- 9. Embedding A Machine Learning Model Into A Web Application -- 10. Predicting Continuous Target Variables With Regression Analysis -- 11. Working With Unlabeled Data-clustering Analysis -- 12. Implementing A Multilayer Artificial Neural Network From Scratch -- 13. Parallelizing Neural Network Training With Tensorflow -- 14. Going Deeper -- The Mechanics Of Tensorflow -- 15. Classifying Images With Deep Convolutional Neural Networks -- 16. Modeling Sequential Data Using Recurrent Neural Networks. Sebastian Raschka, Vahid Mirajalili. Includes Index. Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around youAbout This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no timeWho This Book Is ForThis book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on itIn DetailArtificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide!Style and approachThis highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application Mike Driscoll takes you on a journey talking to a hall-of-fame list of truly remarkable Python experts. You'll be inspired every time by their passion for the Python language, as they share with you their experiences, contributions, and careers in Python. About This Book Hear from these key Python thinkers about the current status of Python, and where it's heading in the future Listen to their close thoughts on significant Python topics, such as Python's role in scientific computing, and machine learning Understand the direction of Python, and what needs to change for Python 4 Who This Book Is For Python programmers and students interested in the way that Python is used - past and present - with useful anecdotes. It will also be of interest to those looking to gain insights from top programmers. What You Will Learn How successful programmers think The history of Python Insights into the minds of the Python core team Trends in Python programming In Detail Each of these twenty Python Interviews can inspire and refresh your relationship with Python and the people who make Python what it is today. Let these interviews spark your own creativity, and discover how you also have the ability to make your mark on a thriving tech community. This book invites you to immerse in the Python landscape, and let these remarkable programmers show you how you too can connect and share with Python programmers around the world. Learn from their opinions, enjoy their stories, and use their tech tips. Brett Cannon - former director of the PSF, Python core developer, led the migration to Python 3. Steve Holden - tireless Python promoter and former chairman and director of the PSF. Carol Willing - former director of the PSF and Python core developer, Project Jupyter Steering Council member. Nick Coghlan - founding member of the PSF's Packaging Working Group and Python core developer. Jessica McKellar - former director of the PSF and Python activist. Marc-André Lemburg - Python core developer and founding member of the PSF. Glyph Lefkowitz - founder of Twisted and fellow of the PSF Doug Hellmann - fellow of the PSF, creator of the Python Module of the Week blog, Python community member since 1998. Massimo Di Pierro - fellow of the PSF, data scientist and the inventor of web2py. Alex Martelli - fellow of the PSF and co-author of Python in a Nutshell. Barry Warsaw - fellow of the PSF, Python core developer since 1995, and original member of PythonLabs. Tarek Ziadé - found ... Unlock modern machine learning and deep learning techniques with Python by using the latest cutting-edge open source Python libraries. About This Book Second edition of the bestselling book on Machine Learning A practical approach to key frameworks in data science, machine learning, and deep learning Use the most powerful Python libraries to implement machine learning and deep learning Get to know the best practices to improve and optimize your machine learning systems and algorithms Who This Book Is For If you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential and unmissable resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for developers and data scientists who want to teach computers how to learn from data. What You Will Learn Understand the key frameworks in data science, machine learning, and deep learning Harness the power of the latest Python open source libraries in machine learning Explore machine learning techniques using challenging real-world data Master deep neural network implementation using the TensorFlow library Learn the mechanics of classification algorithms to implement the best tool for the job Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Delve deeper into textual and social media data using sentiment analysis In Detail Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka's bestselling book, Python Machine Learning. Thoroughly updated using the latest Python open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis. Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow deep learning library. The scikit-learn code has also been fully updated to include recent improvements and additions to this versatile machine learning library. Sebastian Raschka and Vahid Mirjalili's unique insight and expertise introduce you to machine learning and deep learning algorithms from s.. Build Real-world Ai Applications With Python To Intelligently Interact With Your Surroundingsabout This Book* Step Into The Amazing World Of Intelligent Apps Using This Comprehensive Guide* Enter The World Of Ai, Explore It, And Become Independent To Create Your Own Ai Apps* Work Through Simple Yet Insightful Examples That Will Get You Up And Running With Artificial Intelligence In No Timewho This Book Is Forthis Book Is For Python Developers Who Want To Build Real-world Ai Applications. This Book Is Friendly To Python Beginners, But Being Familiar With Python Would Be Useful To Play Around With The Code. It Will Also Be Useful For Experienced Python Programmers Who Are Looking To Implement Ai Techniques In Their Existing Technology Stacks.what You Will Learn* Find Out How To Use Different Classification And Regression Techniques* Understand The Concept Of Clustering And How To Use It To Automatically Segment Data* See How To Build An Intelligent Recommender System* Understand Logic Programming And How To Use It* Develop Automatic Speech Recognition Systems* Understand The Basics Of Heuristic Search And Genetic Programming* Develop An Understanding Of Reinforcement Learning* Discover How To Build Ai Applications Centered On Images, Text, And Time Series Data* Understand How To Use Deep Learning Algorithms And Build Applications Based On Itin Detailai Is Becoming Increasingly Relevant In The Modern World Where The Ecosystem Is Driven By Technology And Data. Ai Is Used Extensively Across Many Fields Such As Robotics, Computer Vision, Finance, And So On. We Will Explore Various Real-world Scenarios In This Book And You'll Learn About Various Ai Algorithms That Can Be Used To Build Various Applications.during The Course Of This Book, You Will Find Out How To Make Informed Decisions About What Algorithms To Use In A Given Context. Starting From The Basics Of The Ai Concepts, You Will Learn How To Develop The Various Building Blocks Of Ai Using Different Data Mining Techniques. You Will See How To Implement Different Algorithms To Get The Best Possible Results, And Will Understand How To Apply Them To Real-world Scenarios. If You Want To Add An Intelligence Layer To Any Application Based On Images, Text, Stock Market, Or Some Other Form Of Data, This Exciting Book On Ai Will Definitely Guide You All The Way! Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! What You Will Learn: Realize different classification and regression techniques; Understand the concept of clustering and how to use it to automatically segment data; See how to build an intelligent recommender system; Understand logic programming and how to use it; Build automatic speech recognition systems; Understand the basics of heuristic search and genetic programming; Develop games using Artificial Intelligence; Learn how reinforcement learning works; Discover how to build intelligent applications centered on images, text, and time series data; See how to use deep learning algorithms and build applications based on it--Publisher website "Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka's bestselling book, Python Machine Learning. Thoroughly updated using the latest Python open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis. Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow deep learning library. The scikit-learn code has also been fully updated to include recent improvements and additions to this versatile machine learning library. Sebastian Raschka and Vahid Mirjalili's unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. By the end of the book, you'll be ready to meet the new data analysis opportunities in today's world. If you've read the first edition of this book, you'll be delighted to find a new balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. You'll be able to learn and work with TensorFlow more deeply than ever before, and get essential coverage of the Keras neural network library, along with the most recent updates to scikit-learn."-- Résumé de l'éditeur Cover 1 Copyright 3 Packt Upsell 4 Foreword 5 Contributor 6 Table of Contents 8 Preface 10 Chapter 1: Brett Cannon 12 Chapter 2: Steve Holden 34 Chapter 3: Carol Willing 50 Chapter 4: Glyph Lefkowitz 64 Chapter 5: Doug Hellmann 94 Chapter 6: Massimo Di Pierro 104 Chapter 7: Alex Martelli 116 Chapter 8: Marc-André Lemburg 152 Chapter 9: Barry Warsaw 166 Chapter 10: Jessica McKellar 194 Chapter 11: Tarek Ziadé 202 Chapter 12: Sebastian Raschka 216 Chapter 13: Wesley Chun 232 Chapter 14: Steven Lott 242 Chapter 15: Oliver Schoenborn 252 Chapter 16: Al Sweigart 274 Chapter 17: Luciano Ramalho 292 Chapter 18: Nick Coghlan 308 Chapter 19: Mike Bayer 334 Chapter 20: Jake Vanderplas 348 Other Books You May Enjoy 356 Artificial Intelligence with Python 357 Understanding Software 358 Leave a review - let other readers know what you think 359 Index 360 1. Brett Cannon 2. Steve Holden 3. Carol Willing 4. Glyph Lefkowitz 5. Doug Hellmann 6. Massimo Di Pierro 7. Alex Martelli 8. Marc-Andre Lemburg 9. Barry Warsaw 10. Jessica McKellar 11. Tarek Ziade 12. Sebastian Raschka 13. Wesley Chun 14. Steven Lott 15. Oliver Schoenborn 16. Al Sweigart 17. Luciano Ramalho 18. Nick Coghlan 19. Mike Bayer 20. Jake Vanderplas