Python machine learning

Migrate from pandas and scikit-Learn to PySpark to handle vast amounts of data and achieve faster data processing time. This book will show you how to make this transition by adapting your skills and leveraging the similarities in syntax, functionality, and interoperability between these tools. Distributed Machine Learning with PySpark offers a roadmap to data scientists considering transitioning from small data libraries (pandas/scikit-learn) to big data processing and machine learning with PySpark. You will learn to translate Python code from pandas/scikit-learn to PySpark to preprocess large volumes of data and build, train, test, and evaluate popular machine learning algorithms such as linear and logistic regression, decision trees, random forests, support vector machines, Na鴳e Bayes, and neural networks. After completing this book, you will understand the foundational concepts of data preparation and machine learning and will have the skills necessary to apply these methods using

A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory.This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation. Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the ne

This self-contained introduction to machine learning, designed from the start with engineers in mind, will equip students with everything they need to start applying machine learning principles and algorithms to real-world engineering problems. With a consistent emphasis on the connections between estimation, detection, information theory, and optimization, it includes: an accessible overview of the relationships between machine learning and signal processing, providing a solid foundation for further study; clear explanations of the differences between state-of-the-art techniques and more classical methods, equipping students with all the understanding they need to make informed technique choices; demonstration of the links between information-theoretical concepts and their practical engineering relevance; reproducible examples using Matlab, enabling hands-on student experimentation. Assuming only a basic understanding of probability and linear algebra, and accompanied by lecture slide

Fundamental theory and practical algorithms of weakly supervised classification, emphasizing an approach based on empirical risk minimization.Standard machine learning techniques require large amounts of labeled data to work well. When we apply machine learning to problems in the physical world, however, it is extremely difficult to collect such quantities of labeled data. This book presents theory and algorithms for weakly supervised learning, a paradigm of machine learning from weakly labeled data. Emphasizing an approach based on empirical risk minimization and drawing on state-of-the-art research in weakly supervised learning, the book provides both the fundamentals of the field and the advanced mathematical theories underlying them. It can be used as a reference for practitioners and researchers and in the classroom.The book first mathematically formulates classification problems, defines common notations, and reviews various algorithms for supervised binary and multiclass classif

Every day we interact with machine learning systems offering individualized predictions for our entertainment, social connections, purchases, or health. These involve several modalities of data, from sequences of clicks to text, images, and social interactions. This book introduces common principles and methods that underpin the design of personalized predictive models for a variety of settings and modalities. The book begins by revising 'traditional' machine learning models, focusing on adapting them to settings involving user data, then presents techniques based on advanced principles such as matrix factorization, deep learning, and generative modeling, and concludes with a detailed study of the consequences and risks of deploying personalized predictive systems. A series of case studies in domains ranging from e-commerce to health plus hands-on projects and code examples will give readers understanding and experience with large-scale real-world datasets and the ability to design mod

Data assimilation is a hugely important mathematical technique, relevant in fields as diverse as geophysics, data science, and neuroscience. This modern book provides an authoritative treatment of the field as it relates to several scientific disciplines, with a particular emphasis on recent developments from machine learning and its role in the optimisation of data assimilation. Underlying theory from statistical physics, such as path integrals and Monte Carlo methods, are developed in the text as a basis for data assimilation, and the author then explores examples from current multidisciplinary research such as the modelling of shallow water systems, ocean dynamics, and neuronal dynamics in the avian brain. The theory of data assimilation and machine learning is introduced in an accessible and unified manner, and the book is suitable for undergraduate and graduate students from science and engineering without specialized experience of statistical physics.

Probabilistic numerical computation formalises the connection between machine learning and applied mathematics. Numerical algorithms approximate intractable quantities from computable ones. They estimate integrals from evaluations of the integrand, or the path of a dynamical system described by differential equations from evaluations of the vector field. In other words, they infer a latent quantity from data. This book shows that it is thus formally possible to think of computational routines as learning machines, and to use the notion of Bayesian inference to build more flexible, efficient, or customised algorithms for computation. The text caters for Masters' and PhD students, as well as postgraduate researchers in artificial intelligence, computer science, statistics, and applied mathematics. Extensive background material is provided along with a wealth of figures, worked examples, and exercises (with solutions) to develop intuition.

Anew title in The QuestKids children's series thatwas launched by In Easy Steps in 2018, designed to make learning fun forchildren.Coding with Python- Create Amazing Graphics introducescoding in Python through a variety of projects. Each one teaches new codingconcepts and results in some amazing graphics.Discover how to mix colours, make patterns, draw 2D and 3Dshapes.Learn how to use loops, variables, random numbers, andfunctions in Python. Packed with challenges, tips, and extensions to keep allbudding coders engaged and eager to keep learning and creating!To create the games in this book, children will need a desktopcomputer or a laptop. The games require a proper keyboard so will not work wellon a tablet or iPad. It is recommended that children should be supervised whenusing the internet, especially when using a new website. Thisis the UK English edition.

With the advent of the Al era, professionals need to select the right tools to meet new opportunities and challenges! This instructional material focuses on the application of big data analysis, text mining, and machine learning in auditing to assist readers in understanding the latest developments in data analysis and smart audit.JCAATs is a new generation audit software developed by Python, which can be executed in a Windows and MAC operation systems with a multi-language, visual, and easy-to-use interface. Non-IT professionals can easily conduct smart data analysis. Through JCAATs' built-in data fusion technology and OPEN DATA connector, heterogeneous data can be quickly analyzed in real-time and effectively identifying anomalies. The features of this book include:The book is structured to provide a progressive learning experience, with chapters covering different concepts and applications, as well as exercises and practice questions. The official textbook endorsed by ICAEA

In recent years the development of new classification and regression algorithms based on deep learning has led to a revolution in the fields of artificial intelligence, machine learning, and data analysis. The development of a theoretical foundation to guarantee the success of these algorithms constitutes one of the most active and exciting research topics in applied mathematics. This book presents the current mathematical understanding of deep learning methods from the point of view of the leading experts in the field. It serves as both a starting point for researchers and graduate students in mathematics trying to get into the field, as well as an invaluable reference for future research.

New edition of a graduate-level textbook on that focuses on online convex optimization, a machine learning framework that views optimization as a process.In many practical applications, the environment is so complex that it is not feasible to lay out a comprehensive theoretical model and use classical algorithmic theory and/or mathematical optimization. Introduction to Online Convex Optimization presents a robust machine learning approach that contains elements of mathematical optimization, game theory, and learning theory: an optimization method that learns from experience as more aspects of the problem are observed. This view of optimization as a process has led to some spectacular successes in modeling and systems that have become part of our daily lives. Based on the “Theoretical Machine Learning” course taught by the author at Princeton University, the second edition of this widely used graduate level text features:Thoroughly updated material throughoutNew chapters on boosting, ad

Understand the theory and implementation of simulation. This book covers simulation topics from a scenario-driven approach using Python and rich visualizations and tabulations. The book discusses simulation used in the natural and social sciences and with simulations taken from the top algorithms used in the industry today. The authors use an engaging approach that mixes mathematics and programming experiments with beginning-intermediate level Python code to create an immersive learning experience that is cohesive and integrated. After reading this book, you will have an understanding of simulation used in natural sciences, engineering, and social sciences using Python. What You Will LearnUse Python and numerical computation to demonstrate the power of simulationChoose a paradigm to run a simulationDraw statistical insights from numerical experimentsKnow how simulation is used to solve real-world problems Who This Book Is ForEntry-level to mid-level Python developers from various backg

Augment your knowledge of Python with this entertaining learning guide, which features 100 exercises and programming puzzles and solutions. Python Challenges will help prepare you for your next exam or a job interview, and covers numerous practical topics such as strings, data structures, recursion, arrays, and more.Each topic is addressed in its own separate chapter, starting with an introduction to the basics and followed by 10 to 15 exercises of various degrees of difficulty, helping you to improve your programming skills effectively. Detailed sample solutions, including the algorithms used for all tasks, are included to maximize your understanding of each area. Author Michael Inden also describes alternative solutions and analyzes possible pitfalls and typical errors.Three appendices round out the book: the first covers the Python command line interpreter, which is often helpful for trying out the code snippets and examples in the book, followed by an overview of Pytest for unit te

This book explains the main ideas behind MemComputing, its theoretical foundations and its applicability to a wide variety of combinatorial optimization problems, machine learning, and quantum mechani

本書以零基礎讀者為對象，用範例引導讀者學習，深入淺出地介紹了Python的相關知識和實戰技能。本書從Python基礎入手，介紹了Python的開發環境、各種數據類型的操作方法、流程控制、函數等Python內核技術，以及使用Python處理文件、處理錯誤與異常等各種應用，最後列舉了Python在重要領域的項目實戰，內容系統且深入。本書提供與內容同步的教學錄像。此外，本書還贈送大量相關學習資料，以便讀者擴展學習。本書適合任何想學習Python的讀者，無論讀者是否從事計算機相關行業、是否接觸過Python，均可通過本書快速掌握Python的開發方法和技巧。

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本書是一本Python基礎性教材，從彰顯Python兩大特點出發，以讓學習Python的讀者打下一個概念清晰、技術紮實的基礎為目的而作。全書，共分為6章。第1章Python初識。第2章Python命令式編程。第3章Python函數式編程。第4章Python容器。第5章Python面向對象編程。第6章第三方Python 庫。本書彰顯程序設計語言自身特色，概念表述精准清晰、例題經典、習題豐富，並以二維碼鏈接形式提供了有關知識擴展，為學習者創造良好的學習環境，適合大學有關專業的程序設計教學，也可供有關技術人員和專業培訓使用。

本書主要介紹Python編程語言的基礎知識和使用Python進行數據爬蟲、數據分析與數據可視化的方法，主要內容包括Python安裝與IDE選擇；Python基礎知識；Python運算符與流程控制；函數；面向對象基礎；模塊；網絡數據爬取；數據分析基礎；數據可視化。 本書涵蓋了Python編程語言的主要應用場景，注重理論知識的學習和實際應用的充分結合。本書提供的PPT、教學視頻均為專業公司製作，內容精美，應用價值較高。章節後提供的綜合案例和習題可供讀者綜合複習每章的知識點。 本書可作為高等學校計算機及相關專業Python編程基礎、Python編程與科學計算等課程的教材，也可供想學習Python基礎知識和使用Python做應用開發的讀者做參考使用。

本書是一部系統論述 Python 編程語言和實際應用技術的圖書， 全書共分為 13 章： 第 1章～第 9 章講解 Python 基本語法； 第 9 章～第 13 章講解 Python 實際應用的技術。 主要內容包括： 編寫第一 Python 程序； Pyhon 基本語法； Python 數據類型； 函數； 面向對象； 日期和時間； 異常處理； 訪問文件與目錄； 圖形界面編程； 網絡編程； 多線程； MySQL 數據庫編程和訪問 Excel 文件。本書適合作為Python課程教材，也可供相關人員自學參考。

本書系統介紹了Python語言的語法基礎知識，包括Python基本數據類型、程序控制結構、組合數據類型、函數與模塊、類和對象、文件，以及Python常用標準庫；並圍繞Python網絡爬蟲、Python數據分析、Python票據識別的具體實例，詳細介紹了Python第三方庫的相關應用，闡述其應用程序的開發方法和過程，實現知識到能力的進階。本書第1～9章為Python學習的基礎內容部分，夯實基礎，萬丈高樓方能有望。第10～12章為Python學習的應用和提高部分，遴選有趣且實用的案例，通過翔實的講解和豐富的代碼案例帶領讀者充分領略Python的強大之處。

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本書按照Python程序語言特點、算法思維和實踐應用邏輯，由淺入深，從零起點到基礎、到精進提升，再到實踐應用，以漸進式方式分成4篇10章。第1篇為Python基礎入門，包括Python概述、Python基礎語法、基本程序結構、Python常用數據結構、自定義函數與模塊；第2篇為Python算法基礎，包括Python常用算法；第3篇為數據結構的Python實現，包括數據結構；第4篇為Python應用開發模塊，包括Python數據處理與分析、人工智能應用實踐、Python應用開發。 每一章都配有講解視頻、典型例題、練習題和上機實踐任務，並附贈示例程序、習題源代碼和教學課件等電子資源。 本書既可以作為教育技術學、教育學(教育技術)、計算機(師範)相關專業及選修Python程序設計課程學生的教材，也可以作為中小學信息技術教師、青少年編程教育工作者、社會人士的自學書籍等。本書封面貼有清華大學出版社防偽標簽，無標簽者不得銷售。

本書通過全彩圖解+視頻講解的形式，介紹了Python編程入門及應用的相關知識，主要內容包括：Python編程環境安裝與運行、Python中的數字運算、Python中的數據類型、輸入輸出與文件操作、條件與循環語句、函數與庫、Python的OS、Python的命名空間與生命週期，以及Python五子棋項目實例、Python實現簡易計算器、Python嵌入式實例―機器視覺等綜合案例的開發。本書內容循序漸進，講解通俗易懂，書中重難點章節配套視頻講解，掃碼即可隨時觀看，同時提供源程序，方便學習實踐。本書適合Python初學者、熱愛編程的青少年朋友自學使用，也適合中小學信息技術課堂或相關培訓機構用作教材。

這本書將重新定義Python學習，它避開了現有Python入門書知識點雜而不精、內容脫離實際開發需求、詳略設置不合理、學習曲線過於陡峭、案例過於炫技等弊端，它將帶領讀者快速掌握Python語言的最新特性、最新編程方法和最佳實踐，讓讀者學完本書後具備項目實戰能力，能用Python解決實際工作中的問題。主要內容：(1)Python入門：Python基礎知識龐雜，但很多知識點並不常用，本書對初學者必備的基礎知識精挑細選，以降低讀者的學習負荷。(2)Python進階：詳細且有針對性地講解了Python的進階知識，主要包括類、模塊和各種常用內置庫的原理和使用。學到這裡，讀者基本具備解決Python編程中各種常見問題的能力。(3)Python應用：講解了如何用Python解決數據科學、辦公自動化、圖形及界面、Web開發4大領域的問題，它將讓讀者掌握Python項目的完整實現過程和常見第三方庫的使用。內容特色：(1)人人可零基礎入門，不論年齡，不分職業，不需要任何背景知識。(2)結構設計合理、學習曲線平滑，輕鬆打牢Python基礎。(3)圖解書中難點和重點，清晰直觀，一圖勝千言。(4)代碼簡潔，變量命名簡單，代碼邏輯清晰，避免晦澀難懂術語。 (5)知識全面，詳略得當，涵蓋最新的語言特性。(6)面向應用，講解必備第三方庫，配有經典、實用案例。 附加價值：配套視頻：100個視頻，近10個小時，邊看邊學，輕鬆愉快實戰案例：1600個小案例，30個綜合案例，邊練邊學，舉一反三配套習題：提供大量的高品質練習題，邊學邊練，強化提高案例源碼：提供全書大小案例源代碼，邊敲邊學，實踐導向在線答疑：紙書、社群、網站可答疑，邊問邊學，事半功倍思維導圖：一張圖呈現全書知識精華，先看後學，窺斑知豹PPT課件：針對性配套完整教學PPT，邊教邊學，教學相長。

本書緊跟Python設計語言的前沿應用，以真實項目為背景，以問題為導向，採用任務驅動式教學法，將知識點有機融入7個項目中，包括：Hello冰墩墩――認識Python、碳排放計算――Python基礎知識、綠色出行――選擇結構、賬戶安全――循環結構、淩雲賽場――函數和文件、志願者風采――可視化編程、強國有我――字符與詞雲。

本書主要包括揭秘 Python 程序設計之謎、控制程序的結構、優化程序的性能、Python 與辦公自動化、Python 數據分析與可視化、Python 與會計、Python 與電子商務七大情景，共 23 個任務。希望通過本書的學習，使學生具備解題的思維方式和程序設計的基本功，並培養學生通過Python 編程來解決實際問題的能力。

本書作為Python程序設計的入門教程，通過大量實例深入淺出地介紹了Python的相關常用基礎知識。本書共分為11章，包括配置Python編程環境，變量、常量、數據類型與運算符，Python序列類型，最簡單的Python語言程序――順序結構，Python分支結構程序設計，Python循環結構程序設計，函數，模塊，類的定義與使用，Python文件操作與Python繪圖基礎等內容。本書以知識內容為主線，以學生發展為中心，圍繞實訓項目遞進式設計內容，全面提升學生的編程能力，解決學生在Python程序設計基礎課中“學不會寫程序”的通病。除了每一章都有相應的基礎實訓項目外，本書還設計了一個貫穿全書的增量式實訓項目“簡易的學生管理系統”。 本書可以作為高等院校相關專業的Python入門基礎教材，也可以作為自學Python基礎知識的讀者的參考書。本書封面貼有清華大學出版社防偽標簽，無標簽者不得銷售。