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《数据挖掘与机器学习:WEKA应用技术与实践》借助代表当今数据挖掘和机器学习最高水平的著名开源软件Weka，通过大量的实践操作，使读者了解并掌握数据挖掘和机器学习的相关技能，拉近理论与实践的距离。全书共分8章，主要内容包括Weka介绍、Explorer界面、KnowledgeFlow界面、Experimenter界面、命令行界面、Weka高级应用、WekaAPI和学习方案源代码分析 基本信息 出版社: 清华大学出版社; 第1版 (2014年7月1日) 平装: 456页 语种： 简体中文 开本: 16 ISBN: 7302371741, 9787302371748 条形码: 9787302371748 商品尺寸: 26.6 x 19.6 x 4.4 cm 商品重量: 739 g 品牌: 清华大学出版社

Table of Contents Chapter 1: Getting Started Chapter 2: Your First Program Chapter 3: Listening to Your Users Chapter 4: Decisions Chapter 5: Functions Chapter 6: Lists Chapter 7: Loops Chapter 8: Objects Chapter 9: Exceptions Chapter 10: Intermezzo Chapter 11: Building Your World Chapter 12: Making the World More Interesting Chapter 13: World-Building Part 2 Chapter 14: Econ 101 Chapter 15: Endgame Appendix A: Homework Solutions Appendix B: Common Errors

Table of Contents CHAPTER 1: WELCOME TO LINUX AND MACOS PART I: THE LINUX AND MACOS OPERATING SYSTEMS CHAPTER 2: GETTING STARTED CHAPTER 3: THE UTILITIES CHAPTER 4: THE FILESYSTEM CHAPTER 5: THE SHELL PART II: THE EDITORS CHAPTER 6: THE VIM EDITOR CHAPTER 7: THE EMACS EDITOR PART III: THE SHELLS CHAPTER 8: THE BOURNE AGAIN SHELL (bash) CHAPTER 9: THE TC SHELL (tcsh) PART IV: PROGRAMMING TOOLS CHAPTER 10: PROGRAMMING THE BOURNE AGAIN SHELL (bash) CHAPTER 11: THE PERL SCRIPTING LANGUAGE CHAPTER 12: THE PYTHON PROGRAMMING LANGUAGE CHAPTER 13: THE MARIADB SQL DATABASE MANAGEMENT SYSTEM CHAPTER 14: THE AWK PATTERN PROCESSING LANGUAGE CHAPTER 15: THE SED EDITOR PART V: SECURE NETWORK UTILITIES CHAPTER 16: THE RSYNC SECURE COPY UTILITY CHAPTER 17: THE OPENSSH SECURE COMMUNICATION UTILITIES PART VI: COMMAND REFERENCE PART VII: APPENDIXES APPENDIX A: REGULAR EXPRESSIONS APPENDIX B: HELP APPENDIX C: Keeping the System Up-to-Date APPENDIX D: MACOS NOTES

Key Features An easy to follow guide taking you through every step of the data wrangling process in the best possible way Work with different types of datasets, and reshape the layout of your data to make it easier for analysis Simple examples and real-life data wrangling solutions for data pre-processing Book Description Around 80% of time in data analysis is spent on cleaning and preparing data for analysis. This is, however, and important task, and is a prerequisite to the rest of the data analysis workflow, including visualization, analysis and reporting. Python and R are considered a popular choice of tool for data analysis, and have packages which can be best used to manipulate different kinds of data, as per your requirement. This book will show you the different data wrangling techniques, and how you can leverage the power of Python and R packages to implement them. You will start with understanding the data wrangling process and get a solid foundation for working with different types of data. You will work with different data structures and aqquire and parse data from various locations. The book will also show you how to reshape the layout of data and manipulate, summarize, and join data sets. Finally, the book includes a quick primer on accessing and processing data from databases, conduct data exploration, and store and retrieve data quickly using databases. The book will include practical examples on each of the above pointers using simple and real-world datasets for easier understanding. By the end of the book, you will have a thorough understanding of all the data wrangling concepts and how to implement them in the best possible way. What you will learn Read a csv file into python and R, and print out some statistics on the data. Gain knowledge of the data formats and programming stuctures involved in retrieving API data. Make effective use of regular expression in the data wrangling process. Explore the tools and packages available for preparing numerical data for analysis. Learn how to have better control over the manupulation of the structure of the data. Create a dexterity for programmatically reading, auditing, correcting, and shaping data. Write and complete programs for taking in, formatting and outputting datasets.

这是一本专注于预测建模的数据分析书，意在为实践者提供预测建模过程的指导，比如如何进行数据预处理、模型调优、预测变量重要性度量、变量选择等。读者可以从中学到许多建模方法以及提高对许多常用的、现代的有效模型的认识，如线性回归、非线性回归和分类模型，涉及树方法、支持向量机等。第10章和第17章分别研究混凝土混合物的抗压强度和作业调度两个案例。 　　作者重实际应用，轻数学理论，从实际数据出发，结合开源软件R语言来求解实际问题，详细给出R代码和处理的步骤。R包AppliedPredictiveModeling包含书中使用的数据，以及可以用于重复书中每一章分析的R代码，让读者能在一定精度范围内重复本书的结果，并自然地将书中的预测建模方法应用到自己的数据上。章后附有习题，方便读者巩固所学。 　　这本业界互相推荐的好书，适合所有数据分析人员阅读。

Early Release 完整版 Deep learning is applicable to a widening range of artificial intelligence problems, such as image classification, speech recognition, text classification, question answering, text-to-speech, and optical character recognition. Deep Learning with Python is structured around a series of practical code examples that illustrate each new concept introduced and demonstrate best practices. By the time you reach the end of this book, you will have become a Keras expert and will be able to apply deep learning in your own projects.

统计学习理论大师Vapnik的 The Nature Of Statistical Learning第二版。这是真正的高清文字完整版。

Table of Contents Part I: Data Chapter 1: Social Media Data Chapter 2: From Data to Insights Chapter 3: Luis Madureira Part II: Defining Analytics in Social Media and Types of Analytics Tools Chapter 4: Analytics in Social Media Chapter 5: Dedicated vs. Hybrid Tools Chapter 6: Alexander and Frederik Peiniger Part III: Differences of Social Media Networks Chapter 7: Social Network Landscape Chapter 8: Tam Su Part IV: The Analytics Process Chapter 9: The Analytics Process Chapter 10: Armando Terribili Part V: Metrics, Dashboards, and Reports Chapter 11: Metrics Chapter 12: Dashboards Chapter 13: Reports Chapter 14: Milan Veverka Part VI: Strategy and Tactics Chapter 15: Strategy Chapter 16: Tactics Chapter 17: Michael Wu Part VII: The Future Chapter 18: Prescriptive Analytics Chapter 19: The Future of Social Media Analytics

Table of Contents Chapter 1: Getting Started Chapter 2: Build a Hi-Lo Guessing Game App! Chapter 3: Creating a GUI for Our Guessing Game Chapter 4: Creating Your First Android App Chapter 5: Polishing Your App by Adding Menus and Preferences Chapter 6: Deciphering Secret Messages Chapter 7: Creating Advanced GUIs and Sharing Your App Chapter 8: Make Secret Messages a Phone App to Share with Friends! Chapter 9: Paint Colorful Bubbles with Your Mouse! Chapter 10: Adding Animation and Collision Detection with Timers Chapter 11: Making BubbleDraw a Multitouch Android App Appendix: Debugging and Avoiding Common Errors in Java

Deep Learning With Python Tap The Power of TensorFlow and Theano with Keras, Develop Your First Model, Achieve State-Of-The-Art Results Deep learning is the most interesting and powerful machine learning technique right now. Top deep learning libraries are available on the Python ecosystem like Theano and TensorFlow. Tap into their power in a few lines of code using Keras, the best-of-breed applied deep learning library. In this mega Ebook is written in the friendly Machine Learning Mastery style that you’re used to, learn exactly how to get started and apply deep learning to your own machine learning projects. After purchasing you will get: 256 Page PDF Ebook. 66 Python Recipes. 18 Step-by-Step Lessons. 9 End-to-End Projects.

Machine Learning Mastery With Python Discover The Fastest Growing Platform For Professional Machine Learning With Step-By-Step Tutorials and End-To-End Projects The Python ecosystem with scikit-learn and pandas is required for operational machine learning. Python is the rising platform for professional machine learning because you can use the same code to explore different models in R&D then deploy it directly to production. In this mega Ebook written in the friendly Machine Learning Mastery style that you’re used to, learn exactly how to get started and apply machine learning using the Python ecosystem. You get: 178 Page PDF Ebook. 74 Python Recipes using scikit-learn and Pandas. 16 Step-by-Step Lessons. 3 End-to-End Projects.

Table of Contents Chapter 1 The Basics Chapter 2 Database Administration Chapter 3 PSQL Chapter 4 PgAdmin III – Graphical GUI Chapter 5 Data Types and other objects Chapter 6 Tables, Constraints, and Indexes Chapter 7 SQL the PostgreSQL way Chapter 8 Writing Functions Chapter 9 Query Performance Tuning Chapter 10 Replication and External data Appendix A. Installing PostgreSQL Appendix B. PostgreSQL Packaged Command-Line Tools

Topics Covered The topics covered in this book are An overview of decision trees and random forests A manual example of how a human would classify a dataset, compared to how a decision tree would work How a decision tree works, and why it is prone to overfitting How decision trees get combined to form a random forest How to use that random forest to classify data and make predictions How to determine how many trees to use in a random forest Just where does the "randomness" come from Out of Bag Errors & Cross Validation - how good of a fit did the machine learning algorithm make? Gini Criteria & Entropy Criteria - how to tell which split on a decision tree is best among many possible choices And More

At its core, Bayes' Theorem is a simple probability and statistics formula that has revolutionized how we understand and deal with uncertainty. If life is seen as black and white, Bayes' Theorem helps us think about the gray areas. When new evidence comes our way, it helps us update our beliefs and create a new belief. Ready to dig in and visually explore Bayes' Theorem? Let’s go! Over 60 hand-drawn visuals are included throughout the book to help you work through each problem as you learn by example. The beautifully hand-drawn visual illustrations are specifically designed and formatted for the kindle. This book also includes sections not found in other books on Bayes' Rule. These include: A short tutorial on how to understand problem scenarios and find P(B), P(A), and P(B|A). - For many people, knowing how to approach scenarios and break them apart can be daunting. In this booklet, we provide a quick step-by-step reference on how to confidently understand scenarios. A few examples of how to think like a Bayesian in everyday life. Bayes' Rule might seem somewhat abstract, but it can be applied to many areas of life and help you make better decisions. Learn how Bayes can help you with critical thinking, problem-solving, and dealing with the gray areas of life. A concise history of Bayes' Rule. - Bayes' Theorem has a fascinating 200+ year history, and we have summed it up for you in this booklet. From its discovery in the 1700’s to its being used to break the German’s Enigma Code during World War 2. Fascinating real-life stories on how Bayes' formula is used everyday.From search and rescue to spam filtering and driverless cars, Bayes is used in many areas of modern day life. An expanded Bayes' Theorem definition, including notations, and proof section. - In this section we define core elementary bayesian statistics terms more concretely. A recommended readings sectionFrom The Theory That Would Not Die to Think Bayes: Bayesian Statistics in Pythoni> and many more, there are a number of fantastic resources we have collected for further reading.

The definitive guide for statisticians and data scientists who understand the advantages of becoming proficient in both R and Python The first book of its kind, Python for R Users: A Data Science Approach makes it easy for R programmers to code in Python and Python users to program in R. Short on theory and long on actionable analytics, it provides readers with a detailed comparative introduction and overview of both languages and features concise tutorials with command-by-command translations—complete with sample code—of R to Python and Python to R.

Book Description Looking for complete instructions on manipulating, processing, cleaning, and crunching structured data in Python? The second edition of this hands-on guide—updated for Python 3.5 and Pandas 1.0—is packed with practical cases studies that show you how to effectively solve a broad set of data analysis problems, using Python libraries such as NumPy, pandas, matplotlib, and IPython. Written by Wes McKinney, the main author of the pandas library, Python for Data Analysis also serves as a practical, modern introduction to scientific computing in Python for data-intensive applications. It’s ideal for analysts new to Python and for Python programmers new to scientific computing. Table of Contents Chapter 1 Preliminaries Chapter 2 Python Language Basics, IPython, and Jupyter Notebooks Chapter 3 Built-in Data Structures, Functions, and Files Chapter 4 NumPy Basics: Arrays and Vectorized Computation Chapter 5 Getting Started with pandas Chapter 6 Data Loading, Storage, and File Formats Chapter 7 Data Cleaning and Preparation Chapter 8 Data Wrangling: Join, Combine, and Reshape Chapter 9 Plotting and Visualization Chapter 10 Data Aggregation and Group Operations Chapter 11 Interlude: Data Analysis Examples Chapter 12 Time Series Chapter 13 Advanced NumPy Chapter 14 Using Modeling Libraries with pandas Chapter 15 Examples Data Sets Appendix Advanced IPython and Jupyter

Book Description: What will you learn from this book? It’s an exciting time to be agile! Finally, our industry has found a real, sustainable way to solve problems that have perplexed generations of software developers. Agile not only leads to great results, but teams say they also have a much better time at work. Yet … if agile is so great, why isn’t everyone doing it? It turns out that agile can work well for one team and cause serious problems for another. The difference is team mindset. With this brain-friendly guide, you’ll change the way you think about your projects—for the better! Preparing for your PMI-ACP certification? This book has everything you need to pass the exam: a complete study guide, tips, exam questions, and a full-length practice PMI-ACP exam. Why does this book look so different? Based on the latest research in cognitive science and learning theory, Head First Agile uses a visually rich format to engage your mind, rather than a text-heavy approach that puts you to sleep. Why waste your time struggling with new concepts? This multi-sensory learning experience is designed for the way your brain really works.

This book starts off by laying the foundation for Natural Language Processing and why Python is one of the best options to build an NLP-based expert system with advantages such as Community support, availability of frameworks and so on. Later it gives you a better understanding of available free forms of corpus and different types of dataset. After this, you will know how to choose a dataset for natural language processing applications and find the right NLP techniques to process sentences in datasets and understand their structure. You will also learn how to tokenize different parts of sentences and ways to analyze them.

Concurrency can be notoriously difficult to get right, but fortunately, the Go programming language was designed with concurrency in mind. In this practical book, you’ll learn how Go was written to help introduce and master these concepts, as well as how to use basic concurrency patterns to form large systems that are reliable and remain simple and easy to understand.

The authors demonstrate how treating text as data frames enables you to manipulate, summarize, and visualize characteristics of text. You’ll also learn how to integrate natural language processing (NLP) into effective workflows. Practical code examples and data explorations will help you generate real insights from literature, news, and social media.

Table of Contents LEARNING HOW TO PROGRAM Lesson 1 - Why should you learn how to program? Lesson 2 - Basic principles of learning a programming language UNIT 1 - VARIABLES, TYPES, EXPRESSIONS, AND STATEMENTS Lesson 3 - Introducing Python: a programming language Lesson 4 - Variables and expressions: giving names and values to things Lesson 5 - Object types and statements of code 46 Lesson 6 - Capstone project: your first Python program-convert hours to minutes UNIT 2 - STRINGS, TUPLES, AND INTERACTING WITH THE USER Lesson 7 - Introducing string objects: sequences of characters Lesson 8 - Advanced string operations Lesson 9 - Simple error messages Lesson 10 - Tuple objects: sequences of any kind of object Lesson 11 - Interacting with the user Lesson 12 - Capstone project: name mashup UNIT 3 - MAKING DECISIONS IN YOUR PROGRAMS Lesson 13 - Introducing decisions in programs Lesson 14 - Making more-complicated decisions Lesson 15 - Capstone project: choose your own adventure UNIT 4 - REPEATING TASKS Lesson 16 - Repeating tasks with loops Lesson 17 - Customizing loops Lesson 18 - Repeating tasks while conditions hold Lesson 19 - Capstone project: Scrabble, Art Edition UNIT 5 - ORGANIZING YOUR CODE INTO REUSABLE BLOCKS Lesson 20 - Building programs to last Lesson 21 - Achieving modularity and abstraction with functions Lesson 22 - Advanced operations with functions Lesson 23 - Capstone project: analyze your friends UNIT 6 - WORKING WITH MUTABLE DATA TYPES Lesson 24 - Mutable and immutable objects Lesson 25 - Working with lists Lesson 26 - Advanced operations with lists Lesson 27 - Dictionaries as maps between objects Lesson 28 - Aliasing and copying lists and dictionaries Lesson 29 - Capstone project: document similarity UNIT 7 - MAKING YOUR OWN OBJECT TYPES BY USING OBJECT-ORIENTED PROGRAMMING Lesson 30 - Making your own object types Lesson 31 - Creating a class for an object type Lesson 32 - Working with your own object types Lesson 33 - Customizing classes Lesson

Who this book is for This book is for machine learning practitioners, data scientists, and deep learning enthusiasts who want to take their machine learning skills to the next level by building real-world projects. The intermediate-level guide will help you to implement libraries from the Python ecosystem to build a variety of projects addressing various machine learning domains. Knowledge of Python programming and machine learning concepts will be helpful. Table of Contents: The Python Machine Learning Ecosystem Build an App to Find Underpriced Apartments Build an App to Find Cheap Airfares Forecast the IPO Market Using Logistic Regression Create a Custom Newsfeed Predict whether Your Content Will Go Viral Use Machine Learning to Forecast the Stock Market Classifying Images with Convolutional Neural Networks Building a Chatbot Build a Recommendation Engine What's next?

Author Ankur Patel provides practical knowledge on how to apply unsupervised learning using two simple, production-ready Python frameworks - scikit-learn and TensorFlow using Keras. With the hands-on examples and code provided, you will identify difficult-to-find patterns in data and gain deeper business insight, detect anomalies, perform automatic feature engineering and selection, and generate synthetic datasets. All you need is programming and some machine learning experience to get started.

Table of Contents: PART 1 - DOCKER FUNDAMENTALS Chapter 1 Discovering Docker Chapter 2 Understanding Docker: Inside The Engine Room PART 2 - DOCKER AND DEVELOPMENT Chapter 1 Using Docker As A Lightweight Virtual Machine Chapter 2 Building Images Chapter 3 Running Containers Chapter 4 Day-To-Day Docker Chapter 5 Configuration Management: Getting Your House In Order PART 3 - DOCKER AND DEVOPS Chapter 1 Continuous Integration: Speeding Up Your Development Pipeline Chapter 2 Continuous Delivery: A Perfect Fit For Docker Principles Chapter 3 Network Simulation: Realistic Environment Testing Without The Pain PART 4 - ORCHESTRATION FROM A SINGLE MACHINE TO THE CLOUD Chapter 1 A Primer On Container Orchestration Chapter 2 The Data Center As An Os With Docker Chapter 3 Docker Platforms PART 5 - DOCKER IN PRODUCTION Chapter 1 Docker And Security Chapter 2 Plain Sailing: Running Docker In Production Chapter 3 Docker In Production: Dealing With Challenges

Book Description: A straightforward, visual guide that shows young learners how to build their own computer projects using Scratch, a popular free programming language, using fun graphics and easy-to-follow instructions. Kids can animate their favorite characters, build games to play with friends, create silly sound effects, and more with Coding Projects in Scratch. All they need is a desktop or laptop with Adobe 10.2 or later, and an internet connection to download Scratch 2.0. Coding can be done without download on https://scratch.mit.edu. Step-by-step instructions teach essential coding basics and outline 18 fun and exciting projects, including a personalized birthday card; a "tunnel of doom" multiplayer game; a dinosaur dance party animation with flashing lights, music, and dance moves—and much more. The simple, logical steps in Coding Projects in Scratch are fully illustrated with fun pixel art and build on the basics of coding, so that kids can have the skills to make whatever kind of project they can dream up. Supporting STEM education initiatives, computer coding teaches kids how to think creatively, work collaboratively, and reason systematically, and is quickly becoming a necessary and sought-after skill. DK's computer coding books are full of fun exercises with step-by-step guidance, making them the perfect introductory tools for building vital skills in computer programming.

Hands-On Machine Learning with Scikit-Learn and TensorFlow 的第二版。这是Eearly Release版，只有前9章，请谨慎下载。

Table of Contents Unit 0 - GETTING STARTED Chapter 1. Get Ready, Get Set, Go Unit 1 - IMPERATIVE PROGRAMMING Chapter 1. A Glorified Calculator Chapter 2. Loops And Branches Chapter 3. Variable Scope Chapter 4. Capstone: Ticket To Mars Unit 2 - TYPES Chapter 1. Real Numbers Chapter 2. Whole Numbers Chapter 3. Big Numbers Chapter 4. Multilingual Text Chapter 5. Converting Between Types Chapter 6. Capstone: The Vigenère Cipher Unit 3 - BUILDING BLOCKS Chapter 1. Functions Chapter 2. Methods Chapter 3. First-Class Functions Chapter 4. Capstone: Temperature Tables Unit 4 - COLLECTIONS Chapter 1. Arrayed In Splendor Chapter 2. Slices: Windows Into Arrays Chapter 3. A Bigger Slice Chapter 4. The Ever-Versatile Map Chapter 5. Capstone: A Slice Of Life Unit 5 - STATE AND BEHAVIOR Chapter 1. A Little Structure Chapter 2. Go'S Got No Class Chapter 3. Composition And Forwarding Chapter 4. Interfaces Chapter 5. Capstone: Martian Animal Sanctuary Unit 6 - DOWN THE GOPHER HOLE Chapter 1. A Few Pointers Chapter 2. Much Ado About Nil Chapter 3. To Err Is Human Chapter 4. Capstone: Sudoku Rules Unit 7 - CONCURRENT PROGRAMMING Chapter 1. Goroutines And Concurrency Chapter 2. Concurrent State Chapter 3. Capstone: Life On Mars

Book Description Take a deep dive into the concepts of machine learning as they apply to contemporary business and management. You will learn how machine learning techniques are used to solve fundamental and complex problems in society and industry. Machine Learning for Decision Makers serves as an excellent resource for establishing the relationship of machine learning with IoT, big data, and cognitive and cloud computing to give you an overview of how these modern areas of computing relate to each other. This book introduces a collection of the most important concepts of machine learning and sets them in context with other vital technologies that decision makers need to know about. These concepts span the process from envisioning the problem to applying machine-learning techniques to your particular situation. This discussion also provides an insight to help deploy the results to improve decision-making. The book uses case studies and jargon busting to help you grasp the theory of machine learning quickly. You'll soon gain the big picture of machine learning and how it fits with other cutting-edge IT services. This knowledge will give you confidence in your decisions for the future of your business. What You Will Learn Discover the machine learning, big data, and cloud and cognitive computing technology stack Gain insights into machine learning concepts and practices Understand business and enterprise decision-making using machine learning Absorb machine-learning best practices Who This Book Is For Managers tasked with making key decisions who want to learn how and when machine learning and related technologies can help them. Table of Contents Chapter 1: Let’s Integrate with Machine Learning Chapter 2: The Practical Concepts of Machine Learning Chapter 3: Machine Learning Algorithms and Their Relationship with Modern Technologies Chapter 4: Technology Stack for Machine Learning and Associated Technologies Chapter 5: Industrial Applications of Machine Learning Chapter 6: I Am the Future: Machine Learning in Action Chapter 7: Innovation, KPIs, Best Practices, and More for Machine Learning Chapter 8: Do Not Forget Me: The Human Side of Machine Learning Chapter 9: Let’s Wrap Up: The Final Destination Appendix A: How to Architect and Build a Machine Learning Solution Appendix B: A Holistic Machine Learning and Agile-Based Software Methodology Appendix C: Data Processing Technologies

Book Description: Learn best practices for building bots by focusing on the technological implementation and UX in this practical book. You will cover key topics such as setting up a development environment for creating chatbots for multiple channels (Facebook Messenger, Skype, and KiK); building a chatbot (design to implementation); integrating to IFTT (If This Then That) and IoT (Internet of Things); carrying out analytics and metrics for chatbots; and most importantly monetizing models and business sense for chatbots. Build Better Chatbots is easy to follow with code snippets provided in the book and complete code open sourced and available to download. With Facebook opening up its Messenger platform for developers, followed by Microsoft opening up Skype for development, a new channel has emerged for brands to acquire, engage, and service customers on chat with chatbots. What You Will Learn Work with the bot development life cycle Master bot UX design Integrate into the bot ecosystem Maximize the business and monetization potential for bots Who This Book Is For Developers, programmers, and hobbyists who have basic programming knowledge. The book can be used by existing chatbot developers to gain a better understanding of analytics and the business side of bots.

Book Description: Master the basics of solving logic puzzles, and creating algorithms using Swift on Apple platforms. This book is based on the curriculum currently being used in common computer classes. You’ll learn to automate algorithmic processes that scale using Swift in the context of iOS, macOS, tvOS, and watchOS. Begin by understanding how to think computationally: to formulate a computational problem and recognize patterns and ways to validate it. Then jump ahead past the abstractions and conceptual work into using code snippets to build frameworks and write code using Xcode and Swift. Once you have frameworks in place, you’ll learn to use algorithms and structure data. Finally, you’ll see how to bring people into what you’ve built through a useable UI and how UI and code relate. What You’ll Learn Recognize patterns and use abstractions Build code into reusable frameworks Manage code and share version control Solve logic puzzles Who This Book Is For Young professionals interested in learning computer science from an Apple platform standpoint.

Book Description Build a microservices architecture with Spring Boot, by evolving an application from a small monolith to an event-driven architecture composed of several services. This book follows an incremental approach to teach microservice structure, test-driven development, Eureka, Ribbon, Zuul, and end-to-end tests with Cucumber. Author Moises Macero follows a very pragmatic approach to explain the benefits of using this type of software architecture, instead of keeping you distracted with theoretical concepts. He covers some of the state-of-the-art techniques in computer programming, from a practical point of view. You'll focus on what's important, starting with the minimum viable product but keeping the flexibility to evolve it. What You'll Learn Build microservices with Spring Boot Use event-driven architecture and messaging with RabbitMQ Create RESTful services with Spring Master service discovery with Eureka and load balancing with Ribbon Route requests with Zuul as your API gateway Write end-to-end rest tests for an event-driven architecture using Cucumber Carry out continuous integration and deployment Who This Book Is For Those with at least some prior experience with Java programming. Some prior exposure to Spring Boot recommended but not required. Table of Contents Chapter 1: Introduction Chapter 2: The Basic Spring Boot Application Chapter 3: A Real Three-Tier Spring Boot Application Chapter 4: Starting with Microservices Chapter 5: The Microservices Journey Through Tools Chapter 6: Testing the Distributed System Appendix A: Upgrading to Spring Boot 2.0

Table of Contents Chapter 1: Introduction to the Django Framework Chapter 2: Django Urls and Views Chapter 3: Django Templates Chapter 4: Jinja Templates in Django Chapter 5: Django Application Management Chapter 6: Django Forms Chapter 7: Django Models Chapter 8: Django Model Queries and Managers Chapter 9: Django Model Forms and Class Views Chapter 10: Django User Management Chapter 11: Django admin Management Chapter 12: REST Services with Django Appendix A: Python Basics

Book Description Quickly find solutions to common programming problems encountered while processing big data. Content is presented in the popular problem-solution format. Look up the programming problem that you want to solve. Read the solution. Apply the solution directly in your own code. Problem solved! PySpark Recipes covers Hadoop and its shortcomings. The architecture of Spark, PySpark, and RDD are presented. You will learn to apply RDD to solve day-to-day big data problems. Python and NumPy are included and make it easy for new learners of PySpark to understand and adopt the model. What You Will Learn Understand the advanced features of PySpark2 and SparkSQL Optimize your code Program SparkSQL with Python Use Spark Streaming and Spark MLlib with Python Perform graph analysis with GraphFrames Who This Book Is For Data analysts, Python programmers, big data enthusiasts Table of Contents Chapter 1: The Era of Big Data, Hadoop, and Other Big Data Processing Frameworks Chapter 2: Installation Chapter 3: Introduction to Python and NumPy Chapter 4: Spark Architecture and the Resilient Distributed Dataset Chapter 5: The Power of Pairs: Paired RDDs Chapter 6: I/O in PySpark Chapter 7: Optimizing PySpark and PySpark Streaming Chapter 8: PySparkSQL Chapter 9: PySpark MLlib and Linear Regression

Book Description Master reinforcement learning, a popular area of machine learning, starting with the basics: discover how agents and the environment evolve and then gain a clear picture of how they are inter-related. You’ll then work with theories related to reinforcement learning and see the concepts that build up the reinforcement learning process. Reinforcement Learning discusses algorithm implementations important for reinforcement learning, including Markov’s Decision process and Semi Markov Decision process. The next section shows you how to get started with Open AI before looking at Open AI Gym. You’ll then learn about Swarm Intelligence with Python in terms of reinforcement learning. The last part of the book starts with the TensorFlow environment and gives an outline of how reinforcement learning can be applied to TensorFlow. There’s also coverage of Keras, a framework that can be used with reinforcement learning. Finally, you'll delve into Google’s Deep Mind and see scenarios where reinforcement learning can be used. What You'll Learn Absorb the core concepts of the reinforcement learning process Use advanced topics of deep learning and AI Work with Open AI Gym, Open AI, and Python Harness reinforcement learning with TensorFlow and Keras using Python Who This Book Is For Data scientists, machine learning and deep learning professionals, developers who want to adapt and learn reinforcement learning. Table of Contents Chapter 1: Reinforcement Learning Basics Chapter 2: RL Theory and Algorithms Chapter 3: OpenAI Basics Chapter 4: Applying Python to Reinforcement Learning Chapter 5: Reinforcement Learning with Keras, TensorFlow, and ChainerRL Chapter 6: Google’s DeepMind and the Future of Reinforcement Learning

Book Description Java in easy steps, 6th edition instructs you how to easily create your own exciting Java programs. Updated for Java 9, which was released September 2017, it contains separate chapters on the major features of the Java language. Complete example programs with colorized code illustra

第1章　机器学习简介 1 1.1　机器学习的起源 2 1.2　机器学习的使用与滥用 3 1.3　机器如何学习 5 1.3.1　抽象化和知识表达 6 1.3.2　一般化 7 1.3.3　评估学习的成功性 9 1.4　将机器学习应用于数据中的步骤 9 1.5　选择机器学习算法 10 1.5.1　考虑输入的数据 10 1.5.2　考虑机器学习算法的类型 11 1.5.3　为数据匹配合适的算法 13 1.6　使用R进行机器学习 13 1.7　总结 17 第2章　数据的管理和理解 18 2.1　R数据结构 18 2.2　向量 19 2.3　因子 20 2.3.1　列表 21 2.3.2　数据框 22 2.3.3　矩阵和数组 24 2.4　用R管理数据 25 2.4.1　保存和加载R数据结构 25 2.4.2　用CSV文件导入和保存数据 26 2.4.3　从SQL数据库导入数据 27 2.5　探索和理解数据 28 2.5.1　探索数据的结构 29 2.5.2　探索数值型变量 29 2.5.3　探索分类变量 37 2.5.4　探索变量之间的关系 39 2.6　总结 42 第3章　懒惰学习——使用近邻分类 44 3.1　理解使用近邻进行分类 45 3.1.1　kNN算法 45 3.1.2　为什么kNN算法是懒惰的 51 3.2　用kNN算法诊断乳腺癌 51 3.2.1　第1步——收集数据 51 3.2.2　第2步——探索和准备数据 52 3.2.3　第3步——基于数据训练模型 55 3.2.4　第4步——评估模型的性能 57 3.2.5　第5步——提高模型的性能 58 3.3　总结 60 第4章　概率学习——朴素贝叶斯分类 61 4.1　理解朴素贝叶斯 61 4.1.1　贝叶斯方法的基本概念 62 4.1.2　朴素贝叶斯算法 65 4.2　例子——基于贝叶斯算法的手机垃圾短信过滤 70 4.2.1　第1步——收集数据 70 4.2.2　第2步——探索和准备数据 71 4.2.3　数据准备——处理和分析文本数据 72 4.2.4　第3步——基于数据训练模型 78 4.2.5　第4步——评估模型的性能 79 4.2.6　第5步——提升模型的性能 80 4.3　总结 81 第5章　分而治之——应用决策树和规则进行分类 82 5.1　理解决策树 82 5.1.1　分而治之 83 5.1.2　C5.0决策树算法 86 5.2　例子——使用C5.0决策树识别高风险银行贷款 89 5.2.1　第1步——收集数据 89 5.2.2　第2步——探索和准备数据 89 5.2.3　第3步——基于数据训练模型 92 5.2.4　第4步——评估模型的性能 95 5.2.5　第5步——提高模型的性能 95 5.3　理解分类规则 98 5.3.1　独立而治之 99 5.3.2　单规则（1R）算法 101 5.3.3　RIPPER算法 103 5.3.4　来自决策树的规则 105 5.4　例子——应用规则学习识别有毒的蘑菇 105 5.4.1　第1步——收集数据 106 5.4.2　第2步——探索和准备数据 106 5.4.3　第3步——基于数据训练模型 107 5.4.4　第4步——评估模型的性能 109 5.4.5　第5步——提高模型的性能 109 5.5　总结 111 第6章　预测数值型数据——回归方法 113 6.1　理解回归 113 6.1.1　简单线性回归 115 6.1.2　普通最小二乘估计 117 6.1.3　相关系数 118 6.1.4　多元线性回归 120 6.2　例子——应用线性回归预测医疗费用 122 6.2.1　第1步——收集数据 122 6.2.2　第2步——探索和准备数据 123 6.2.3　第3步——基于数据训练模型 127 6.2.4　第4步——评估模型的性能 129 6.2.5　第5步——提高模型的性能 130 6.3　理解回归树和模型树 133 6.4　例子——用回归树和模型树估计葡萄酒的质量 135 6.4.1　第1步——收集数据 135 6.4.2　第2步——探索和准备数据 136 6.4.3　第3步——基于数据训练模型 137 6.4.4　第4步——评估模型的性能 140 6.4.5　第5步——提高模型的性能 142 6.5　总结 144 第7章　黑箱方法——神经网络和支持向量机 146 7.1　理解神经网络 146 7.1.1　从生物神经元到人工神经元 148 7.1.2　激活函数 148 7.1.3　网络拓扑 151 7.1.4　用后向传播训练神经网络 153 7.2　用人工神经网络对混凝土的强度进行建模 154 7.2.1　第1步——收集数据 154 7.2.2　第2步——探索和准备数据 155 7.2.3　第3步——基于数据训练模型 156 7.2.4　第4步——评估模型的性能 158 7.2.5　第5步——提高模型的性能 159 7.3　理解支持向量机 160 7.3.1　用超平面分类 161 7.3.2　寻找最大间隔 161 7.3.3　对非线性空间使用核函数 164 7.4　用支持向量机进行光学字符识别 165 7.4.1　第1步——收集数据 166 7.4.2　第2步——探索和准备数据 166 7.4.3　第3步——基于数据训练模型 167 7.4.4　第4步——评估模型的性能 169 7.4.5　第5步——提高模型的性能 170 7.5　总结 171 第8章　探寻模式——基于关联规则的购物篮分析 172 8.1　理解关联规则 172 8.2　例子——用关联规则确定经常一起购买的食品杂货 176 8.2.1　第1步——收集数据 176 8.2.2　第2步——探索和准备数据 177 8.2.3　第3步——基于数据训练模型 183 8.2.4　第4步——评估模型的性能 184 8.2.5　第5步——提高模型的性能 187 8.3　总结 189 第9章　寻找数据的分组——k均值聚类 191 9.1　理解聚类 191 9.1.1　聚类——一种机器学习任务 192 9.1.2　k均值聚类算法 193 9.1.3　用k均值聚类探寻青少年市场细分 198 9.1.4　第1步——收集数据 198 9.1.5　第2步——探索和准备数据 199 9.1.6　第3步——基于数据训练模型 202 9.1.7　第4步——评估模型的性能 204 9.1.8　第5步——提高模型的性能 206 9.2　总结 207 第10章　模型性能的评价 208 10.1　度量分类方法的性能 208 10.1.1　在R中处理分类预测数据 209 10.1.2　深入探讨混淆矩阵 211 10.1.3　使用混淆矩阵度量性能 212 10.1.4　准确度之外的其他性能评价指标 214 10.1.5　性能权衡的可视化 221 10.2　评估未来的性能 224 10.2.1　保持法 225 10.2.2　交叉验证 226 10.2.3　自助法抽样 229 10.3　总结 229 第11章　提高模型的性能 231 11.1　调整多个模型来提高性能 231 11.2　使用元学习来提高模型的性能 239 11.2.1　理解集成学习 239 11.2.2　bagging 241 11.2.3　boosting 243 11.2.4　随机森林 244 11.3　总结 248 第12章　其他机器学习主题 249 12.1　分析专用数据 250 12.1.1　用RCurl添加包从网上获取数据 250 12.1.2　用XML添加包读/写XML格式数据 250 12.1.3　用rjson添加包读/写JSON 251 12.1.4　用xlsx添加包读/写Microsoft Excel电子表格 251 12.1.5　生物信息学数据 251 12.1.6　社交网络数据和图数据 252 12.2　提高R语言的性能 252 12.2.1　处理非常大的数据集 253 12.2.2　使用并行处理来加快学习过程 254 12.2.3　GPU计算 257 12.2.4　部署最优的学习算法 257 12.3　总结 258

Are you a seasoned Java developer who wishes to learn Python? Perhaps you’ve just joined a project where a chunk of system integration code is written in Python. Or maybe you need to implement a report generation module in the next sprint and your colleague mentioned that Python would be the perfect tool for the job. In any case, if you are in a situation where you have to pick up the Python programming language overnight, this book is just for you! Hit the ground running and gain a fast-paced overview of what the Python language is all about, the syntax that it uses and the ecosystem of libraries and tools that surround the language. This concise book doesn’t spend time on details from an introductory programming course or document every single Python feature. Instead, Python for the Busy Java Developer is designed for experienced Java developers to obtain sufficient familiarity with the language and dive into coding, quickly. What You'll Learn Discover the fundamentals of the core Python language and how they compare to Java Understand Python syntax and the differences between Python 2.x and 3.x Explore the Python ecosystem, its standard libraries, and how to implement them

内容简介 《世界著名计算机教材精选：数据挖掘十大算法》详细介绍了在实际中用途最广、影响最大的十种数据挖掘算法，这十种算法是数据挖掘领域的顶级专家进行投票筛选的，覆盖了分类、聚类、统计学习、关联分析和链接分析等重要的数据挖掘研究和发展主题。《世界著名计算机教材精选：数据挖掘十大算法》对每一种算法都进行了多个角度的深入剖析，包括算法历史、算法过程、算法特性、软件实现、前沿发展等，此外，在每章最后还给出了丰富的习题和精挑细选的参考文献，对于读者掌握算法基本知识和进一步研究都非常有价值，对数据挖掘、机器学习和人工智能等学科的课程的设计有指导意义。 目录 第1章C4.5 1 1.1引言2 1.2算法描述3 1.3算法特性6 1.3.1决策树剪枝6 1.3.2连续型属性8 1.3.3缺失值处理8 1.3.4规则集诱导9 1.4软件实现10 1.5示例10 1.5.1 Golf数据集10 1.5.2 Soybean数据集11 1.6高级主题11 1.6.1二级存储12 1.6.2斜决策树12 1.6.3特征选择12 1.6.4集成方法12 1.6.5分类规则13 1.6.6模型重述13 1.7习题14 参考文献15 第2章k-means18 2.1引言19 2.2算法描述19 2.3可用软件22 2.4示例23 2.5高级主题27 2.6小结28 2.7习题28 参考文献29 第3章SVM： 支持向量机31 3.1支持向量分类器32 3.2支持向量分类器的软间隔优化34 3.3核技巧35 3.4理论基础38 3.5支持向量回归器40 3.6软件实现41 3.7当前和未来的研究41 3.7.1计算效率41 3.7.2核的选择41 3.7.3泛化分析42 3.7.4结构化支持向量机的学习42 3.8习题43 参考文献44 第4章Apriori47 4.1引言48 4.2算法描述48 4.2.1挖掘频繁模式和关联规则48 4.2.2挖掘序列模式52 4.2.3讨论53 4.3软件实现54 4.4示例55 4.4.1可行示例55 4.4.2性能评估60 4.5高级主题61 4.5.1改进Apriori类型的频繁模式挖掘61 4.5.2无候选的频繁模式挖掘62 4.5.3增量式方法63 4.5.4稠密表示: 闭合模式和最大模式63 4.5.5量化的关联规则64 4.5.6其他的重要性/兴趣度度量方法65 4.5.7类别关联规则66 4.5.8使用更丰富的形式： 序列、树和图66 4.6小结67 4.7习题67 参考文献68 第5章EM72 5.1引言73 5.2算法描述74 5.3软件实现74 5.4示例75 5.4.1例5.1： 多元正态混合75 5.4.2例5.2: 混合因子分析78 5.5高级主题80 5.6习题81 参考文献87 第6章PageRank90 6.1引言91 6.2算法描述92 6.3一个扩展：Timed-PageRank95 6.4小结96 6.5习题96 参考文献97 第7章AdaBoost98 7.1引言99 7.2算法描述99 7.2.1符号定义99 7.2.2通用推举过程100 7.2.3AdaBoost算法101 7.3示例103 7.3.1异或问题求解103 7.3.2真实数据上的性能104 7.4实际应用105 7.5高级主题107 7.5.1理论问题107 7.5.2多类别AdaBoost110 7.5.3其他高级主题111 7.6软件实现111 7.7习题112 参考文献113 第8章kNN： k-最近邻115 8.1引言116 8.2算法描述116 8.2.1宏观描述116 8.2.2若干议题117 8.2.3软件实现118 8.3示例118 8.4高级主题120 8.5习题121 致谢121 参考文献122 第9章Naive Bayes124 9.1引言125 9.2算法描述125 9.3独立给力127 9.4模型扩展128 9.5软件实现130 9.6示例130 9.6.1例1130 9.6.2例2132 9.7高级主题133 9.8习题133 参考文献134 第10章CART： 分类和回归树136 10.1前身137 10.2概述138 10.3示例138 10.4算法描述140 10.5分裂准则141 10.6先验概率和类别均衡142 10.7缺失值的处理144 10.8属性的重要度145 10.9动态特征构造146 10.10代价敏感学习147 10.11停止准则、剪枝、树序列和树选择147 10.12概率树149 10.13理论基础150 10.14 CART之后的相关研究150 10.15可用软件151 10.16习题152 参考文献153

Information visualization is a language. Like any language, it can be used for multiple purposes. A poem, a novel, and an essay all share the same language, but each one has its own set of rules. The same is true with information visualization: a product manager, statistician, and graphic designer each approach visualization from different perspectives. Data at Work was written with you, the spreadsheet user, in mind. This book will teach you how to think about and organize data in ways that directly relate to your work, using the skills you already have. In other words, you don’t need to be a graphic designer to create functional, elegant charts: this book will show you how. Although all of the examples in this book were created in Microsoft Excel, this is not a book about how to use Excel. Data at Work will help you to know which type of chart to use and how to format it, regardless of which spreadsheet application you use and whether or not you have any design experience. In this book, you’ll learn how to extract, clean, and transform data; sort data points to identify patterns and detect outliers; and understand how and when to use a variety of data visualizations including bar charts, slope charts, strip charts, scatter plots, bubble charts, boxplots, and more. Because this book is not a manual, it never specifies the steps required to make a chart, but the relevant charts will be available online for you to download, with brief explanations of how they were created.

Quickly learn to program for microcontrollers and IoT devices without a lot of study and expense. MicroPython and controllers that support it eliminate the need for programming in a C-like language, making the creation of IoT applications and devices easier and more accessible than ever. MicroPython for the Internet of Things is ideal for readers new to electronics and the world of IoT. Specific examples are provided covering a range of supported devices, sensors, and MicroPython boards such as Pycom’s WiPy modules and MicroPython’s pyboard. Never has programming for microcontrollers been easier. The book takes a practical and hands-on approach without a lot of detours into the depths of theory. The book: Shows a faster and easier way to program microcontrollers and IoT devices Teaches MicroPython, a variant of one of the most widely used scripting languages Is friendly and accessible to those new to electronics, with fun example projects What You'll Learn Program in MicroPython Understand sensors and basic electronics Develop your own IoT projects Build applications for popular boards such as WiPy and pyboard Load MicroPython on the ESP8266 and similar boards Interface with hardware breakout boards Connect hardware to software through MicroPython Explore the easy-to-use Adafruit IO connecting your microcontroller to the cloud Who This Book Is For Anyone interested in building IoT solutions without the heavy burden of programming in C++ or C. The book also appeals to those wanting an easier way to work with hardware than is provided by the Arduino and the Raspberry Pi platforms.