This highly anticipated fourth edition of the most ...Download Link : readmeaway.com/download?i=0128042915            0128042915 Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems) PDF by Ian H. WittenRead Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems) PDF from Morgan Kaufmann,Ian H. WittenDownload Ian H. Witten's PDF E-book Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems)

    Weinberg shows you exactly how to become a more effective consultant. He reveals specific techniques and strategies that really work.Through the use of vividly memorable rules, laws, and principles -- such as The Law of Raspberry Jam, The Potato Chip Principle, and Lessons from the Farm -- the author shows you how to-- price and market your services-- avoid traps and find alternative approaches-- keep ahead of your clients-- create a special "consultant's survival kit"-- trade improvement for perfection-- negotiate in difficult situations-- measure your effectiveness-- be yourselfYou will also find straightforward advice on marketing your services, including how to-- find clients-- get needed exposure-- set just-right fees-- gain trustThe Secrets of Consulting -- techniques, strategies, and first-hand experiences -- all that you'll need to set up, run, and be successful at your own consulting business.

    But with so many distributed databases and tools available today, it’s often difficult to understand what each one offers and how they differ. With this practical guide, Alex Petrov guides developers through the concepts behind modern database and storage engine internals.Throughout the book, you’ll explore relevant material gleaned from numerous books, papers, blog posts, and the source code of several open source databases. These resources are listed at the end of parts one and two. You’ll discover that the most significant distinctions among many modern databases reside in subsystems that determine how storage is organized and how data is distributed.This book examines:Storage engines: Explore storage classification and taxonomy, and dive into B-Tree-based and immutable log structured storage engines, with differences and use-cases for eachDistributed systems: Learn step-by-step how nodes and processes connect and build complex communication patterns, from UDP to reliable consensus protocolsDatabase clusters: Discover how to achieve consistent models for replicated data

    If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Muller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.With this book, you'll learn:Fundamental concepts and applications of machine learningAdvantages and shortcomings of widely used machine learning algorithmsHow to represent data processed by machine learning, including which data aspects to focus onAdvanced methods for model evaluation and parameter tuningThe concept of pipelines for chaining models and encapsulating your workflowMethods for working with text data, including text-specific processing techniquesSuggestions for improving your machine learning and data science skills

    Adopting Lean will take time and commitment, but it’s vital for harnessing the cultural and technical forces that are accelerating the rate of innovation.* Discover how Lean focuses on people and teamwork at every level, in contrast to traditional management practices* Approach problem-solving experimentally, by exploring solutions, testing assumptions, and getting feedback from real users* Lead and manage large-scale programs in a way that empowers employees, increases the speed and quality of delivery, and lowers costs* Learn how to implement ideas from the DevOps and Lean Startup movements even in complex, regulated environments

    Find out how the Swedish police combined XP, Scrum, and Kanban in a 60-person project. From start to finish, you'll see how to deliver a successful product using Lean principles. We start with an organization in desperate need of a new way of doing things and finish with a group of sixty, all working in sync to develop a scalable, complex system. You'll walk through the project step by step, from customer engagement, to the daily "cocktail party," version control, bug tracking, and release. In this honest look at what works--and what doesn't--you'll find out how to: Make quality everyone's business, not just the testers. Keep everyone moving in the same direction without micromanagement. Use simple and powerful metrics to aid in planning and process improvement. Balance between low-level feature focus and high-level system focus. You'll be ready to jump into the trenches and streamline your own development process.ContentsForewordPrefacePART I: HOW WE WORK1. About the Project1.1 Timeline 51.2 How We Sliced the Elephant 61.3 How We Involved the Customer 72. Structuring the Teams3. Attending the Daily Cocktail Party3.1 First Tier: Feature Team Daily Stand-up3.2 Second Tier: Sync Meetings per Specialty3.3 Third Tier: Project Sync Meeting4. The Project Board4.1 Our Cadences4.2 How We Handle Urgent Issues and Impediments5. Scaling the Kanban Boards6. Tracking the High-Level Goal7. Defining Ready and Done7.1 Ready for Development7.2 Ready for System Test7.3 How This Improved Collaboration 8. Handling Tech Stories8.1 Example 1: System Test Bottleneck8.2 Example 2: Day Before the Release8.3 Example 3: The 7-Meter Class9. Handling Bugs9.1 Continuous System Test9.2 Fix the Bugs Immediately9.3 Why We Limit the Number of Bugs in the Bug Tracker9.4 Visualizing Bugs9.5 Preventing Recurring Bugs10. Continuously Improving the Process10.1 Team Retrospectives10.2 Process Improvement Workshops10.3 Managing the Rate of Change11. Managing Work in Progress11.1 Using WIP Limits11.2 Why WIP Limits Apply Only to Features12. Capturing and Using Process Metrics12.1 Velocity (Features per Week)12.2 Why We Don’t Use Story Points12.3 Cycle Time (Weeks per Feature)12.4 Cumulative Flow12.5 Process Cycle Efficiency13. Planning the Sprint and Release13.1 Backlog Grooming13.2 Selecting the Top Ten Features13.3 Why We Moved Backlog Grooming Out of the Sprint Planning Meeting13.4 Planning the Release14. How We Do Version Control14.1 No Junk on the Trunk14.2 Team Branches14.3 System Test Branch15. Why We Use Only Physical Kanban Boards16. What We Learned16.1 Know Your Goal16.2 Experiment16.3 Embrace Failure16.4 Solve Real Problems16.5 Have Dedicated Change Agents16.6 Involve PeoplePART II: A CLOSER LOOK AT THE TECHNIQUES 17. Agile and Lean in a Nutshell17.1 Agile in a Nutshell17.2 Lean in a Nutshell17.3 Scrum in a Nutshell17.4 XP in a Nutshell17.5 Kanban in a Nutshell18. Reducing the Test Automation Backlog18.1 What to Do About It18.2 How to Improve Test Coverage a Little Bit Each Iteration18.3 Step 1: List Your Test Cases18.4 Step 2: Classify Each Test18.5 Step 3: Sort the List in Priority Order18.6 Step 4: Automate a Few Tests Each Iteration18.7 Does This Solve the Problem?19. Sizing the Backlog with Planning Poker19.1 Estimating Without Planning Poker19.2 Estimating with Planning Poker19.3 Special Cards20. Cause-Effect Diagrams20.1 Solve Problems, Not Symptoms20.2 The Lean Problem-Solving Approach: A3 Thinking20.3 How to Use Cause-Effect Diagrams20.4 Example 1: Long Release Cycle20.5 Example 2: Defects Released to Production20.6 Example 3: Lack of Pair Programming20.7 Example 4: Lots of Problems20.8 Practical Issues: How to Create and Maintain the Diagrams20.9 Pitfalls20.10 Why Use Cause-Effect Diagrams?21. Final WordsA1. Glossary: How We Avoid Buzzword BingoIndex

    Edited by the Sustainability Institute’s Diana Wright, this essential primer brings systems thinking out of the realm of computers and equations and into the tangible world, showing readers how to develop the systems-thinking skills that thought leaders across the globe consider critical for 21st-century life.Some of the biggest problems facing the world—war, hunger, poverty, and environmental degradation—are essentially system failures. They cannot be solved by fixing one piece in isolation from the others, because even seemingly minor details have enormous power to undermine the best efforts of too-narrow thinking.While readers will learn the conceptual tools and methods of systems thinking, the heart of the book is grander than methodology. Donella Meadows was known as much for nurturing positive outcomes as she was for delving into the science behind global dilemmas. She reminds readers to pay attention to what is important, not just what is quantifiable, to stay humble, and to stay a learner.In a world growing ever more complicated, crowded, and interdependent, Thinking in Systems helps readers avoid confusion and helplessness, the first step toward finding proactive and effective solutions.

    More and more frequently, ScrumMasters and project managers are being asked to coach agile teams. But it's a challenging role. It requires new skills--as well as a subtle understanding of when to step in and when to step back. Migrating from "command and control" to agile coaching requires a whole new mind-set. In Coaching Agile Teams, Lyssa Adkins gives agile coaches the insights they need to adopt this new mind-set and to guide teams to extraordinary performance in a re-energized work environment. You'll gain a deep view into the role of the agile coach, discover what works and what doesn't, and learn how to adapt powerful skills from many allied disciplines, including the fields of professional coaching and mentoring. Coverage includes Understanding what it takes to be a great agile coach Mastering all of the agile coach's roles: teacher, mentor, problem solver, conflict navigator, and performance coach Creating an environment where self-organized, high-performance teams can emerge Coaching teams past cooperation and into full collaboration Evolving your leadership style as your team grows and changes Staying actively engaged without dominating your team and stunting its growth Recognizing failure, recovery, and success modes in your coaching Getting the most out of your own personal agile coaching journey Whether you're an agile coach, leader, trainer, mentor, facilitator, ScrumMaster, project manager, product owner, or team member, this book will help you become skilled at helping others become truly great. What could possibly be more rewarding?

    We are living in an age where data comes in abundance, and thanks to the self-learning algorithms from the field of machine learning, we can turn this data into knowledge. Automated speech recognition on our smart phones, web search engines, e-mail spam filters, the recommendation systems of our favorite movie streaming services – machine learning makes it all possible.Thanks to the many powerful open-source libraries that have been developed in recent years, machine learning is now right at our fingertips. Python provides the perfect environment to build machine learning systems productively.This book will teach you the fundamentals of machine learning and how to utilize these in real-world applications using Python. Step-by-step, you will expand your skill set with the best practices for transforming raw data into useful information, developing learning algorithms efficiently, and evaluating results.You will discover the different problem categories that machine learning can solve and explore how to classify objects, predict continuous outcomes with regression analysis, and find hidden structures in data via clustering. You will build your own machine learning system for sentiment analysis and finally, learn how to embed your model into a web app to share with the world

    How can you work with it efficiently? Recently updated for Spark 1.3, this book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. With Spark, you can tackle big datasets quickly through simple APIs in Python, Java, and Scala. This edition includes new information on Spark SQL, Spark Streaming, setup, and Maven coordinates. Written by the developers of Spark, this book will have data scientists and engineers up and running in no time. You’ll learn how to express parallel jobs with just a few lines of code, and cover applications from simple batch jobs to stream processing and machine learning. Quickly dive into Spark capabilities such as distributed datasets, in-memory caching, and the interactive shell Leverage Spark’s powerful built-in libraries, including Spark SQL, Spark Streaming, and MLlib Use one programming paradigm instead of mixing and matching tools like Hive, Hadoop, Mahout, and Storm Learn how to deploy interactive, batch, and streaming applications Connect to data sources including HDFS, Hive, JSON, and S3 Master advanced topics like data partitioning and shared variables

    This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.You'll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts.Develop your understanding of probability and statistics by writing and testing codeRun experiments to test statistical behavior, such as generating samples from several distributionsUse simulations to understand concepts that are hard to grasp mathematicallyLearn topics not usually covered in an introductory course, such as Bayesian estimationImport data from almost any source using Python, rather than be limited to data that has been cleaned and formatted for statistics toolsUse statistical inference to answer questions about real-world data

    They treat it as a craft. They are professionals. In The Clean Coder: A Code of Conduct for Professional Programmers, legendary software expert Robert C. Martin introduces the disciplines, techniques, tools, and practices of true software craftsmanship. This book is packed with practical advice-about everything from estimating and coding to refactoring and testing. It covers much more than technique: It is about attitude. Martin shows how to approach software development with honor, self-respect, and pride; work well and work clean; communicate and estimate faithfully; face difficult decisions with clarity and honesty; and understand that deep knowledge comes with a responsibility to act. Readers will learn What it means to behave as a true software craftsman How to deal with conflict, tight schedules, and unreasonable managers How to get into the flow of coding, and get past writer's block How to handle unrelenting pressure and avoid burnout How to combine enduring attitudes with new development paradigms How to manage your time, and avoid blind alleys, marshes, bogs, and swamps How to foster environments where programmers and teams can thrive When to say "No"-and how to say it When to say "Yes"-and what yes really means Great software is something to marvel at: powerful, elegant, functional, a pleasure to work with as both a developer and as a user. Great software isn't written by machines. It is written by professionals with an unshakable commitment to craftsmanship. The Clean Coder will help you become one of them-and earn the pride and fulfillment that they alone possess.

    But many of those failures are preventable. The Lean Startup is a new approach being adopted across the globe, changing the way companies are built and new products are launched. Eric Ries defines a startup as an organization dedicated to creating something new under conditions of extreme uncertainty. This is just as true for one person in a garage or a group of seasoned professionals in a Fortune 500 boardroom. What they have in common is a mission to penetrate that fog of uncertainty to discover a successful path to a sustainable business.The Lean Startup approach fosters companies that are both more capital efficient and that leverage human creativity more effectively. Inspired by lessons from lean manufacturing, it relies on "validated learning," rapid scientific experimentation, as well as a number of counter-intuitive practices that shorten product development cycles, measure actual progress without resorting to vanity metrics, and learn what customers really want. It enables a company to shift directions with agility, altering plans inch by inch, minute by minute.Rather than wasting time creating elaborate business plans, The Lean Startup offers entrepreneurs - in companies of all sizes - a way to test their vision continuously, to adapt and adjust before it's too late. Ries provides a scientific approach to creating and managing successful startups in a age when companies need to innovate more than ever.

    Predictive Analytics For Dummies explores the power of predictive analytics and how you can use it to make valuable predictions for your business, or in fields such as advertising, fraud detection, politics, and others. This practical book does not bog you down with loads of mathematical or scientific theory, but instead helps you quickly see how to use the right algorithms and tools to collect and analyze data and apply it to make predictions.Topics include using structured and unstructured data, building models, creating a predictive analysis roadmap, setting realistic goals, budgeting, and much more.Shows readers how to use Big Data and data mining to discover patterns and make predictions for tech-savvy businesses Helps readers see how to shepherd predictive analytics projects through their companies Explains just enough of the science and math, but also focuses on practical issues such as protecting project budgets, making good presentations, and more Covers nuts-and-bolts topics including predictive analytics basics, using structured and unstructured data, data mining, and algorithms and techniques for analyzing data Also covers clustering, association, and statistical models; creating a predictive analytics roadmap; and applying predictions to the web, marketing, finance, health care, and elsewhere Propose, produce, and protect predictive analytics projects through your company with Predictive Analytics For Dummies.

    But in our zeal to instill the evaluation process with scientific rigor, we've gone from measuring performance to fixating on measuring itself. The result is a tyranny of metrics that threatens the quality of our lives and most important institutions. In this timely and powerful book, Jerry Muller uncovers the damage our obsession with metrics is causing--and shows how we can begin to fix the problem.Filled with examples from education, medicine, business and finance, government, the police and military, and philanthropy and foreign aid, this brief and accessible book explains why the seemingly irresistible pressure to quantify performance distorts and distracts, whether by encouraging "gaming the stats" or "teaching to the test." That's because what can and does get measured is not always worth measuring, may not be what we really want to know, and may draw effort away from the things we care about. Along the way, we learn why paying for measured performance doesn't work, why surgical scorecards may increase deaths, and much more. But metrics can be good when used as a complement to--rather than a replacement for--judgment based on personal experience, and Muller also gives examples of when metrics have been beneficial.Complete with a checklist of when and how to use metrics, The Tyranny of Metrics is an essential corrective to a rarely questioned trend that increasingly affects us all.