Now that machine learning is thriving, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn how to use a range of techniques, starting with simple Linear Regression and progressing to Deep Neural Networks. If you have some programming experience and you’re ready to code a machine learning project, this guide is for you.This hands-on book shows you how to use:Scikit-Learn, an accessible framework that implements many algorithms efficiently and serves as a great machine learning entry pointTensorFlow, a more complex library for distributed numerical computation, ideal for training and running very large neural networksPractical code examples that you can apply without learning excessive machine learning theory or algorithm details

    They are typically used to categorize something based on other data that you have. The purpose of this book is to help you understand how Random Forests work, as well as the different options that you have when using them to analyze a problem. Additionally, since Decision Trees are a fundamental part of Random Forests, this book explains how they work. This book is focused on understanding Random Forests at the conceptual level. Knowing how they work, why they work the way that they do, and what options are available to improve results. This book covers how Random Forests work in an intuitive way, and also explains the equations behind many of the functions, but it only has a small amount of actual code (in python). This book is focused on giving examples and providing analogies for the most fundamental aspects of how random forests and decision trees work. The reason is that those are easy to understand and they stick with you. There are also some really interesting aspects of random forests, such as information gain, feature importances, or out of bag error, that simply cannot be well covered without diving into the equations of how they work. For those the focus is providing the information in a straight forward and easy to understand way.

    It is suited for undergraduate degree students of computer science engineering, IT as well as computer applications. This book will enable students to apply the Python programming concepts in solving real-world problems.The book begins with an introduction to computers, problem solving approaches, programming languages, object oriented programming, and Python programming. Separate chapters dealing with the important constructs of Python language such as control statements, functions, strings, files, data structures, classes and objects, inheritance, operator overloading, and exceptions are provided in the book.

    This guide also illustrates how to handle input and output, make programs perform repetitive tasks, manipulate data, hide information, use functions and build flexible, easily modifiable programs.

    With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication.Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify "named entities" Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligenceThis book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful.

    You may still be drawing a paycheck, but the job you were hired to do no longer exists. Your company has changed, the technology has changed, the economy has changed, and the ways you can add value have changed. Have you adapted to these changes? Or are you at risk? Architect your career Economic downturn. Job cuts. Outsourcing. The ever-changing tech landscape. The threats abound. Chad Fowler is here to offer 52 ways to keep your job, despite the vagaries of the market.It's all about making the right choices. Choosing which technologies to focus on and which business domains to master have at least as much impact on your success as your technical knowledge--don't let those choices be accidental. Chad shows you all aspects of the decision-making process so you can ensure that you're investing your time and energy in the right areas.It's all about skills. You'll develop a structured plan for keeping your skills up-to-date so that you can compete with both the growing stable of developers in so-called low-cost countries as well as your higher-priced local peers. You'll learn how to shift your skillset up the value chain, from an offshore-ready commodity to one in high demand.It's all about marketing. As with any product or service, if nobody knows what you're selling, nobody will buy. Chad shows you how to create a plan for marketing yourself both inside your company and to the industry in general.Like it or not, the IT career landscape has changed. This handbook will teach you what you need to do to avoid being left behind. About the author Chad Fowler has been a software developer and manager for some of the world's largest corporations. He recently lived and worked in India, setting up and leading an offshore software development center for a large multinational company.

    The authors, who developed the metaobject protocol andwho were among the group that developed CLOS, introduce this new approach toprogramming language design, describe its evolution and design principles, andpresent a formal specification of a metaobject protocol for CLOS.Kiczales, desRivi?res, and Bobrow show that the "art of metaobject protocol design" lies increating a synthetic combination of object-oriented and reflective techniques thatcan be applied under existing software engineering considerations to yield a newapproach to programming language design that meets a broad set of designcriteria.One of the major benefits of including the metaobject protocol inprogramming languages is that it allows users to adjust the language to better suittheir needs. Metaobject protocols also disprove the adage that adding moreflexibility to a programming language reduces its performance. In presenting theprinciples of metaobject protocols, the authors work with actual code for asimplified implementation of CLOS and its metaobject protocol, providing anopportunity for the reader to gain hands-on experience with the design process. Theyalso include a number of exercises that address important concerns and openissues.Gregor Kiczales and Jim des Rivi?res, are Members of the Research Staff, andDaniel Bobrow is a Research Fellow, in the System Sciences Laboratory at Xerox PaloAlto Research Center.

    After a year of self-study, I learned to program well enough to land a job as a software engineer II at eBay. Once I got there, I realized I was severely under-prepared. I was overwhelmed by the amount of things I needed to know but hadn't learned yet. My journey learning to program, and my experience at my first job as a software engineer were the inspiration for this book. This book is not just about learning to program; although you will learn to code. If you want to program professionally, it is not enough to learn to code; that is why, in addition to helping you learn to program, I also cover the rest of the things you need to know to program professionally that classes and books don't teach you. "The Self-taught Programmer" is a roadmap, a guide to take you from writing your first Python program, to passing your first technical interview. I divided the book into five sections: 1. Start to program in Python 3 and build your first program.2. Learn Object-oriented programming and create a powerful Python program to get you hooked.3. Learn to use tools like Git, Bash, and regular expressions. Then use your new coding skills to build a web scraper.4. Study Computer Science fundamentals like data structures and algorithms.5. Finish with best coding practices, tips for working with a team, and advice on landing a programming job.You CAN learn to program professionally. The path is there. Will you take it?

    In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

    This book will help those familiar the command line and basic shell scripting start using Ansible to provision and manage anywhere from one to thousands of servers.The book begins with fundamentals, like installing Ansible, setting up a basic inventory file, and basic concepts, then guides you through Ansible's many uses, including ad-hoc commands, basic and advanced playbooks, application deployments, custom modules, and special cases like running ansible in 'pull' mode when you have thousands of servers to manage (or more). Everything is explained with pertinent real-world examples, often using Vagrant-managed virtual machines.

     Data Structures and Algorithms in Python is the first mainstream object-oriented book available for the Python data structures course. Designed to provide a comprehensive introduction to data structures and algorithms, including their design, analysis, and implementation, the text will maintain the same general structure as Data Structures and Algorithms in Java and Data Structures and Algorithms in C++.

    Demonstrates the programming language's strength as a Web development tool, covering syntax, data types, built-ins, the Python standard module library, and real world examples

    With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it.Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains:Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in a dataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details."-- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths."-- Tim Wolters, CTO, Collective Intellect

    It brings together all the knowledge needed to write any program, use any standard or third-party Python 3 library, and create new library modules of your own.

    Developed by computer science instructors, books in the For the absolute beginner series teach the principles of programming through simple game creation. You will acquire the skills that you need for more practical Python programming applications and you will learn how these skills can be put to use in real-world scenarios. Best of all, by the time you finish this book you will be able to apply the basic principles you've learned to the next programming language you tackle.Features Fun approach to a difficult topic Readers will create games with Python as they learn the fundamentals of this programming language The CD will include games that readers can cut and paste into their own Web site The author provides challenges at the end of chapters to push readers to program on their own.