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|Artificial Intelligence with Python: A Comprehensive Guide to Building Intelligent Apps for Python Beginners and Developers
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Author: Prateek Joshi
Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you
About This Book
- Step into the amazing world of intelligent apps using this comprehensive guide
- Enter the world of Artificial Intelligence, explore it, and create your own applications
- Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time
Who This Book Is For
This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks.
What You Will Learn
- Realize different classification and regression techniques
- Understand the concept of clustering and how to use it to automatically segment data
- See how to build an intelligent recommender system
- Understand logic programming and how to use it
- Build automatic speech recognition systems
- Understand the basics of heuristic search and genetic programming
- Develop games using Artificial Intelligence
- Learn how reinforcement learning works
- Discover how to build intelligent applications centered on images, text, and time series data
- See how to use deep learning algorithms and build applications based on it
Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications.
During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide!
Style and approach
This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application.
|Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning)
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Author: Richard S. Sutton
Brand: Bradford Book
Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications.
Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.
The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.
|Data Smart: Using Data Science to Transform Information into Insight
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Author: John W. Foreman
Brand: John Foreman
Data Science gets thrown around in the press like it's magic. Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions.
But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope.
Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet.
|Machine Learning with R - Second Edition: Expert techniques for predictive modeling to solve all your data analysis problems
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Author: Brett Lantz
Brand: Lantz Brett
- Harness the power of R for statistical computing and data science
- Explore, forecast, and classify data with R
- Use R to apply common machine learning algorithms to real-world scenarios
Machine learning, at its core, is concerned with transforming data into actionable knowledge. This makes machine learning well suited to the present-day era of big data. Given the growing prominence of Râa cross-platform, zero-cost statistical programming environmentâthere has never been a better time to start applying machine learning to your data. Whether you are new to data analytics or a veteran, machine learning with R offers a powerful set of methods to quickly and easily gain insights from your data.
Want to turn your data into actionable knowledge, predict outcomes that make real impact, and have constantly developing insights? R gives you access to the cutting-edge power you need to master exceptional machine learning techniques.
Updated and upgraded to the latest libraries and most modern thinking, the second edition of Machine Learning with R provides you with a rigorous introduction to this essential skill of professional data science. Without shying away from technical theory, it is written to provide focused and practical knowledge to get you building algorithms and crunching your data, with minimal previous experience.
With this book youâll discover all the analytical tools you need to gain insights from complex data and learn how to to choose the correct algorithm for your specific needs. Through full engagement with the sort of real-world problems data-wranglers face, youâll learn to apply machine learning methods to deal with common tasks, including classification, prediction, forecasting, market analysis, and clustering. Transform the way you think about data; discover machine learning with R.
What you will learn
- Harness the power of R to build common machine learning algorithms with real-world data science applications
- Get to grips with R techniques to clean and prepare your data for analysis, and visualize your results
- Discover the different types of machine learning models and learn which is best to meet your data needs and solve your analysis problems
- Classify your data with Bayesian and nearest neighbour methods
- Predict values by using R to build decision trees, rules, and support vector machines
- Forecast numeric values with linear regression, and model your data with neural networks
- Evaluate and improve the performance of machine learning models
- Learn specialized machine learning techniques for text mining, social network data, big data, and more
About the Author
Brett Lantz has used innovative data methods to understand human behavior for more than 10 years. A sociologist by training, he was first enchanted by machine learning while studying a large database of teenagers' social networking website profiles. Since then, he has worked on the interdisciplinary studies of cellular telephone calls, medical billing data, and philanthropic activity, among others.
Table of Contents
- Introducing Machine Learning
- Managing and Understanding Data
- Lazy Learning â Classification Using Nearest Neighbors
- Probabilistic Learning â Classification Using Naive Bayes
- Divide and Conquer â Classification Using Decision Trees and Rules
- Forecasting Numeric Data â Regression Methods
- Black Box Methods â Neural Networks and Support Vector Machines
- Finding Patterns â Market Basket Analysis Using Association Rules
- Finding Groups of Data â Clustering with K-means
- Evaluating Model Performance
- Improving Model Performance
- Machine Learning with R Second Edition
- R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
- An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
- Machine Learning With R Cookbook - 110 Recipes for Building Powerful Predictive Models with R
- Text Mining with R: A Tidy Approach
- Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (MIT Press)
- R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics (O'reilly Cookbooks)
- Applied Predictive Modeling
- Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
- Python Machine Learning, 1st Edition
- ggplot2: Elegant Graphics for Data Analysis (Use R!)
|I Am a Strange Loop
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Author: Douglas R. Hofstadter
Brand: Basic Books
Can thought arise out of matter? Can self, soul, consciousness, I” arise out of mere matter? If it cannot, then how can you or I be here?
I Am a Strange Loop argues that the key to understanding selves and consciousness is the strange loop”a special kind of abstract feedback loop inhabiting our brains. The most central and complex symbol in your brain is the one called I.” The I” is the nexus in our brain, one of many symbols seeming to have free will and to have gained the paradoxical ability to push particles around, rather than the reverse.
How can a mysterious abstraction be realor is our I” merely a convenient fiction? Does an I” exert genuine power over the particles in our brain, or is it helplessly pushed around by the laws of physics?
These are the mysteries tackled in I Am a Strange Loop, Douglas Hofstadter's first book-length journey into philosophy since Gödel, Escher, Bach. Compulsively readable and endlessly thought-provoking, this is a moving and profound inquiry into the nature of mind.
Amazon Best Books of the Month, March 2007: Pulitzer-Prize winner Douglas Hofstadter takes on some weighty and wonderful questions in I Am a Strange Loop--among them, the "size" of a soul and the vagaries of thought--and proposes persuasive answers that surprised me both with their simplicity and their sense of optimism: a rare combination to be found in a book that tackles the mysteries of the brain. This long-awaited book is a must-have for avid science readers and navel-gazers. --Anne Bartholomew
|Machine Learning: The New AI (The MIT Press Essential Knowledge series)
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Author: Ethem Alpaydin
Brand: Ethem Alpaydin
A concise overview of machine learning -- computer programs that learn from data -- which underlies applications that include recommendation systems, face recognition, and driverless cars.
Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition -- as well as some we don't yet use everyday, including driverless cars. It is the basis of the new approach in computing where we do not write programs but collect data; the idea is to learn the algorithms for the tasks automatically from data. As computing devices grow more ubiquitous, a larger part of our lives and work is recorded digitally, and as "Big Data" has gotten bigger, the theory of machine learning -- the foundation of efforts to process that data into knowledge -- has also advanced. In this book, machine learning expert Ethem Alpaydin offers a concise overview of the subject for the general reader, describing its evolution, explaining important learning algorithms, and presenting example applications.
Alpaydin offers an account of how digital technology advanced from number-crunching mainframes to mobile devices, putting today's machine learning boom in context. He describes the basics of machine learning and some applications; the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances, with such applications as customer segmentation and learning recommendations; and reinforcement learning, when an autonomous agent learns act so as to maximize reward and minimize penalty. Alpaydin then considers some future directions for machine learning and the new field of "data science," and discusses the ethical and legal implications for data privacy and security.
- Machine Learning The New AI The MIT Press Essential Knowledge series
|Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit
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Author: Steven Bird
Brand: Brand: O'Reilly Media
This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. 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 intelligence
This 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.
- Used Book in Good Condition
- Web Scraping with Python: Collecting Data from the Modern Web
- Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More
- Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
- Foundations of Statistical Natural Language Processing
- Text Analytics with Python: A Practical Real-World Approach to Gaining Actionable Insights from your Data
- Introduction to Machine Learning with Python: A Guide for Data Scientists
- Speech and Language Processing, 2nd Edition
- Taming Text: How to Find, Organize, and Manipulate It
- Text Mining with R: A Tidy Approach
- Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms
|Amazon Echo: Master Your Amazon Echo; User Guide and Manual
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Author: Andrew Mckinnon
Brand: McKinnon Andrew
AMAZON ECHO 2017 USER GUIDE! UPDATED FULLY FOR 2017! Amazon just never stops getting better, with one of their most popular products, it just takes the entire Amazon experience into a whole new level. The Amazon Echo is a device that follows a voice command, providing you answers about news, music, weather and more. You can also use it to play your favorite music, audiobooks, and even use it as an alarm clock! The possibilities are endless! This book will provide you with everything you need to know about the Amazon Echo. You’ll get to know its design and setup, and learn how to navigate the device itself including its App. Amazon Echo has a name, and you can call her Alexa. She will make your life more convenient on a day-to-day basis. You’ll be surprised with what’s in-store for you! Here’s a Preview of the Book: “The Amazon Echo is highly efficient at what it does, and this is possible only because of the resourceful and well-placed parts embedded in the device. This gadget is created to respond fast to your queries and provide you with accurate and quick results.” “Did you know that Alexa can tell you a joke? Or if you would like, she can read to you information from Wikipedia or convert the dollar into the pound. She can even tell you how to spell a word, give you the background information of an actor or spew general trivia on anything you would like.” Alexa can be your very own virtual friend! She can lend you a helping hand when you need it most, and if you welcome her into your home, you’ll surely be great partners! So what are you waiting for?
- Amazon Echo Master Your Amazon Echo User Guide and Manual
- Alexa: 1000 best Things To Ask Alexa: Helpful and amusing questions you can’t do without. (user guides, internet,alexa,echo,dot,smart devices) (Volume 1)
- Amazon Echo User Guide: Newbie to Expert in 1 Hour!
- Amazon Alexa: Ultimate User Guide 2017 for Amazon Echo, Echo Dot & Amazon Tap +500 Secret Easter Eggs included.
- Alexa: 933 Funniest Things to Ask Alexa: (Echo Dot, Amazon Echo Dot, Amazon Echo, Amazon Dot, Alexa) (Funny Stuffs & Videos Added Every Week in the Facebook Page, Links Added Inside)
- Amazon Echo Dot: Echo Dot User Manual: From Newbie to Expert in One Hour: Echo Dot 2nd Generation User Guide: (Amazon Echo, Amazon Dot, Echo Dot, ... Manual, Alexa, User Manual, Echo Dot ebook)
- Alexa: The Ultimate guide to using your personal assistant to the fullest (Amazon Echo Look, Amazon Echo Show, Amazon Echo Dot, Amazon Echo)
- Aobelieve Acrylic Speaker Stand for Alexa Amazon Echo Plus and 1st Generation Echo, with Remote Holder, Black
- #1 Acrylic Amazon Alexa Echo Plus Speaker Stand (Black) – Enhanced Strength and Stability to Protect Alexa Boom Speaker – Mount Stabilizes Sound – Sleek Smart Home Décor
- Amazon Echo: Amazon Echo 2nd Generation User Guide 2017 Updated: Step-By-Step Instructions To Enrich Your Smart Life (alexa, dot, echo amazon, echo ... dot, echo dot user manual, echo) (Volume 3)
- Portable Battery Base for Echo (Use Echo Anywhere) NOT COMPATIBLE WITH THE NEW ECHO PLUS
|Introduction to the Theory of Computation
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Author: Michael Sipser
Brand: Brand: Cengage Learning
Gain a clear understanding of even the most complex, highly theoretical computational theory topics in the approachable presentation found only in the market-leading INTRODUCTION TO THE THEORY OF COMPUTATION, 3E. The number one choice for today's computational theory course, this revision continues the book's well-know, approachable style with timely revisions, additional practice, and more memorable examples in key areas. A new first-of-its-kind theoretical treatment of deterministic context-free languages is ideal for a better understanding of parsing and LR(k) grammars. You gain a solid understanding of the fundamental mathematical properties of computer hardware, software, and applications with a blend of practical and philosophical coverage and mathematical treatments, including advanced theorems and proofs. INTRODUCTION TO THE THEORY OF COMPUTATION, 3E's comprehensive coverage makes this a valuable reference for your continued studies in theoretical computing.
- Used Book in Good Condition
|Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)
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Author: Daphne Koller
Brand: Koller Daphne Friedman Nir
A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions.
Most tasks require a person or an automated system to reason -- to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality.
Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.
- Probabilistic Graphical Models Principles and Techniques
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