Artificial Intelligence with Python: A Comprehensive Guide to Building Intelligent Apps for Python Beginners and Developers

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Packt Publishing #ad - This practical book covers a range of topics including predictive analytics and deep learning. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. Discover how to make informed decisions about which algorithms to use, and how to apply them to real-world scenarios.

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 learnrealize different classification and regression techniquesunderstand the concept of clustering and how to use it to automatically segment dataSee how to build an intelligent recommender systemUnderstand logic programming and how to use itBuild automatic speech recognition systemsUnderstand the basics of heuristic search and genetic programmingDevelop games using Artificial IntelligenceLearn how reinforcement learning worksDiscover how to build intelligent applications centered on images, text, and time series dataSee how to use deep learning algorithms and build applications based on itIn DetailArtificial Intelligence is becoming increasingly relevant in the modern world.

Table of contents introduction to artificial intelligence classification and regression using supervised learning predictive Analytics with Ensemble Learning Detecting Patterns with Unsupervised Learning Building Recommender Systems Logic Programming Heuristic Search Techniques Genetic Algorithms Building Games with Artificial Intelligence Natural Language Processing Probabilistic Reasoning for Sequential Data Building A Speech Recognizer Object Detection and Tracking Artificial Neural Networks Reinforcement Learning Deep Learning with Convolutional Neural Networks.

Artificial Intelligence with Python: A Comprehensive Guide to Building Intelligent Apps for Python Beginners and Developers #ad - Build real-world artificial intelligence applications with python to intelligently interact with the world around youAbout This BookStep into the amazing world of intelligent apps using this comprehensive guideEnter the world of Artificial Intelligence, explore it, and create your own applicationsWork through simple yet insightful examples that will get you up and running with Artificial Intelligence in no timeWho This Book Is ForThis book is for Python developers who want to build real-world Artificial Intelligence applications.

By harnessing the power of algorithms, building intelligent recommender systems, you can create apps which intelligently interact with the world around you, automatic speech recognition systems and more. Starting with ai basics you'll move on to learn how to develop building blocks using data mining techniques.

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Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition

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Packt Publishing #ad - You'll be able to learn and work with tensorFlow more deeply than ever before, and get essential coverage of the Keras neural network library, along with the most recent updates to scikit-learn. What you will learnunderstand the key frameworks in data science, machine learning, and deep learningharness the power of the latest python open source libraries in machine learningexplore machine learning techniques using challenging real-world datamaster deep neural network implementation using the TensorFlow libraryLearn the mechanics of classification algorithms to implement the best tool for the jobPredict continuous target outcomes using regression analysisUncover hidden patterns and structures in data with clusteringDelve deeper into textual and social media data using sentiment analysisTable of ContentsGiving Computers the Ability to Learn from DataTraining Simple Machine Learning Algorithms for ClassificationA Tour of Machine Learning Classifiers Using Scikit-LearnBuilding Good Training Sets - Data PreprocessingCompressing Data via Dimensionality ReductionLearning Best Practices for Model Evaluation and Hyperparameter TuningCombining Different Models for Ensemble LearningApplying Machine Learning to Sentiment AnalysisEmbedding a Machine Learning Model into a Web ApplicationPredicting Continuous Target Variables with Regression AnalysisWorking with Unlabeled Data - Clustering AnalysisImplementing a Multilayer Artificial Neural Network from ScratchParallelizing Neural Network Training with TensorFlowGoing Deeper - The Mechanics of TensorFlowClassifying Images with Deep Convolutional Neural NetworksModeling Sequential Data using Recurrent Neural Networks.

The scikit-learn code has also been fully updated to include recent improvements and additions to this versatile machine learning library. Sebastian raschka and vahid mirjalili's unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples.

Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition #ad - Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka's bestselling book, Python Machine Learning. Key featuressecond edition of the bestselling book on machine learninga practical approach to key frameworks in data science, machine learning, and deep learningUse the most powerful Python libraries to implement machine learning and deep learningGet to know the best practices to improve and optimize your machine learning systems and algorithmsBook DescriptionMachine learning is eating the software world, and now deep learning is extending machine learning.

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Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

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O'Reilly Media #ad - Graphics in this book are printed in black and white. Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data.

With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, decision trees, particularly neural netsuse scikit-learn to track an example machine-learning project end-to-endExplore several training models, and ensemble methodsUse the TensorFlow library to build and train neural netsDive into neural net architectures, random forests, recurrent nets, including support vector machines, including convolutional nets, and deep reinforcement learningLearn techniques for training and scaling deep neural netsApply practical code examples without acquiring excessive machine learning theory or algorithm details O reilly Media.

Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems #ad - 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 a range of techniques, starting with simple linear regression and progressing to deep neural networks.

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Deep Learning with Python

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Manning Publications #ad - Written by keras creator and google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects. What's inside deep learning from first principlesSetting up your own deep-learning environment Image-classification modelsDeep learning for text and sequencesNeural style transfer, text generation, and image generation About the Reader Readers need intermediate Python skills.

Written by keras creator and google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. We went from near-unusable speech and image recognition, to near-human accuracy. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition CVPR, the Conference and Workshop on Neural Information Processing Systems NIPS, the International Conference on Learning Representations ICLR, and others.

Deep Learning with Python #ad - About the author françois Chollet works on deep learning at Google in Mountain View, CA. We went from machines that couldn't beat a serious Go player, to defeating a world champion. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework.

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Artificial Intelligence: A Modern Approach

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Pearson Education India #ad - Printed in asia - carries same contents as of US edition - Opt Expedited Shipping for 3 to 4 day delivery - O reilly Media.

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Artificial Intelligence: A Modern Approach 3rd Edition

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Pearson #ad - Artificial intelligence: a modern approach, 3e is available to purchase as an eText for your Kindle™, NOOK™, and the iPhone®/iPad®. Artificial intelligence: a modern Approach, 3e offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Jennifer Widom.

Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. To read the full New York Times article, click here. Dr. According to an article in the new york times, the course on artificial intelligence is “one of three being offered experimentally by the Stanford computer science department to extend technology knowledge and skills beyond this elite campus to the entire world.

Artificial Intelligence: A Modern Approach 3rd Edition #ad - One of the other two courses, an introduction to database software, is being taught by Pearson author Dr. O reilly Media. Peter norvig, contributing artificial Intelligence author and Professor Sebastian Thrun, a Pearson author are offering a free online course at Stanford University on artificial intelligence.

Overnight shipping available. To learn more about the course on artificial intelligence, visit http://www. Ai-class. Com.

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Artificial Intelligence and Machine Learning for Business: A No-Nonsense Guide to Data Driven Technologies

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Relativistic #ad - O reilly Media. Overnight shipping available. Consequently, organizations which understand these tools and know how to use them are benefiting at the expense of their rivals. Artificial intelligence and Machine Learning for Business cuts through the technical jargon that is often associated with these subjects.

Artificial Intelligence and Machine Learning for Business: A No-Nonsense Guide to Data Driven Technologies #ad - It delivers a simple and concise introduction for managers and business people. The focus is very much on practical application, and how to work with technical specialists data scientists to maximize the benefits of these technologies. Artificial intelligence AI and Machine Learning are now mainstream business tools.

They are being applied across many industries to increase profits, reduce costs, save lives and improve customer experiences.

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Make Your Own Neural Network

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CreateSpace Independent Publishing Platform #ad - Make your Own Neural Network. Overnight shipping available. The ambition of this guide is to make neural networks as accessible as possible to as many readers as possible - there are enough texts for advanced readers already!You'll learn to code in Python and make your own neural network, teaching it to recognise human handwritten numbers, and performing as well as professionally developed networks.

Part 3 extends these ideas further. A step-by-step gentle journey through the mathematics of neural networks, and making your own using the Python computer language. You won't need any mathematics beyond secondary school, and an accessible introduction to calculus is also included. We introduce the mathematical ideas underlying the neural networks, gently with lots of illustrations and examples.

Make Your Own Neural Network #ad - O reilly Media. Part 1 is about ideas. Neural networks are a key element of deep learning and artificial intelligence, which today is capable of some truly impressive feats. Part 2 is practical. This guide will take you on a fun and unhurried journey, starting from very simple ideas, and gradually building up an understanding of how neural networks work.

All the code in this has been tested to work on a Raspberry Pi Zero.

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Introduction to Machine Learning with Python: A Guide for Data Scientists

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O'Reilly Media #ad - Familiarity with the NumPy and matplotlib libraries will help you get even more from this book. With this book, 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, 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 text-specific processing techniquesSuggestions for improving your machine learning and data science skills O reilly Media.

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 müller and sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them.

Introduction to Machine Learning with Python: A Guide for Data Scientists #ad - If you use python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams.

Overnight shipping available. Make your Own Neural Network.

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Python Crash Course: A Hands-On, Project-Based Introduction to Programming

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No Starch Press #ad - In the second half of the book, you'll put your new knowledge into practice with three substantial projects: a Space Invaders-inspired arcade game, data visualizations with Python's super-handy libraries, and a simple web app you can deploy online. As you work through python crash course, numpy, and that grow more difficult as the game progresseswork with data to generate interactive visualizationsCreate and customize simple web apps and deploy them safely onlineDeal with mistakes and errors so you can solve your own programming problemsIf you've been thinking seriously about digging into programming, you'll learn how to:Use powerful Python libraries and tools, including matplotlib, and PygalMake 2D games that respond to keypresses and mouse clicks, Python Crash Course will get you up to speed and have you writing real programs fast.

Python Crash Course: A Hands-On, Project-Based Introduction to Programming #ad - No starch Press. Overnight shipping available. Why wait any longer? Start your engines and code! O reilly Media. Python crash course is a fast-paced, thorough introduction to programming with Python that will have you writing programs, solving problems, and making things that work in no time. In the first half of the book, such as lists, dictionaries, you'll learn about basic programming concepts, classes, and loops, and practice writing clean and readable code with exercises for each topic.

Make your Own Neural Network. You'll also learn how to make your programs interactive and how to test your code safely before adding it to a project.

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Artificial Intelligence By Example: Develop machine intelligence from scratch using real artificial intelligence use cases

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Packt Publishing #ad - Artificial intelligence by example serves as a starting point for you to understand how AI is built, with the help of intriguing examples and case studies. Artificial intelligence By Example will make you an adaptive thinker and help you apply concepts to real-life scenarios. Using some of the most interesting AI examples, right from a simple chess engine to a cognitive chatbot, you will learn how to tackle the machine you are competing with.

Be an adaptive thinker that leads the way to artificial intelligenceKey FeaturesAI-based examples to guide you in designing and implementing machine intelligenceDevelop your own method for future AI solutionsAcquire advanced AI, machine learning, and deep learning design skillsBook DescriptionArtificial intelligence has the potential to replicate humans in every field.

Artificial Intelligence By Example: Develop machine intelligence from scratch using real artificial intelligence use cases #ad - . This comprehensive guide will be a starter kit for you to develop AI applications on your own. By the end of this book, will have understood the fundamentals of AI and worked through a number of case studies that will help you develop your business vision. What you will learnuse adaptive thinking to solve real-life ai case studiesrise beyond being a modern-day factory code workeracquire advanced aI, explanatory, technology consultants, machine learning, quantum computing, and descriptive guide for junior developers, experienced developers, and IoT and blockchain technologyUnderstand future AI solutions and adapt quickly to themDevelop out-of-the-box thinking to face any challenge the market presentsWho This Book Is ForArtificial Intelligence by Example is a simple, and deep learning designing skillsLearn about cognitive NLP chatbots, and those interested in AI who want to understand the fundamentals of Artificial Intelligence and implement it practically by devising smart solutions.

You will study some of the most advanced machine learning models, understand how to apply AI to blockchain and IoT, and develop emotional quotient in chatbots using neural networks. You will move on to designing AI solutions in a simple manner rather than get confused by complex architectures and techniques.

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