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.
Artificial Intelligence with Python: A Comprehensive Guide to Building Intelligent Apps for Python Beginners and Developers #ad - 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.
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.
Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd EditionPackt 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.
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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.
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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.
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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.
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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.
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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.
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