Mastering Predictive Analytics With Scikit Learn And Tensorflow
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Mastering Predictive Analytics With Scikit Learn And Tensorflow
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Author : Alvaro Fuentes
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-09-29
Mastering Predictive Analytics With Scikit Learn And Tensorflow written by Alvaro Fuentes and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-29 with Computers categories.
Learn advanced techniques to improve the performance and quality of your predictive models Key FeaturesUse ensemble methods to improve the performance of predictive analytics modelsImplement feature selection, dimensionality reduction, and cross-validation techniquesDevelop neural network models and master the basics of deep learningBook Description Python is a programming language that provides a wide range of features that can be used in the field of data science. Mastering Predictive Analytics with scikit-learn and TensorFlow covers various implementations of ensemble methods, how they are used with real-world datasets, and how they improve prediction accuracy in classification and regression problems. This book starts with ensemble methods and their features. You will see that scikit-learn provides tools for choosing hyperparameters for models. As you make your way through the book, you will cover the nitty-gritty of predictive analytics and explore its features and characteristics. You will also be introduced to artificial neural networks and TensorFlow, and how it is used to create neural networks. In the final chapter, you will explore factors such as computational power, along with improvement methods and software enhancements for efficient predictive analytics. By the end of this book, you will be well-versed in using deep neural networks to solve common problems in big data analysis. What you will learnUse ensemble algorithms to obtain accurate predictionsApply dimensionality reduction techniques to combine features and build better modelsChoose the optimal hyperparameters using cross-validationImplement different techniques to solve current challenges in the predictive analytics domainUnderstand various elements of deep neural network (DNN) modelsImplement neural networks to solve both classification and regression problemsWho this book is for Mastering Predictive Analytics with scikit-learn and TensorFlow is for data analysts, software engineers, and machine learning developers who are interested in implementing advanced predictive analytics using Python. Business intelligence experts will also find this book indispensable as it will teach them how to progress from basic predictive models to building advanced models and producing more accurate predictions. Prior knowledge of Python and familiarity with predictive analytics concepts are assumed.
Hands On Machine Learning With Scikit Learn Keras And Tensorflow
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Author : Aurélien Géron
language : en
Publisher: O'Reilly Media
Release Date : 2019-09-05
Hands On Machine Learning With Scikit Learn Keras And Tensorflow written by Aurélien Géron and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-05 with Computers categories.
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. 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. 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, particularly neural nets Use Scikit-Learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets
Mastering Azure For Predictive Analytics And Machine Learning
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Author : KRISHNA KISHOR TIRUPATI SATISH VADLAMANI SHALU JAIN A RENUKA
language : en
Publisher: DeepMisti Publication
Release Date : 2024-10-09
Mastering Azure For Predictive Analytics And Machine Learning written by KRISHNA KISHOR TIRUPATI SATISH VADLAMANI SHALU JAIN A RENUKA and has been published by DeepMisti Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-09 with Computers categories.
In Today's Data-Driven World, The Ability To Harness The Power Of Predictive Analytics And Machine Learning Has Become A Pivotal Force In Shaping Innovation Across Industries. This Book, Mastering Azure For Predictive Analytics And Machine Learning, Aims To Bridge The Gap Between Cloud Technology And The Analytical Tools Needed To Drive Insights From Complex Data. Our Objective Is To Provide Readers With The Foundational Knowledge And Advanced Techniques Necessary To Leverage Microsoft Azure For Predictive Modeling And Machine Learning Applications. The Structure Of This Book Offers A Comprehensive Exploration Of The Tools, Methodologies, And Best Practices That Define Modern Analytics And Machine Learning In The Cloud. From Setting Up Your Azure Environment To Deploying Machine Learning Models, We Cover Each Stage With Practical Examples And Detailed Guidance. The Content Is Designed For A Broad Audience, Including Students, Data Scientists, It Professionals, And Business Leaders Who Seek To Use Azure’s Capabilities To Make Data-Informed Decisions. Drawing From The Latest Industry Research And Real-World Use Cases, This Book Not Only Provides Theoretical Knowledge But Also Equips Readers With Hands-On Skills They Can Apply In Real-Time Data Projects. Each Chapter Balances Depth With Accessibility, Covering Topics Like Data Preparation, Model Building, And Cloud-Based Deployment, While Also Touching On Critical Issues Such As Scalability, Security, And Automation. Additionally, We Highlight Best Practices For Managing Azure’s Infrastructure And Optimizing Machine Learning Workflows Within The Platform. The Inspiration For This Book Comes From The Recognition Of The Growing Role That Cloud Platforms Like Azure Play In Transforming How Organizations Use Data To Innovate And Compete. We Are Immensely Thankful To Chancellor Shri Shiv Kumar Gupta Of Maharaja Agrasen Himalayan Garhwal University For His Support And Commitment To Academic And Technological Excellence, Which Has Been Instrumental In Making This Book A Reality. We Hope That Mastering Azure For Predictive Analytics And Machine Learning Will Be A Valuable Resource For Anyone Looking To Deepen Their Understanding Of How Cloud Computing And Machine Learning Can Converge To Unlock The Full Potential Of Predictive Analytics. The Knowledge Contained In These Pages Is Intended To Empower Readers To Lead Transformative Data Projects With Confidence. Thank You For Embarking On This Journey With Us. Authors
Python Data Science Handbook
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Author : Jake VanderPlas
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2016-11-21
Python Data Science Handbook written by Jake VanderPlas and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-21 with Computers categories.
For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms
Handbook Of Hydroinformatics
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Author : Saeid Eslamian
language : en
Publisher: Elsevier
Release Date : 2022-11-30
Handbook Of Hydroinformatics written by Saeid Eslamian and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-30 with Technology & Engineering categories.
Classic Soft-Computing Techniques is the first volume of the three, in the Handbook of HydroInformatics series. Through this comprehensive, 34-chapters work, the contributors explore the difference between traditional computing, also known as hard computing, and soft computing, which is based on the importance given to issues like precision, certainty and rigor. The chapters go on to define fundamentally classic soft-computing techniques such as Artificial Neural Network, Fuzzy Logic, Genetic Algorithm, Supporting Vector Machine, Ant-Colony Based Simulation, Bat Algorithm, Decision Tree Algorithm, Firefly Algorithm, Fish Habitat Analysis, Game Theory, Hybrid Cuckoo–Harmony Search Algorithm, Honey-Bee Mating Optimization, Imperialist Competitive Algorithm, Relevance Vector Machine, etc. It is a fully comprehensive handbook providing all the information needed around classic soft-computing techniques. This volume is a true interdisciplinary work, and the audience includes postgraduates and early career researchers interested in Computer Science, Mathematical Science, Applied Science, Earth and Geoscience, Geography, Civil Engineering, Engineering, Water Science, Atmospheric Science, Social Science, Environment Science, Natural Resources, and Chemical Engineering. - Key insights from global contributors in the fields of data management research, climate change and resilience, insufficient data problem, etc. - Offers applied examples and case studies in each chapter, providing the reader with real world scenarios for comparison. - Introduces classic soft-computing techniques, necessary for a range of disciplines.
Python Machine Learning
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Author : Sebastian Raschka
language : en
Publisher: Packt Publishing Ltd
Release Date : 2015-09-23
Python Machine Learning written by Sebastian Raschka and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-09-23 with Computers categories.
Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is For If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will Learn Explore how to use different machine learning models to ask different questions of your data Learn how to build neural networks using Keras and Theano Find out how to write clean and elegant Python code that will optimize the strength of your algorithms Discover how to embed your machine learning model in a web application for increased accessibility Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Organize data using effective pre-processing techniques Get to grips with sentiment analysis to delve deeper into textual and social media data In Detail Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. Style and approach Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.
Hands On Genetic Algorithms With Python
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Author : Eyal Wirsansky
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-07-12
Hands On Genetic Algorithms With Python written by Eyal Wirsansky and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-12 with Computers categories.
Explore the ever-growing world of genetic algorithms to build and enhance AI applications involving search, optimization, machine learning, deep learning, NLP, and XAI using Python libraries Key Features Learn how to implement genetic algorithms using Python libraries DEAP, scikit-learn, and NumPy Take advantage of cloud computing technology to increase the performance of your solutions Discover bio-inspired algorithms such as particle swarm optimization (PSO) and NEAT Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionWritten by Eyal Wirsansky, a senior data scientist and AI researcher with over 25 years of experience and a research background in genetic algorithms and neural networks, Hands-On Genetic Algorithms with Python offers expert insights and practical knowledge to master genetic algorithms. After an introduction to genetic algorithms and their principles of operation, you’ll find out how they differ from traditional algorithms and the types of problems they can solve, followed by applying them to search and optimization tasks such as planning, scheduling, gaming, and analytics. As you progress, you’ll delve into explainable AI and apply genetic algorithms to AI to improve machine learning and deep learning models, as well as tackle reinforcement learning and NLP tasks. This updated second edition further expands on applying genetic algorithms to NLP and XAI and speeding up genetic algorithms with concurrency and cloud computing. You’ll also get to grips with the NEAT algorithm. The book concludes with an image reconstruction project and other related technologies for future applications. By the end of this book, you’ll have gained hands-on experience in applying genetic algorithms across a variety of fields, with emphasis on artificial intelligence with Python.What you will learn Use genetic algorithms to solve planning, scheduling, gaming, and analytics problems Create reinforcement learning, NLP, and explainable AI applications Enhance the performance of ML models and optimize deep learning architecture Deploy genetic algorithms using client-server architectures, enhancing scalability and computational efficiency Explore how images can be reconstructed using a set of semi-transparent shapes Delve into topics like elitism, niching, and multiplicity in genetic solutions to enhance optimization strategies and solution diversity Who this book is for If you’re a data scientist, software developer, AI enthusiast who wants to break into the world of genetic algorithms and apply them to real-world, intelligent applications as quickly as possible, this book is for you. Working knowledge of the Python programming language is required to get started with this book.
Mastering Data Science
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Author : Cybellium
language : en
Publisher: Cybellium Ltd
Release Date :
Mastering Data Science written by Cybellium and has been published by Cybellium Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
Unleash the Power of Insights from Data Are you ready to embark on a transformative journey into the world of data science? "Mastering Data Science" is your comprehensive guide to unlocking the full potential of data for extracting valuable insights and driving informed decisions. Whether you're an aspiring data scientist looking to enhance your skills or a business leader seeking to leverage data-driven strategies, this book equips you with the knowledge and tools to master the art of data science. Key Features: 1. Dive into Data Science: Immerse yourself in the realm of data science, understanding its core principles, methodologies, and applications. Build a solid foundation that empowers you to extract meaningful insights from complex datasets. 2. Data Exploration and Visualization: Master the art of data exploration and visualization. Learn how to analyze datasets, uncover patterns, and create compelling visualizations that reveal hidden trends. 3. Statistical Analysis and Hypothesis Testing: Uncover the power of statistical analysis and hypothesis testing. Explore techniques for making data-driven inferences, validating assumptions, and drawing meaningful conclusions. 4. Machine Learning Fundamentals: Delve into machine learning concepts and techniques. Learn about supervised and unsupervised learning, feature engineering, model selection, and evaluation. 5. Predictive Analytics: Discover the realm of predictive analytics. Learn how to build predictive models that forecast future outcomes, enabling proactive decision-making. 6. Natural Language Processing (NLP) and Text Mining: Explore NLP and text mining techniques. Learn how to process and analyze textual data, extract sentiments, and uncover insights from unstructured content. 7. Time Series Analysis: Master time series analysis for modeling sequential data. Learn how to forecast trends, identify seasonality, and make predictions based on temporal patterns. 8. Big Data and Data Wrangling: Dive into big data analytics and data wrangling. Learn how to handle and preprocess large datasets, ensuring data quality and usability. 9. Deep Learning and Neural Networks: Uncover the world of deep learning and neural networks. Learn how to build and train deep learning models for tasks like image recognition and natural language understanding. 10. Real-World Applications: Gain insights into real-world applications of data science across industries. From healthcare to finance, explore how organizations harness data science for strategic decision-making. Who This Book Is For: "Mastering Data Science" is an indispensable resource for aspiring data scientists, analysts, and business professionals who want to excel in extracting insights from data. Whether you're new to data science or seeking advanced techniques, this book will guide you through the intricacies and empower you to harness the full potential of data for innovation. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com
Deep Learning And Ai Superhero
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Author : Cuantum Technologies LLC
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-01-20
Deep Learning And Ai Superhero written by Cuantum Technologies LLC and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-20 with Computers categories.
Master TensorFlow, Keras, and PyTorch for deep learning in AI applications. Learn neural networks, CNNs, RNNs, LSTMs, and GANs through hands-on exercises and real-world projects. Key Features TensorFlow, Keras, and PyTorch for diverse deep learning frameworks Neural network concepts with real-world industry relevance Cloud and edge AI deployment techniques for scalable solutions Book DescriptionDive into the world of deep learning with this comprehensive guide that bridges theory and practice. From foundational neural networks to advanced architectures like CNNs, RNNs, and Transformers, this book equips you with the tools to build, train, and optimize AI models using TensorFlow, Keras, and PyTorch. Clear explanations of key concepts such as gradient descent, loss functions, and backpropagation are combined with hands-on exercises to ensure practical understanding. Explore cutting-edge AI frameworks, including generative adversarial networks (GANs) and autoencoders, while mastering real-world applications like image classification, text generation, and natural language processing. Detailed chapters cover transfer learning, fine-tuning pretrained models, and deployment strategies for cloud and edge computing. Practical exercises and projects further solidify your skills as you implement AI solutions for diverse challenges. Whether you're deploying AI models on cloud platforms like AWS or optimizing them for edge devices with TensorFlow Lite, this book provides step-by-step guidance. Designed for developers, AI enthusiasts, and data scientists, it balances theoretical depth with actionable insights, making it the ultimate resource for mastering modern deep learning frameworks and advancing your career in AIWhat you will learn Understand neural network basics Build models using TensorFlow and Keras Train and optimize PyTorch models Apply CNNs for image recognition Use RNNs and LSTMs for sequence tasks Leverage Transformers in NLP Who this book is for This book is for software developers, AI enthusiasts, data scientists, and ML engineers who aim to master deep learning frameworks. A foundational understanding of programming and basic ML concepts is recommended. Ideal for those seeking hands-on experience in real-world AI projects.
Mastering Clickhouse
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Author : Robert Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-01-03
Mastering Clickhouse written by Robert Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-03 with Computers categories.
"Mastering ClickHouse: High-Performance Data Analytics for Modern Applications" serves as an indispensable guide for data professionals seeking to leverage the power of ClickHouse, the acclaimed open-source columnar database management system designed for online analytical processing (OLAP). This comprehensive resource delves into all aspects of ClickHouse, from its foundational architecture to advanced integration techniques, enabling readers to understand and exploit its full potential for processing massive datasets with remarkable speed and efficiency. Structured to cater to both novices and seasoned experts, this book provides step-by-step guidance on setting up ClickHouse environments, configuring the system for optimal performance, and implementing robust security measures. Readers will learn about efficient data ingestion techniques and query optimization strategies to maximize analytics throughput. Real-world use cases illustrate the versatility of ClickHouse across different industries, highlighting its ability to enhance decision-making processes through advanced analytics, real-time data insights, and seamless integration within diverse technology stacks. Whether you aim to refine your current data infrastructure or embark on a new analytics journey, this book offers the essential insights and practical skills needed to innovate and excel with ClickHouse.