Dangeti pratap (38 resultados)

Paperback. Condición: Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.

Condición: very_good. The book is clean and shows minor shelf ware.

Condición: As New. Unread book in perfect condition.

Condición: New.

Paperback or Softback. Condición: New. Statistics for Machine Learning. Book.

Condición: New.

Condición: New.

Condición: As New. Unread book in perfect condition.

Paperback. Condición: New. Understand, explore, and effectively present data using the powerful data visualization techniques of PythonKey FeaturesUse the power of Pandas and Matplotlib to easily solve data mining issuesUnderstand the basics of statistics to build powerful predictive data modelsGrasp data mining concepts with helpful use-cases and examplesBook DescriptionData mining, or parsing the data to extract useful insights, is a niche skill that can transform your career as a data scientist Python is a flexible programming language that is equipped with a strong suite of libraries and toolkits, and gives you the perfect platform to sift through your data and mine the insights you seek. This Learning Path is designed to familiarize you with the Python libraries and the underlying statistics that you need to get comfortable with data mining.You will learn how to use Pandas, Python's popular library to analyze different kinds of data, and leverage the power of Matplotlib to generate appealing and impressive visualizations for the insights you have derived. You will also explore different machine learning techniques and statistics that enable you to build powerful predictive models.By the end of this Learning Path, you will have the perfect foundation to take your data mining skills to the next level and set yourself on the path to become a sought-after data science professional.This Learning Path includes content from the following Packt products:Statistics for Machine Learning by Pratap DangetiMatplotlib 2.x By Example by Allen Yu, Claire Chung, Aldrin YimPandas Cookbook by Theodore PetrouWhat you will learnUnderstand the statistical fundamentals to build data modelsSplit data into independent groups Apply aggregations and transformations to each groupCreate impressive data visualizationsPrepare your data and design models Clean up data to ease data analysis and visualizationCreate insightful visualizations with Matplotlib and SeabornCustomize the model to suit your own predictive goalsWho this book is forIf you want to learn how to use the many libraries of Python to extract impactful information from your data and present it as engaging visuals, then this is the ideal Learning Path for you. Some basic knowledge of Python is enough to get started with this Learning Path.…

Condición: New.

Digital. Condición: New. Build Machine Learning models with a sound statistical understanding.About This Book. Learn about the statistics behind powerful predictive models with p-value, ANOVA, and F- statistics. Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering. Master the statistical aspect of Machine Learning with the help of this example-rich guide to R and Python.Who This Book Is ForThis book is intended for developers with little to no background in statistics, who want to implement Machine Learning in their systems. Some programming knowledge in R or Python will be useful.What You Will Learn. Understand the Statistical and Machine Learning fundamentals necessary to build models. Understand the major differences and parallels between the statistical way and the Machine Learning way to solve problems. Learn how to prepare data and feed models by using the appropriate Machine Learning algorithms from the more-than-adequate R and Python packages. Analyze the results and tune the model appropriately to your own predictive goals. Understand the concepts of required statistics for Machine Learning. Introduce yourself to necessary fundamentals required for building supervised and unsupervised deep learning models. Learn reinforcement learning and its application in the field of artificial intelligence domainIn DetailComplex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. This book will teach you all it takes to perform complex statistical computations required for Machine Learning. You will gain information on statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. Understand the real-world examples that discuss the statistical side of Machine Learning and familiarize yourself with it. You will also design programs for performing tasks such as model, parameter fitting, regression, classification, density collection, and more.By the end of the book, you will have mastered the required statistics for Machine Learning and will be able to apply your new skills to any sort of industry problem.Style and approachThis practical, step-by-step guide will give you an understanding of the Statistical and Machine Learning fundamentals you'll need to build models.…

Paperback. Condición: New. Understand, explore, and effectively present data using the powerful data visualization techniques of PythonKey FeaturesUse the power of Pandas and Matplotlib to easily solve data mining issuesUnderstand the basics of statistics to build powerful predictive data modelsGrasp data mining concepts with helpful use-cases and examplesBook DescriptionData mining, or parsing the data to extract useful insights, is a niche skill that can transform your career as a data scientist Python is a flexible programming language that is equipped with a strong suite of libraries and toolkits, and gives you the perfect platform to sift through your data and mine the insights you seek. This Learning Path is designed to familiarize you with the Python libraries and the underlying statistics that you need to get comfortable with data mining.You will learn how to use Pandas, Python's popular library to analyze different kinds of data, and leverage the power of Matplotlib to generate appealing and impressive visualizations for the insights you have derived. You will also explore different machine learning techniques and statistics that enable you to build powerful predictive models.By the end of this Learning Path, you will have the perfect foundation to take your data mining skills to the next level and set yourself on the path to become a sought-after data science professional.This Learning Path includes content from the following Packt products:Statistics for Machine Learning by Pratap DangetiMatplotlib 2.x By Example by Allen Yu, Claire Chung, Aldrin YimPandas Cookbook by Theodore PetrouWhat you will learnUnderstand the statistical fundamentals to build data modelsSplit data into independent groups Apply aggregations and transformations to each groupCreate impressive data visualizationsPrepare your data and design models Clean up data to ease data analysis and visualizationCreate insightful visualizations with Matplotlib and SeabornCustomize the model to suit your own predictive goalsWho this book is forIf you want to learn how to use the many libraries of Python to extract impactful information from your data and present it as engaging visuals, then this is the ideal Learning Path for you. Some basic knowledge of Python is enough to get started with this Learning Path.…

Digital. Condición: New. Build Machine Learning models with a sound statistical understanding.About This Book. Learn about the statistics behind powerful predictive models with p-value, ANOVA, and F- statistics. Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering. Master the statistical aspect of Machine Learning with the help of this example-rich guide to R and Python.Who This Book Is ForThis book is intended for developers with little to no background in statistics, who want to implement Machine Learning in their systems. Some programming knowledge in R or Python will be useful.What You Will Learn. Understand the Statistical and Machine Learning fundamentals necessary to build models. Understand the major differences and parallels between the statistical way and the Machine Learning way to solve problems. Learn how to prepare data and feed models by using the appropriate Machine Learning algorithms from the more-than-adequate R and Python packages. Analyze the results and tune the model appropriately to your own predictive goals. Understand the concepts of required statistics for Machine Learning. Introduce yourself to necessary fundamentals required for building supervised and unsupervised deep learning models. Learn reinforcement learning and its application in the field of artificial intelligence domainIn DetailComplex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. This book will teach you all it takes to perform complex statistical computations required for Machine Learning. You will gain information on statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. Understand the real-world examples that discuss the statistical side of Machine Learning and familiarize yourself with it. You will also design programs for performing tasks such as model, parameter fitting, regression, classification, density collection, and more.By the end of the book, you will have mastered the required statistics for Machine Learning and will be able to apply your new skills to any sort of industry problem.Style and approachThis practical, step-by-step guide will give you an understanding of the Statistical and Machine Learning fundamentals you'll need to build models.…

Condición: New.

Condición: New.

Condición: As New. Unread book in perfect condition.

Condición: New.

Condición: As New. Unread book in perfect condition.

Condición: New. pp. 682.

Paperback. Condición: New. Understand, explore, and effectively present data using the powerful data visualization techniques of PythonKey FeaturesUse the power of Pandas and Matplotlib to easily solve data mining issuesUnderstand the basics of statistics to build powerful predictive data modelsGrasp data mining concepts with helpful use-cases and examplesBook DescriptionData mining, or parsing the data to extract useful insights, is a niche skill that can transform your career as a data scientist Python is a flexible programming language that is equipped with a strong suite of libraries and toolkits, and gives you the perfect platform to sift through your data and mine the insights you seek. This Learning Path is designed to familiarize you with the Python libraries and the underlying statistics that you need to get comfortable with data mining.You will learn how to use Pandas, Python's popular library to analyze different kinds of data, and leverage the power of Matplotlib to generate appealing and impressive visualizations for the insights you have derived. You will also explore different machine learning techniques and statistics that enable you to build powerful predictive models.By the end of this Learning Path, you will have the perfect foundation to take your data mining skills to the next level and set yourself on the path to become a sought-after data science professional.This Learning Path includes content from the following Packt products:Statistics for Machine Learning by Pratap DangetiMatplotlib 2.x By Example by Allen Yu, Claire Chung, Aldrin YimPandas Cookbook by Theodore PetrouWhat you will learnUnderstand the statistical fundamentals to build data modelsSplit data into independent groups Apply aggregations and transformations to each groupCreate impressive data visualizationsPrepare your data and design models Clean up data to ease data analysis and visualizationCreate insightful visualizations with Matplotlib and SeabornCustomize the model to suit your own predictive goalsWho this book is forIf you want to learn how to use the many libraries of Python to extract impactful information from your data and present it as engaging visuals, then this is the ideal Learning Path for you. Some basic knowledge of Python is enough to get started with this Learning Path.…

Digital. Condición: New. Build Machine Learning models with a sound statistical understanding.About This Book. Learn about the statistics behind powerful predictive models with p-value, ANOVA, and F- statistics. Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering. Master the statistical aspect of Machine Learning with the help of this example-rich guide to R and Python.Who This Book Is ForThis book is intended for developers with little to no background in statistics, who want to implement Machine Learning in their systems. Some programming knowledge in R or Python will be useful.What You Will Learn. Understand the Statistical and Machine Learning fundamentals necessary to build models. Understand the major differences and parallels between the statistical way and the Machine Learning way to solve problems. Learn how to prepare data and feed models by using the appropriate Machine Learning algorithms from the more-than-adequate R and Python packages. Analyze the results and tune the model appropriately to your own predictive goals. Understand the concepts of required statistics for Machine Learning. Introduce yourself to necessary fundamentals required for building supervised and unsupervised deep learning models. Learn reinforcement learning and its application in the field of artificial intelligence domainIn DetailComplex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. This book will teach you all it takes to perform complex statistical computations required for Machine Learning. You will gain information on statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. Understand the real-world examples that discuss the statistical side of Machine Learning and familiarize yourself with it. You will also design programs for performing tasks such as model, parameter fitting, regression, classification, density collection, and more.By the end of the book, you will have mastered the required statistics for Machine Learning and will be able to apply your new skills to any sort of industry problem.Style and approachThis practical, step-by-step guide will give you an understanding of the Statistical and Machine Learning fundamentals you'll need to build models.…

Paperback. Condición: New. Understand, explore, and effectively present data using the powerful data visualization techniques of PythonKey FeaturesUse the power of Pandas and Matplotlib to easily solve data mining issuesUnderstand the basics of statistics to build powerful predictive data modelsGrasp data mining concepts with helpful use-cases and examplesBook DescriptionData mining, or parsing the data to extract useful insights, is a niche skill that can transform your career as a data scientist Python is a flexible programming language that is equipped with a strong suite of libraries and toolkits, and gives you the perfect platform to sift through your data and mine the insights you seek. This Learning Path is designed to familiarize you with the Python libraries and the underlying statistics that you need to get comfortable with data mining.You will learn how to use Pandas, Python's popular library to analyze different kinds of data, and leverage the power of Matplotlib to generate appealing and impressive visualizations for the insights you have derived. You will also explore different machine learning techniques and statistics that enable you to build powerful predictive models.By the end of this Learning Path, you will have the perfect foundation to take your data mining skills to the next level and set yourself on the path to become a sought-after data science professional.This Learning Path includes content from the following Packt products:Statistics for Machine Learning by Pratap DangetiMatplotlib 2.x By Example by Allen Yu, Claire Chung, Aldrin YimPandas Cookbook by Theodore PetrouWhat you will learnUnderstand the statistical fundamentals to build data modelsSplit data into independent groups Apply aggregations and transformations to each groupCreate impressive data visualizationsPrepare your data and design models Clean up data to ease data analysis and visualizationCreate insightful visualizations with Matplotlib and SeabornCustomize the model to suit your own predictive goalsWho this book is forIf you want to learn how to use the many libraries of Python to extract impactful information from your data and present it as engaging visuals, then this is the ideal Learning Path for you. Some basic knowledge of Python is enough to get started with this Learning Path.…

Digital. Condición: New. Build Machine Learning models with a sound statistical understanding.About This Book. Learn about the statistics behind powerful predictive models with p-value, ANOVA, and F- statistics. Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering. Master the statistical aspect of Machine Learning with the help of this example-rich guide to R and Python.Who This Book Is ForThis book is intended for developers with little to no background in statistics, who want to implement Machine Learning in their systems. Some programming knowledge in R or Python will be useful.What You Will Learn. Understand the Statistical and Machine Learning fundamentals necessary to build models. Understand the major differences and parallels between the statistical way and the Machine Learning way to solve problems. Learn how to prepare data and feed models by using the appropriate Machine Learning algorithms from the more-than-adequate R and Python packages. Analyze the results and tune the model appropriately to your own predictive goals. Understand the concepts of required statistics for Machine Learning. Introduce yourself to necessary fundamentals required for building supervised and unsupervised deep learning models. Learn reinforcement learning and its application in the field of artificial intelligence domainIn DetailComplex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. This book will teach you all it takes to perform complex statistical computations required for Machine Learning. You will gain information on statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. Understand the real-world examples that discuss the statistical side of Machine Learning and familiarize yourself with it. You will also design programs for performing tasks such as model, parameter fitting, regression, classification, density collection, and more.By the end of the book, you will have mastered the required statistics for Machine Learning and will be able to apply your new skills to any sort of industry problem.Style and approachThis practical, step-by-step guide will give you an understanding of the Statistical and Machine Learning fundamentals you'll need to build models.…

PAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PF. Condición: New. This item is printed on demand. Brand new book, sourced directly from publisher. Dispatch time is 6-7 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

PAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condición: New. Print on Demand.

Condición: New. PRINT ON DEMAND.