Python real world machine par joshi prateek (23 résultats)
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Etat : Sehr gut. Zustand: Sehr gut | Seiten: 956 | Sprache: Englisch | Produktart: Bücher | Explore the great features of the all-new JIRA 7 to manage projects and effectively handle bugs and software issues Key Features Updated for JIRA 7, this book covers all the new features introduced in JIRA 7 with a dedicated chapter on JI…RA Service Desk-one of the biggest new add-ons to JIRA This book lays a strong foundation to work with agile projects in JIRA from both the administrator and end user's perspective Learn to solve challenging data science problems by building powerful machine learning models using Python Book Description Machine learning is increasingly spreading in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. Machine learning is transforming the way we understand and interact with the world around us. In the first module, Python Machine Learning Cookbook, you will learn how to perform various machine learning tasks using a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms. The second module, Advanced Machine Learning with Python, is designed to take you on a guided tour of the most relevant and powerful machine learning techniques and you'll acquire a broad set of powerful skills in the area of feature selection and feature engineering. The third module in this learning path, Large Scale Machine Learning with Python, dives into scalable machine learning and the three forms of scalability. It covers the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python. This Learning Path will teach you Python machine learning for the real world. The machine learning techniques covered in this Learning Path are at the forefront of commercial practice. This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: Python Machine Learning Cookbook by Prateek Joshi Advanced Machine Learning with Python by John Hearty Large Scale Machine Learning with Python by Bastiaan Sjardin, Alberto Boschetti, Luca Massaron What you will learn Use predictive modeling and apply it to real-world problems Understand how to perform market segmentation using unsupervised learning Apply your new-found skills to solve real problems, through clearly-explained code for every technique and test Compete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms Increase predictive accuracy with deep learning and scalable data-handling techniques Work with modern state-of-the-art large-scale machine learning techniques Learn to use Python code to implement a range of machine learning algorithms and techniques Who this book is for This Learning Path is for Python programmers who are looking to use machine learning algorithms to create real-world applications. It is ideal for Python professionals who want to work with large and complex datasets and Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. Experience with Python, Jupyter Notebooks, and command-line execution together with a good level of mathematical knowledge to understand the concepts is expected. Machine learning basic knowledge is also expected.
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Taschenbuch. Etat : Neu. Python Machine Learning Cookbook | 100 recipes that teach you how to perform various machine learning tasks in the real world | Prateek Joshi | Taschenbuch | Kartoniert / Broschiert | Englisch | 2016 | Packt Publishing | EAN 9781786464477 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36…244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 100 recipes that teach you how to perform various machine learning tasks in the real worldKey FeaturesUnderstand which algorithms to use in a given context with the help of this exciting recipe-based guideLearn about perceptrons and see how…they are used to build neural networksStuck while making sense of images, text, speech, and real estate This guide will come to your rescue, showing you how to perform machine learning for each one of these using various techniquesBook DescriptionMachine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more.With this book, you will learn how to perform various machine learning tasks in different environments. We'll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you'll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms.You'll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples.What you will learnExplore classification algorithms and apply them to the income bracket estimation problemUse predictive modeling and apply it to real-world problemsUnderstand how to perform market segmentation using unsupervised learningExplore data visualization techniques to interact with your data in diverse waysFind out how to build a recommendation engineUnderstand how to interact with text data and build models to analyze itWork with speech data and recognize spoken words using Hidden Markov ModelsAnalyze stock market data using Conditional Random FieldsWork with image data and build systems for image recognition and biometric face recognitionGrasp how to use deep neural networks to build an optical character recognition systemWho this book is for:¿This book is for Python programmers who are looking to use machine-learning algorithms to create real-world applications. This book is friendly to Python beginners, but familiarity with Python programming would certainly be useful to play around with the code.
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Taschenbuch. Etat : Neu. Python Real World Machine Learning | Real World Machine Learning: Take your Python Machine learning skills to the next level | Prateek Joshi (u. a.) | Taschenbuch | Kartoniert / Broschiert | Englisch | 2017 | Packt Publishing | EAN 9781787123212 | Verantwortliche Person für die EU: Libri GmbH, Europaalle…e 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Explore the great features of the all-new JIRA 7 to manage projects and effectively handle bugs and software issuesKey FeaturesUpdated for JIRA 7, this book covers all the new features introduced in JIRA 7 with a dedicated chapter on JIRA Se…rvice Deskone of the biggest new add-ons to JIRAThis book lays a strong foundation to work with agile projects in JIRA from both the administrator and end user's perspectiveLearn to solve challenging data science problems by building powerful machine learning models using PythonBook DescriptionMachine learning is increasingly spreading in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. Machine learning is transforming the way we understand and interact with the world around us.In the first module, Python Machine Learning Cookbook, you will learn how to perform various machine learning tasks using a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms.The second module, Advanced Machine Learning with Python, is designed to take you on a guided tour of the most relevant and powerful machine learning techniques and you'll acquire a broad set of powerful skills in the area of feature selection and feature engineering.The third module in this learning path, Large Scale Machine Learning with Python, dives into scalable machine learning and the three forms of scalability. It covers the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python.This Learning Path will teach you Python machine learning for the real world. The machine learning techniques covered in this Learning Path are at the forefront of commercial practice.This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:Python Machine Learning Cookbook by Prateek JoshiAdvanced Machine Learning with Python by John HeartyLarge Scale Machine Learning with Python by Bastiaan Sjardin, Alberto Boschetti, Luca MassaronWhat you will learnUse predictive modeling and apply it to real-world problemsUnderstand how to perform market segmentation using unsupervised learningApply your new-found skills to solve real problems, through clearly-explained code for every technique and testCompete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithmsIncrease predictive accuracy with deep learning and scalable data-handling techniquesWork with modern state-of-the-art large-scale machine learning techniquesLearn to use Python code to implement a range of machine learning algorithms and techniquesWho this book is forThis Learning Path is for Python programmers who are looking to use machine learning algorithms to create real-world applications. It is ideal for Python professionals who want to work with large and complex datasets and Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. Experience with Python, Jupyter Not Elektronisches Buch, and command-line execution together with a good level of mathematical knowledge to understand the concepts is expected. Machine learning basic knowledge is also expected.
