Building Machine Learning Systems With Python Third Edition

Building Machine Learning Systems with Python   Third Edition PDF
Author: Luis Coelho
Publisher:
ISBN:
Size: 80.79 MB
Format: PDF, ePub, Docs
Category : Python (Computer program language)
Languages : en
Pages : 406
View: 1357
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Building Machine Learning Systems With Python Third Edition

by Luis Coelho, Building Machine Learning Systems With Python Third Edition Books available in PDF, EPUB, Mobi Format. Download Building Machine Learning Systems With Python Third Edition books, Get more from your data by creating practical machine learning systems with Python Key Features Develop your own Python-based machine learning system Discover how Python offers multiple algorithms for modern machine learning systems Explore key Python machine learning libraries to implement in your projects Book Description Machine learning allows systems to learn things without being explicitly programmed to do so. Python is one of the most popular languages used to develop machine learning applications, which take advantage of its extensive library support. This third edition of Building Machine Learning Systems with Python addresses recent developments in the field by covering the most-used datasets and libraries to help you build practical machine learning systems. Using machine learning to gain deeper insights from data is a key skill required by modern application developers and analysts alike. Python, being a dynamic language, allows for fast exploration and experimentation. This book shows you exactly how to find patterns in your raw data. You will start by brushing up on your Python machine learning knowledge and being introduced to libraries. You'll quickly get to grips with serious, real-world projects on datasets, using modeling and creating recommendation systems. With Building Machine Learning Systems with Python, you'll gain the tools and understanding required to build your own systems, all tailored to solve real-world data analysis problems. By the end of this book, you will be able to build machine learning systems using techniques and methodologies such as classification, sentiment analysis, computer vision, reinforcement learning, and neural networks. What you will learn Build a classification system that can be applied to text, images, and sound Employ Amazon Web Services (AWS) to run analysis on the cloud Solve problems related to regression using scikit-learn and TensorFlow Recommend products to users based on their past purchases Understand different ways to apply deep neural networks on structured data Address recent developments in the field of computer vision and reinforcement learning Who this book is for Building Machine Learning Systems with Python is for data scientists, machine learning developers, and Python developers who want to learn how to build increasingly complex machine learning systems. You will use Python's machine learning capabilities to develop effective solutions. Prior knowledge of Python progr ...




Building Machine Learning Systems With Python

Building Machine Learning Systems with Python PDF
Author: Luis Pedro Coelho
Publisher: Packt Publishing Ltd
ISBN: 1788622227
Size: 78.69 MB
Format: PDF, ePub
Category : Computers
Languages : en
Pages : 406
View: 6749
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Building Machine Learning Systems With Python

by Luis Pedro Coelho, Building Machine Learning Systems With Python Books available in PDF, EPUB, Mobi Format. Download Building Machine Learning Systems With Python books, Get more from your data by creating practical machine learning systems with Python Key Features Develop your own Python-based machine learning system Discover how Python offers multiple algorithms for modern machine learning systems Explore key Python machine learning libraries to implement in your projects Book Description Machine learning allows systems to learn things without being explicitly programmed to do so. Python is one of the most popular languages used to develop machine learning applications, which take advantage of its extensive library support. This third edition of Building Machine Learning Systems with Python addresses recent developments in the field by covering the most-used datasets and libraries to help you build practical machine learning systems. Using machine learning to gain deeper insights from data is a key skill required by modern application developers and analysts alike. Python, being a dynamic language, allows for fast exploration and experimentation. This book shows you exactly how to find patterns in your raw data. You will start by brushing up on your Python machine learning knowledge and being introduced to libraries. You'll quickly get to grips with serious, real-world projects on datasets, using modeling and creating recommendation systems. With Building Machine Learning Systems with Python, you’ll gain the tools and understanding required to build your own systems, all tailored to solve real-world data analysis problems. By the end of this book, you will be able to build machine learning systems using techniques and methodologies such as classification, sentiment analysis, computer vision, reinforcement learning, and neural networks. What you will learn Build a classification system that can be applied to text, images, and sound Employ Amazon Web Services (AWS) to run analysis on the cloud Solve problems related to regression using scikit-learn and TensorFlow Recommend products to users based on their past purchases Understand different ways to apply deep neural networks on structured data Address recent developments in the field of computer vision and reinforcement learning Who this book is for Building Machine Learning Systems with Python is for data scientists, machine learning developers, and Python developers who want to learn how to build increasingly complex machine learning systems. You will use Python's machine learning capabilities to develop effective solutions. Prior knowledge of Python programming is expected.




Python Machine Learning

Python Machine Learning PDF
Author: Sebastian Raschka
Publisher: Packt Publishing Ltd
ISBN: 1789958296
Size: 38.41 MB
Format: PDF
Category : Computers
Languages : en
Pages : 770
View: 3518
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Python Machine Learning

by Sebastian Raschka, Python Machine Learning Books available in PDF, EPUB, Mobi Format. Download Python Machine Learning books, Applied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning. Key Features Third edition of the bestselling, widely acclaimed Python machine learning book Clear and intuitive explanations take you deep into the theory and practice of Python machine learning Fully updated and expanded to cover TensorFlow 2, Generative Adversarial Network models, reinforcement learning, and best practices Book Description Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself. Updated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It's also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents. This book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments. What you will learn Master the frameworks, models, and techniques that enable machines to 'learn' from data Use scikit-learn for machine learning and TensorFlow for deep learning Apply machine learning to image classification, sentiment analysis, intelligent web applications, and more Build and train neural networks, GANs, and other models Discover best practices for evaluating and tuning models Predict continuous target outcomes using regression analysis Dig deeper into textual and social media data using sentiment analysis Who This Book Is For If you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for anyone who wants to teach computers how to learn from data.




Python Machine Learning By Example Third Edition

Python Machine Learning by Example   Third Edition PDF
Author: Yuxi (Hayden) Liu
Publisher:
ISBN: 9781800209718
Size: 29.39 MB
Format: PDF, Mobi
Category : Computers
Languages : en
Pages : 526
View: 5032
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Python Machine Learning By Example Third Edition

by Yuxi (Hayden) Liu, Python Machine Learning By Example Third Edition Books available in PDF, EPUB, Mobi Format. Download Python Machine Learning By Example Third Edition books, Python Machine Learning By Example, Third Edition serves as a comprehensive gateway into the world of machine learning (ML). With six new chapters, on topics including movie recommendation engine development with Naïve Bayes, recognizing faces with support vector machine, predicting stock prices with artificial neural networks, categorizing images of clothing with convolutional neural networks, predicting with sequences using recurring neural networks, and leveraging reinforcement learning for making decisions, the book has been considerably updated for the latest enterprise requirements. At the same time, this book provides actionable insights on the key fundamentals of ML with Python programming. Hayden applies his expertise to demonstrate implementations of algorithms in Python, both from scratch and with libraries. Each chapter walks through an industry-adopted application. With the help of realistic examples, you will gain an understanding of the mechanics of ML techniques in areas such as exploratory data analysis, feature engineering, classification, regression, clustering, and NLP. By the end of this ML Python book, you will have gained a broad picture of the ML ecosystem and will be well-versed in the best practices of applying ML techniques to solve problems.




Python Machine Learning Third Edition

Python Machine Learning   Third Edition PDF
Author: Sebastian Raschka
Publisher:
ISBN:
Size: 66.78 MB
Format: PDF, ePub, Docs
Category :
Languages : en
Pages : 770
View: 2569
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Python Machine Learning Third Edition

by Sebastian Raschka, Python Machine Learning Third Edition Books available in PDF, EPUB, Mobi Format. Download Python Machine Learning Third Edition books, Applied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning. Key Features Third edition of the bestselling, widely acclaimed Python machine learning book Clear and intuitive explanations take you deep into the theory and practice of Python machine learning Fully updated and expanded to cover TensorFlow 2, Generative Adversarial Network models, reinforcement learning, and best practices Book Description Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself. Updated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It's also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents. This book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments. What you will learn Master the frameworks, models, and techniques that enable machines to 'learn' from data Use scikit-learn for machine learning and TensorFlow for deep learning Apply machine learning to image classification, sentiment analysis, intelligent web applications, and more Build and train neural networks, GANs, and other models Discover best practices for evaluating and tuning models Predict continuous target outcomes using regression analysis Dig deeper into textual and social media data using sentiment analysis Who This Book Is For If you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential ...




Hands On Exploratory Data Analysis With Python

Hands On Exploratory Data Analysis with Python PDF
Author: Suresh Kumar Mukhiya
Publisher: Packt Publishing Ltd
ISBN: 178953562X
Size: 65.39 MB
Format: PDF, Mobi
Category : Computers
Languages : en
Pages : 352
View: 6758
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Hands On Exploratory Data Analysis With Python

by Suresh Kumar Mukhiya, Hands On Exploratory Data Analysis With Python Books available in PDF, EPUB, Mobi Format. Download Hands On Exploratory Data Analysis With Python books, Discover techniques to summarize the characteristics of your data using PyPlot, NumPy, SciPy, and pandas Key Features Understand the fundamental concepts of exploratory data analysis using Python Find missing values in your data and identify the correlation between different variables Practice graphical exploratory analysis techniques using Matplotlib and the Seaborn Python package Book Description Exploratory Data Analysis (EDA) is an approach to data analysis that involves the application of diverse techniques to gain insights into a dataset. This book will help you gain practical knowledge of the main pillars of EDA - data cleaning, data preparation, data exploration, and data visualization. You’ll start by performing EDA using open source datasets and perform simple to advanced analyses to turn data into meaningful insights. You’ll then learn various descriptive statistical techniques to describe the basic characteristics of data and progress to performing EDA on time-series data. As you advance, you’ll learn how to implement EDA techniques for model development and evaluation and build predictive models to visualize results. Using Python for data analysis, you’ll work with real-world datasets, understand data, summarize its characteristics, and visualize it for business intelligence. By the end of this EDA book, you’ll have developed the skills required to carry out a preliminary investigation on any dataset, yield insights into data, present your results with visual aids, and build a model that correctly predicts future outcomes. What you will learn Import, clean, and explore data to perform preliminary analysis using powerful Python packages Identify and transform erroneous data using different data wrangling techniques Explore the use of multiple regression to describe non-linear relationships Discover hypothesis testing and explore techniques of time-series analysis Understand and interpret results obtained from graphical analysis Build, train, and optimize predictive models to estimate results Perform complex EDA techniques on open source datasets Who this book is for This EDA book is for anyone interested in data analysis, especially students, statisticians, data analysts, and data scientists. The practical concepts presented in this book can be applied in various disciplines to enhance decision-making processes with data analysis and synthesis. Fundamental knowledge of Python programming and statistical concepts is all you need to get started with this book.




Hands On Unsupervised Learning With Python

Hands On Unsupervised Learning with Python PDF
Author: Giuseppe Bonaccorso
Publisher: Packt Publishing Ltd
ISBN: 1789349273
Size: 50.30 MB
Format: PDF, ePub, Mobi
Category : Computers
Languages : en
Pages : 386
View: 1106
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Hands On Unsupervised Learning With Python

by Giuseppe Bonaccorso, Hands On Unsupervised Learning With Python Books available in PDF, EPUB, Mobi Format. Download Hands On Unsupervised Learning With Python books, Discover the skill-sets required to implement various approaches to Machine Learning with Python Key Features Explore unsupervised learning with clustering, autoencoders, restricted Boltzmann machines, and more Build your own neural network models using modern Python libraries Practical examples show you how to implement different machine learning and deep learning techniques Book Description Unsupervised learning is about making use of raw, untagged data and applying learning algorithms to it to help a machine predict its outcome. With this book, you will explore the concept of unsupervised learning to cluster large sets of data and analyze them repeatedly until the desired outcome is found using Python. This book starts with the key differences between supervised, unsupervised, and semi-supervised learning. You will be introduced to the best-used libraries and frameworks from the Python ecosystem and address unsupervised learning in both the machine learning and deep learning domains. You will explore various algorithms, techniques that are used to implement unsupervised learning in real-world use cases. You will learn a variety of unsupervised learning approaches, including randomized optimization, clustering, feature selection and transformation, and information theory. You will get hands-on experience with how neural networks can be employed in unsupervised scenarios. You will also explore the steps involved in building and training a GAN in order to process images. By the end of this book, you will have learned the art of unsupervised learning for different real-world challenges. What you will learn Use cluster algorithms to identify and optimize natural groups of data Explore advanced non-linear and hierarchical clustering in action Soft label assignments for fuzzy c-means and Gaussian mixture models Detect anomalies through density estimation Perform principal component analysis using neural network models Create unsupervised models using GANs Who this book is for This book is intended for statisticians, data scientists, machine learning developers, and deep learning practitioners who want to build smart applications by implementing key building block unsupervised learning, and master all the new techniques and algorithms offered in machine learning and deep learning using real-world examples. Some prior knowledge of machine learning concepts and statistics is desirable.




Hands On Markov Models With Python

Hands On Markov Models with Python PDF
Author: Ankur Ankan
Publisher: Packt Publishing Ltd
ISBN: 1788629337
Size: 54.80 MB
Format: PDF, Docs
Category : Computers
Languages : en
Pages : 178
View: 2774
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Hands On Markov Models With Python

by Ankur Ankan, Hands On Markov Models With Python Books available in PDF, EPUB, Mobi Format. Download Hands On Markov Models With Python books, Unleash the power of unsupervised machine learning in Hidden Markov Models using TensorFlow, pgmpy, and hmmlearn Key Features Build a variety of Hidden Markov Models (HMM) Create and apply models to any sequence of data to analyze, predict, and extract valuable insights Use natural language processing (NLP) techniques and 2D-HMM model for image segmentation Book Description Hidden Markov Model (HMM) is a statistical model based on the Markov chain concept. Hands-On Markov Models with Python helps you get to grips with HMMs and different inference algorithms by working on real-world problems. The hands-on examples explored in the book help you simplify the process flow in machine learning by using Markov model concepts, thereby making it accessible to everyone. Once you’ve covered the basic concepts of Markov chains, you’ll get insights into Markov processes, models, and types with the help of practical examples. After grasping these fundamentals, you’ll move on to learning about the different algorithms used in inferences and applying them in state and parameter inference. In addition to this, you’ll explore the Bayesian approach of inference and learn how to apply it in HMMs. In further chapters, you’ll discover how to use HMMs in time series analysis and natural language processing (NLP) using Python. You’ll also learn to apply HMM to image processing using 2D-HMM to segment images. Finally, you’ll understand how to apply HMM for reinforcement learning (RL) with the help of Q-Learning, and use this technique for single-stock and multi-stock algorithmic trading. By the end of this book, you will have grasped how to build your own Markov and hidden Markov models on complex datasets in order to apply them to projects. What you will learn Explore a balance of both theoretical and practical aspects of HMM Implement HMMs using different datasets in Python using different packages Understand multiple inference algorithms and how to select the right algorithm to resolve your problems Develop a Bayesian approach to inference in HMMs Implement HMMs in finance, natural language processing (NLP), and image processing Determine the most likely sequence of hidden states in an HMM using the Viterbi algorithm Who this book is for Hands-On Markov Models with Python is for you if you are a data analyst, data scientist, or machine learning developer and want to enhance your machine learning knowledge and skills. This book will also help you build your own hidden Markov models by applying them to any sequence of data. Basic knowledge of machine learning and the Python programming language is expected to get the most out of the book




Bayesian Analysis With Python

Bayesian Analysis with Python PDF
Author: Osvaldo Martin
Publisher: Packt Publishing Ltd
ISBN: 1789349664
Size: 12.10 MB
Format: PDF
Category : Computers
Languages : en
Pages : 356
View: 371
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Bayesian Analysis With Python

by Osvaldo Martin, Bayesian Analysis With Python Books available in PDF, EPUB, Mobi Format. Download Bayesian Analysis With Python books, Bayesian modeling with PyMC3 and exploratory analysis of Bayesian models with ArviZ Key Features A step-by-step guide to conduct Bayesian data analyses using PyMC3 and ArviZ A modern, practical and computational approach to Bayesian statistical modeling A tutorial for Bayesian analysis and best practices with the help of sample problems and practice exercises. Book Description The second edition of Bayesian Analysis with Python is an introduction to the main concepts of applied Bayesian inference and its practical implementation in Python using PyMC3, a state-of-the-art probabilistic programming library, and ArviZ, a new library for exploratory analysis of Bayesian models. The main concepts of Bayesian statistics are covered using a practical and computational approach. Synthetic and real data sets are used to introduce several types of models, such as generalized linear models for regression and classification, mixture models, hierarchical models, and Gaussian processes, among others. By the end of the book, you will have a working knowledge of probabilistic modeling and you will be able to design and implement Bayesian models for your own data science problems. After reading the book you will be better prepared to delve into more advanced material or specialized statistical modeling if you need to. What you will learn Build probabilistic models using the Python library PyMC3 Analyze probabilistic models with the help of ArviZ Acquire the skills required to sanity check models and modify them if necessary Understand the advantages and caveats of hierarchical models Find out how different models can be used to answer different data analysis questions Compare models and choose between alternative ones Discover how different models are unified from a probabilistic perspective Think probabilistically and benefit from the flexibility of the Bayesian framework Who this book is for If you are a student, data scientist, researcher, or a developer looking to get started with Bayesian data analysis and probabilistic programming, this book is for you. The book is introductory so no previous statistical knowledge is required, although some experience in using Python and NumPy is expected.




                                          PDF
Author: حامد الشمرتي
Publisher: Al Manhal
ISBN:
Size: 74.49 MB
Format: PDF, ePub, Mobi
Category : Technology & Engineering
Languages : ar
Pages : 520
View: 3038
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by حامد الشمرتي, Books available in PDF, EPUB, Mobi Format. Download books, إن ما دفعنا لكتابة هذا المؤلف هو إحساسنا المتزايد بأن طالبنا العزيز يحتاج إلى هذه النوعية من التأليف في هذا المجال من خلال خبرتنا وممارستنا العميقة لتدريس هذا الموضوع لمدة طويلة في الجامعات العراقية (بغداد،المستنصرية،كلية المنصور،كلية الرافدين) فلقد كانت مبعث أحساس دقيق لما يحتاجه الطالب وخاصة في الدراسة الإحصائية والإدارية والاقتصادية لهذا الموضوع . لقد سبقنا في الكتابة في هذا المجال كثيرين وخاصة في موضوع بحوث العمليات ولكن مؤلفنا الجديد يختلف كلياً فهو عبارة عن محاضرات عبر هذه السنين الطويلة في كل عام يحذف منها ما يشوب تفكير الطلبة ويوسع ما يضيف إلى مداركهم معلومات هم بحاجة إليها للاستخدام في مثل هذه الموضوعات هذا من جهة ومن جهة أخرى فأن النمو الصناعي والاقتصادي والتكنولوجي اخذ يتزايد بسرعة يصعب اللحاق به ما لم تتحقق نظرة علمية مصحوبة بمنهجية صحيحة حديثة في اتخاذ القرارات الكمية المركبة التي تحقق وتعجل في تحقيق الأهداف المتوخاة في البناء الصناعي. ولما كانت الأجيال من الطلبة ترغب وتنشد النهوض والتعجيل في تحقيق النمو فأن ذلك يتطلب اطلاعها وزيادة معرفتها بالأساليب والطرق العلمية الحديثة المساعدة على تحقيق ذلك , وهذا هو الدافع الحقيقي وراء تأليف هذا الكتاب على اعتبار أن النمو الناجح في تحقيق الأهداف المتوخاة منه تعتمد كثيراً على الإدارة المنهجية الكمية الصحيحة التي تعد بدورها ولادة منهجية حديثة ملائمة بكيفية مواجهة واتخاذ القرارات المرتبطة بتحقيق الأهداف الاقتصادية والصناعية والإدارية . لقد رأينا أن نكتب في هذا المجال لنغطي ناحيتين ، الناحية النظرية والتي تتدرج في العمق لتلائم مستويات مختلفة من الطلبة ، وفي نفس الوقت الناحية التطبيقية والتي تتعدد مجالاتها بحيث تغطي اهتمامات مجموعة كبيرة من المتخصصين. في الناحية النظرية يتدرج الكتاب في عرض الموضوعات من المبادئ الرئيسية حتى يصل بالطالب ( القارئ) إلى احدث الاتجاهات في الدوريات العالمية بحيث يتلائم مع القارئ العادي ورجل الصناعة والأعمال والباحث في هذا المجال . ومن ناحية التطبيق يتعرض لنواحي عديدة من التطبيقات الهندسية والصناعية والإدارية والاقتصادية