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Showing posts with label Machine Learning Ebook. Show all posts
Showing posts with label Machine Learning Ebook. Show all posts

23 January, 2021

Download free Python for Unix and Linux System Administration in PDF

Download free Python for Unix and Linux System Administration in PDF
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Python is very easy language for solving problem. It is ideal language especially in Linux and Unix networks. In this Notes each chapter presents a particular administrative issue such as concurrency or data backup, and presents Python solution through hands-on example.
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Python Machine Learning - by PACKT

Python Machine Learning - by PACKT

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👉
 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 start from scratch or want to extend your data science knowledge, this is an essential and unmissable resource.

👉 In this book, you will find a number of text styles that distinguish between different kinds of information.

👉 The execution of the code examples provided in this book requires an installation of Python 3.4.3 or newer on Mac OS X, Linux, or Microsoft Windows. We will make frequent use of Python's essential libraries for scientific computing throughout this book, including SciPy, NumPy, scikit-learn, matplotlib, and pandas.

👉 If you want to become a machine learning practitioner, a better problem solver, or maybe even consider a career in machine learning research, then this book is for you!

👉 If you have already studied machine learning theory in detail, this book will show you how to put your knowledge into practice. If you have used machine learning techniques before and want to gain more insight into how machine learning really works, this book is for you!

👉 Don't worry if you are completely new to the machine learning field; you have even more reason to be excited. I promise you that machine learning will change the way you think about the problems you want to solve and will show you how to tackle them by unlocking the power of data.
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05 January, 2021

Machine learning for audio, image and video analysis theory and applications

Machine learning for audio, image and video analysis theory and applications by Camastra, Francesco Vinciarelli, Alessandro
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Product Description

This second edition focuses on audio, image and video data, the three main types of input that machines deal with when interacting with the real world. A set of appendices provides the reader with self-contained introductions to the mathematical background necessary to read the book.
Divided into three main parts, From Perception to Computation introduces methodologies aimed at representing the data in forms suitable for computer processing, especially when it comes to audio and images. Whilst the second part, Machine Learning includes an extensive overview of statistical techniques aimed at addressing three main problems, namely classification (automatically assigning a data sample to one of the classes belonging to a predefined set), clustering (automatically grouping data samples according to the similarity of their properties) and sequence analysis (automatically mapping a sequence of observations into a sequence of human-understandable symbols). The third part Applications shows how the abstract problems defined in the second part underlie technologies capable to perform complex tasks such as the recognition of hand gestures or the transcription of handwritten data.

Machine Learning for Audio, Image and Video Analysis is suitable for students to acquire a solid background in machine learning as well as for practitioners to deepen their knowledge of the state-of-the-art. All application chapters are based on publicly available data and free software packages, thus allowing readers to replicate the experiments.

Review

“This nice book of over 560 pages is really useful for students, researchers, practitioners, and anybody who is interested in machine learning and related subjects.” (Michael M. Dediu, Mathematical Reviews, May, 2017)

From the Back Cover

This second edition focuses on audio, image and video data, the three main types of input that machines deal with when interacting with the real world. A set of appendices provides the reader with self-contained introductions to the mathematical background necessary to read the book.

Divided into three main parts, From Perception to Computation introduces methodologies aimed at representing the data in forms suitable for computer processing, especially when it comes to audio and images. Whilst the second part, Machine Learning includes an extensive overview of statistical techniques aimed at addressing three main problems, namely classification (automatically assigning a data sample to one of the classes belonging to a predefined set), clustering (automatically grouping data samples according to the similarity of their properties) and sequence analysis (automatically mapping a sequence of observations into a sequence of human-understandable symbols). The third part Applications shows how the abstract problems defined in the second part underlie technologies capable to perform complex tasks such as the recognition of hand gestures or the transcription of handwritten data.

Machine Learning for Audio, Image and Video Analysis is suitable for students to acquire a solid background in machine learning as well as for practitioners to deepen their knowledge of the state-of-the-art. All application chapters are based on publicly available data and free software packages, thus allowing readers to replicate the experiments.

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28 December, 2020

Machine Learning for Time Series Forecasting with Python

Machine Learning for Time Series Forecasting with Python

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Author(s): Francesca Lazzeri

Publisher: Wiley, Year: 2020

Description:
Learn how to apply the principles of machine learning to time series modeling with this indispensable resource

Machine Learning for Time Series Forecasting with Python is an incisive and straightforward examination of one of the most crucial elements of decision-making in finance, marketing, education, and healthcare: time series modeling.

Despite the centrality of time series forecasting, few business analysts are familiar with the power or utility of applying machine learning to time series modeling. Author Francesca Lazzeri, a distinguished machine learning scientist and economist, corrects that deficiency by providing readers with comprehensive and approachable explanation and treatment of the application of machine learning to time series forecasting.

Written for readers who have little to no experience in time series forecasting or machine learning, the book comprehensively covers all the topics necessary to:

Understand time series forecasting concepts, such as stationarity, horizon, trend, and seasonality
Prepare time series data for modeling
Evaluate time series forecasting models’ performance and accuracy
Understand when to use neural networks instead of traditional time series models in time series forecasting

Machine Learning for Time Series Forecasting with Python is full real-world examples, resources and concrete strategies to help readers explore and transform data and develop usable, practical time series forecasts.

Perfect for entry-level data scientists, business analysts, developers, and researchers, this book is an invaluable and indispensable guide to the fundamental and advanced concepts of machine learning applied to time series modeling.
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23 December, 2020

Statistics and Machine Learning Methods for EHR Data: From Data Extraction to Data Analytics PDF Free

Statistics and Machine Learning Methods for EHR Data: From Data Extraction to Data Analytics PDF Free

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Author(s): Hulin Wu, Jose Miguel Yamal, Ashraf Yaseen, Vahed Maroufy

Series: Chapman & Hall/CRC Healthcare Informatics Series

Publisher: CRC Press, Year: 2020

Description:

The use of Electronic Health Records (EHR)/Electronic Medical Records (EMR) data is becoming more prevalent for research. However, analysis of this type of data has many unique complications due to how they are collected, processed and types of questions that can be answered. This book covers many important topics related to using EHR/EMR data for research including data extraction, cleaning, processing, analysis, inference, and predictions based on many years of practical experience of the authors. The book carefully evaluates and compares the standard statistical models and approaches with those of machine learning and deep learning methods and reports the unbiased comparison results for these methods in predicting clinical outcomes based on the EHR data.

Key Features:

  • Written based on hands-on experience of contributors from multidisciplinary EHR research projects, which include methods and approaches from statistics, computing, informatics, data science and clinical/epidemiological domains.
  • Documents the detailed experience on EHR data extraction, cleaning and preparation
  • Provides a broad view of statistical approaches and machine learning prediction models to deal with the challenges and limitations of EHR data.
  • Considers the complete cycle of EHR data analysis.

The use of EHR/EMR analysis requires close collaborations between statisticians, informaticians, data scientists and clinical/epidemiological investigators. This book reflects that multidisciplinary perspective.

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22 December, 2020

Python Machine Learning: The Complete Guide to Understand Python Machine Learning for Beginners and Artificial Intelligence

29 August, 2020

The Hundred-Page Machine Learning Book

The Hundred-Page Machine Learning Book
Andriy Burkov
Is this book for you?

You will enjoy the book if you are:
- a software engineer or a scientist who wants to become a machine learning engineer or a data scientist
- a data scientist trying to stay on the edge of the state-of-the-art and deepen their ML expertise
- a manager who wants to feel confident while talking about AI with engineers and product people
- a curious person looking to find out how machine learning works and maybe build something new
Categories:
Computers\\Cybernetics: Artificial Intelligence
Year:
2019
Publisher:
Andriy Burkov
Language:
english
Pages:
152
ISBN 13:
978-1999579500
File:
PDF, 6.98 MB
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26 August, 2020

Advances in Financial Machine Learning

Advances in Financial Machine Learning
Marcos Lopez de Prado
Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their particular setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.
Year:
2020
Edition:
1
Publisher:
Wiley
Language:
english
Pages:
400 / 393
ISBN 10:
1119482089
ISBN 13:
9781119482086
File:
PDF, 8.48 MB
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23 August, 2020

Learn TensorFlow 2.0: Implement Machine Learning And Deep Learning Models With Python

Learn TensorFlow 2.0: Implement Machine Learning And Deep Learning Models With Python
Pramod Singh, Avinash Manure
Learn how to use TensorFlow 2.0 to build machine learning and deep learning models with complete examples.
The book begins with introducing TensorFlow 2.0 framework and the major changes from its last release. Next, it focuses on building Supervised Machine Learning models using TensorFlow 2.0. It also demonstrates how to build models using customer estimators. Further, it explains how to use TensorFlow 2.0 API to build machine learning and deep learning models for image classification using the standard as well as custom parameters.
You'll review sequence predictions, saving, serving, deploying, and standardized datasets, and then deploy these models to production. All the code presented in the book will be available in the form of executable scripts at Github which allows you to try out the examples and extend them in interesting ways. What You'll Learn:
• Review the new features of TensorFlow 2.0
• Use TensorFlow 2.0 to build machine learning and deep learning models
• Perform sequence predictions using TensorFlow 2.0
• Deploy TensorFlow 2.0 models with practical examples
Who This Book Is For: Data scientists, machine and deep learning engineers.
Categories:
Computers\\Programming
Year:
2020
Publisher:
Apress
Language:
english
Pages:
177
ISBN 10:
1484255577
ISBN 13:
9781484255582
File:
PDF, 6.22 MB
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