Python Machine Learning Case Studies

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Python Machine Learning Case Studies

Author : Danish Haroon
Publisher : Apress
ISBN 13 : 1484228235
Total Pages : 216 pages
Book Rating : 4.4/5 (842 download)

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Book Synopsis Python Machine Learning Case Studies by : Danish Haroon

Download or read book Python Machine Learning Case Studies written by Danish Haroon and published by Apress. This book was released on 2017-10-27 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: Embrace machine learning approaches and Python to enable automatic rendering of rich insights and solve business problems. The book uses a hands-on case study-based approach to crack real-world applications to which machine learning concepts can be applied. These smarter machines will enable your business processes to achieve efficiencies on minimal time and resources. Python Machine Learning Case Studies takes you through the steps to improve business processes and determine the pivotal points that frame strategies. You’ll see machine learning techniques that you can use to support your products and services. Moreover you’ll learn the pros and cons of each of the machine learning concepts to help you decide which one best suits your needs. By taking a step-by-step approach to coding in Python you’ll be able to understand the rationale behind model selection and decisions within the machine learning process. The book is equipped with practical examples along with code snippets to ensure that you understand the data science approach to solving real-world problems. What You Will Learn Gain insights into machine learning concepts Work on real-world applications of machine learning Learn concepts of model selection and optimization Get a hands-on overview of Python from a machine learning point of view Who This Book Is For Data scientists, data analysts, artificial intelligence engineers, big data enthusiasts, computer scientists, computer sciences students, and capital market analysts.

Machine Learning Applications Using Python

Author : Puneet Mathur
Publisher : Apress
ISBN 13 : 1484237870
Total Pages : 384 pages
Book Rating : 4.4/5 (842 download)

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Book Synopsis Machine Learning Applications Using Python by : Puneet Mathur

Download or read book Machine Learning Applications Using Python written by Puneet Mathur and published by Apress. This book was released on 2018-12-12 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gain practical skills in machine learning for finance, healthcare, and retail. This book uses a hands-on approach by providing case studies from each of these domains: you’ll see examples that demonstrate how to use machine learning as a tool for business enhancement. As a domain expert, you will not only discover how machine learning is used in finance, healthcare, and retail, but also work through practical case studies where machine learning has been implemented. Machine Learning Applications Using Python is divided into three sections, one for each of the domains (healthcare, finance, and retail). Each section starts with an overview of machine learning and key technological advancements in that domain. You’ll then learn more by using case studies on how organizations are changing the game in their chosen markets. This book has practical case studies with Python code and domain-specific innovative ideas for monetizing machine learning. What You Will LearnDiscover applied machine learning processes and principles Implement machine learning in areas of healthcare, finance, and retail Avoid the pitfalls of implementing applied machine learning Build Python machine learning examples in the three subject areas Who This Book Is For Data scientists and machine learning professionals.

Solving Data Science Case Studies with Python

Author : Aman Kharwal
Publisher : Thecleverprogrammer
ISBN 13 :
Total Pages : 45 pages
Book Rating : 4./5 ( download)

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Book Synopsis Solving Data Science Case Studies with Python by : Aman Kharwal

Download or read book Solving Data Science Case Studies with Python written by Aman Kharwal and published by Thecleverprogrammer. This book was released on 2021-06-28 with total page 45 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is specially written for those who know the basics of the Python programming language as well as the necessary Python libraries you need for data science like NumPy, Pandas, Matplotlib, Seaborn, Plotly, and Scikit-learn. This book aims to teach you how to think while solving a business problem with your data science skills. To achieve the goal of this book, I started by giving you all the knowledge you need to have before you apply for your first data science job. The technical skills and soft skills you need to become a Data Scientist are also discussed in this book. Next, you'll find some of the best data science case studies that will help you understand what your approach should be while solving a business problem. Ultimately, you will also find some of the most important data science interview questions with their solutions at the end. I hope this book will add a lot of value to your data science skills and that you will feel confident in your entire journey to become Data Scientist.

Practical Machine Learning with Python

Author : Dipanjan Sarkar
Publisher : Apress
ISBN 13 : 1484232070
Total Pages : 545 pages
Book Rating : 4.4/5 (842 download)

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Book Synopsis Practical Machine Learning with Python by : Dipanjan Sarkar

Download or read book Practical Machine Learning with Python written by Dipanjan Sarkar and published by Apress. This book was released on 2017-12-20 with total page 545 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully. Practical Machine Learning with Python follows a structured and comprehensive three-tiered approach packed with hands-on examples and code. Part 1 focuses on understanding machine learning concepts and tools. This includes machine learning basics with a broad overview of algorithms, techniques, concepts and applications, followed by a tour of the entire Python machine learning ecosystem. Brief guides for useful machine learning tools, libraries and frameworks are also covered. Part 2 details standard machine learning pipelines, with an emphasis on data processing analysis, feature engineering, and modeling. You will learn how to process, wrangle, summarize and visualize data in its various forms. Feature engineering and selection methodologies will be covered in detail with real-world datasets followed by model building, tuning, interpretation and deployment. Part 3 explores multiple real-world case studies spanning diverse domains and industries like retail, transportation, movies, music, marketing, computer vision and finance. For each case study, you will learn the application of various machine learning techniques and methods. The hands-on examples will help you become familiar with state-of-the-art machine learning tools and techniques and understand what algorithms are best suited for any problem. Practical Machine Learning with Python will empower you to start solving your own problems with machine learning today! What You'll Learn Execute end-to-end machine learning projects and systems Implement hands-on examples with industry standard, open source, robust machine learning tools and frameworks Review case studies depicting applications of machine learning and deep learning on diverse domains and industries Apply a wide range of machine learning models including regression, classification, and clustering. Understand and apply the latest models and methodologies from deep learning including CNNs, RNNs, LSTMs and transfer learning. Who This Book Is For IT professionals, analysts, developers, data scientists, engineers, graduate students

Machine Learning for Decision Sciences with Case Studies in Python

Author : S. Sumathi
Publisher : CRC Press
ISBN 13 : 1000590933
Total Pages : 477 pages
Book Rating : 4.0/5 (5 download)

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Book Synopsis Machine Learning for Decision Sciences with Case Studies in Python by : S. Sumathi

Download or read book Machine Learning for Decision Sciences with Case Studies in Python written by S. Sumathi and published by CRC Press. This book was released on 2022-07-08 with total page 477 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a detailed description of machine learning algorithms in data analytics, data science life cycle, Python for machine learning, linear regression, logistic regression, and so forth. It addresses the concepts of machine learning in a practical sense providing complete code and implementation for real-world examples in electrical, oil and gas, e-commerce, and hi-tech industries. The focus is on Python programming for machine learning and patterns involved in decision science for handling data. Features: Explains the basic concepts of Python and its role in machine learning. Provides comprehensive coverage of feature engineering including real-time case studies. Perceives the structural patterns with reference to data science and statistics and analytics. Includes machine learning-based structured exercises. Appreciates different algorithmic concepts of machine learning including unsupervised, supervised, and reinforcement learning. This book is aimed at researchers, professionals, and graduate students in data science, machine learning, computer science, and electrical and computer engineering.

Practical Machine Learning for Data Analysis Using Python

Author : Abdulhamit Subasi
Publisher : Academic Press
ISBN 13 : 0128213809
Total Pages : 536 pages
Book Rating : 4.1/5 (282 download)

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Book Synopsis Practical Machine Learning for Data Analysis Using Python by : Abdulhamit Subasi

Download or read book Practical Machine Learning for Data Analysis Using Python written by Abdulhamit Subasi and published by Academic Press. This book was released on 2020-06-05 with total page 536 pages. Available in PDF, EPUB and Kindle. Book excerpt: Practical Machine Learning for Data Analysis Using Python is a problem solver's guide for creating real-world intelligent systems. It provides a comprehensive approach with concepts, practices, hands-on examples, and sample code. The book teaches readers the vital skills required to understand and solve different problems with machine learning. It teaches machine learning techniques necessary to become a successful practitioner, through the presentation of real-world case studies in Python machine learning ecosystems. The book also focuses on building a foundation of machine learning knowledge to solve different real-world case studies across various fields, including biomedical signal analysis, healthcare, security, economics, and finance. Moreover, it covers a wide range of machine learning models, including regression, classification, and forecasting. The goal of the book is to help a broad range of readers, including IT professionals, analysts, developers, data scientists, engineers, and graduate students, to solve their own real-world problems. - Offers a comprehensive overview of the application of machine learning tools in data analysis across a wide range of subject areas - Teaches readers how to apply machine learning techniques to biomedical signals, financial data, and healthcare data - Explores important classification and regression algorithms as well as other machine learning techniques - Explains how to use Python to handle data extraction, manipulation, and exploration techniques, as well as how to visualize data spread across multiple dimensions and extract useful features

Data Science Projects with Python

Author : Stephen Klosterman
Publisher : Packt Publishing Ltd
ISBN 13 : 1800569440
Total Pages : 433 pages
Book Rating : 4.8/5 (5 download)

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Book Synopsis Data Science Projects with Python by : Stephen Klosterman

Download or read book Data Science Projects with Python written by Stephen Klosterman and published by Packt Publishing Ltd. This book was released on 2021-07-29 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gain hands-on experience of Python programming with industry-standard machine learning techniques using pandas, scikit-learn, and XGBoost Key FeaturesThink critically about data and use it to form and test a hypothesisChoose an appropriate machine learning model and train it on your dataCommunicate data-driven insights with confidence and clarityBook Description If data is the new oil, then machine learning is the drill. As companies gain access to ever-increasing quantities of raw data, the ability to deliver state-of-the-art predictive models that support business decision-making becomes more and more valuable. In this book, you'll work on an end-to-end project based around a realistic data set and split up into bite-sized practical exercises. This creates a case-study approach that simulates the working conditions you'll experience in real-world data science projects. You'll learn how to use key Python packages, including pandas, Matplotlib, and scikit-learn, and master the process of data exploration and data processing, before moving on to fitting, evaluating, and tuning algorithms such as regularized logistic regression and random forest. Now in its second edition, this book will take you through the end-to-end process of exploring data and delivering machine learning models. Updated for 2021, this edition includes brand new content on XGBoost, SHAP values, algorithmic fairness, and the ethical concerns of deploying a model in the real world. By the end of this data science book, you'll have the skills, understanding, and confidence to build your own machine learning models and gain insights from real data. What you will learnLoad, explore, and process data using the pandas Python packageUse Matplotlib to create compelling data visualizationsImplement predictive machine learning models with scikit-learnUse lasso and ridge regression to reduce model overfittingEvaluate random forest and logistic regression model performanceDeliver business insights by presenting clear, convincing conclusionsWho this book is for Data Science Projects with Python – Second Edition is for anyone who wants to get started with data science and machine learning. If you're keen to advance your career by using data analysis and predictive modeling to generate business insights, then this book is the perfect place to begin. To quickly grasp the concepts covered, it is recommended that you have basic experience of programming with Python or another similar language, and a general interest in statistics.

Machine Learning for Hackers

Author : Drew Conway
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1449330533
Total Pages : 323 pages
Book Rating : 4.4/5 (493 download)

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Book Synopsis Machine Learning for Hackers by : Drew Conway

Download or read book Machine Learning for Hackers written by Drew Conway and published by "O'Reilly Media, Inc.". This book was released on 2012-02-13 with total page 323 pages. Available in PDF, EPUB and Kindle. Book excerpt: If you’re an experienced programmer interested in crunching data, this book will get you started with machine learning—a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation. Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you’ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research. Develop a naïve Bayesian classifier to determine if an email is spam, based only on its text Use linear regression to predict the number of page views for the top 1,000 websites Learn optimization techniques by attempting to break a simple letter cipher Compare and contrast U.S. Senators statistically, based on their voting records Build a “whom to follow” recommendation system from Twitter data

Introduction to Machine Learning with Python

Author : William Gray
Publisher : Independently Published
ISBN 13 : 9781096755364
Total Pages : 276 pages
Book Rating : 4.7/5 (553 download)

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Book Synopsis Introduction to Machine Learning with Python by : William Gray

Download or read book Introduction to Machine Learning with Python written by William Gray and published by Independently Published. This book was released on 2019-05-04 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: What exactly is machine learning and why is it so valuable in the online business ? Are you thinking of learning Python machine learning ?This book teach well you the practical ways to do it ! ★★★ Buy the Paperback version and get the Kindle Book versions for FREE ★★★ Machine Learning is a branch of AI that applied algorithms to learn from data and create predictions - this is important in predicting the world around us. Python is a popular and open-source programming language. In addition, it is one of the most applied languages in artificial intelligence and other scientific fields. Today, it is a top skill in high demand in the job market. Machine learning has become an integral part of many commercial applications and research projects. Using Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. Inside Introduction to Machine Learning with Python, you'll learn: Fundamental concepts and applications of machine learning Understand the various categories of machine learning algorithms. Some of the branches of Artificial Intelligence The basics of Python Concepts of Machine Learning using Python Python Machine Learning Applications Machine Learning Case Studies with Python The way that Python evolved throughout time And many more Throughout the recent years, artificial intelligence and machine learning have made some enormous, significant strides in terms of universal, global applicability. You'll discover the steps required to develop a successful machine-learning application using Python. Introduction to Machine Learning with Python is a step-by-step guide for any person who wants to start learning Artificial Intelligence - It will help you in preparing a solid foundation and learn any other high-level courses. Stay ahead and make a choice that will last... If You like to know more, scroll to the top and select " BUY NOW " buttom ★★★ Buy the Paperback version and get the Kindle Book versions for FREE ★★★

Introduction to Machine Learning in the Cloud with Python

Author : Pramod Gupta
Publisher : Springer Nature
ISBN 13 : 3030712702
Total Pages : 284 pages
Book Rating : 4.0/5 (37 download)

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Book Synopsis Introduction to Machine Learning in the Cloud with Python by : Pramod Gupta

Download or read book Introduction to Machine Learning in the Cloud with Python written by Pramod Gupta and published by Springer Nature. This book was released on 2021-04-28 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an introduction to machine learning and cloud computing, both from a conceptual level, along with their usage with underlying infrastructure. The authors emphasize fundamentals and best practices for using AI and ML in a dynamic infrastructure with cloud computing and high security, preparing readers to select and make use of appropriate techniques. Important topics are demonstrated using real applications and case studies.

Machine Learning with Python: Comprehensive Beginner's Guide to Machine Learning in Python with Exercises and Case Studies

Author : Paige Jacobs
Publisher : Machine Learning with Python
ISBN 13 : 9781797861173
Total Pages : 184 pages
Book Rating : 4.8/5 (611 download)

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Book Synopsis Machine Learning with Python: Comprehensive Beginner's Guide to Machine Learning in Python with Exercises and Case Studies by : Paige Jacobs

Download or read book Machine Learning with Python: Comprehensive Beginner's Guide to Machine Learning in Python with Exercises and Case Studies written by Paige Jacobs and published by Machine Learning with Python. This book was released on 2019-02-22 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: There are different processes that are included within the meaning of the term "learning." If you refer to a dictionary and look up the meaning of learning, then you will come across different phrases like "to gain knowledge, understanding of or skill, through study, experience or instruction," and "the change in behavioral tendencies through experience."If you look at it superficially, in respect to machines, it is safe to say that any change in the structure of the machine, the data stored in the memory or its data composition, in order to improve the efficiency and the effectiveness of the machine's performance is an undeniable sign of learning in a machine. When you start to delve deeper into this subject, only a couple of these changes can be categorized as machine learning.In this book, you will learn about machine learning using Python. The information given in each of the chapters will improve your understanding of machine learning programming using Python. The sample codes along with the case studies will enable you to test your knowledge.So get started with your knowledge of Machine Learning with Python with this book!

Machine Learning and Data Science Blueprints for Finance

Author : Hariom Tatsat
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492073008
Total Pages : 432 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Machine Learning and Data Science Blueprints for Finance by : Hariom Tatsat

Download or read book Machine Learning and Data Science Blueprints for Finance written by Hariom Tatsat and published by "O'Reilly Media, Inc.". This book was released on 2020-10-01 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You’ll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP). Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development. You’ll explore real-life problems faced by practitioners and learn scientifically sound solutions supported by code and examples. This book covers: Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction Algorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio management Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations

Codeless Deep Learning with KNIME

Author : Kathrin Melcher
Publisher : Packt Publishing Ltd
ISBN 13 : 180056242X
Total Pages : 385 pages
Book Rating : 4.8/5 (5 download)

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Book Synopsis Codeless Deep Learning with KNIME by : Kathrin Melcher

Download or read book Codeless Deep Learning with KNIME written by Kathrin Melcher and published by Packt Publishing Ltd. This book was released on 2020-11-27 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover how to integrate KNIME Analytics Platform with deep learning libraries to implement artificial intelligence solutions Key FeaturesBecome well-versed with KNIME Analytics Platform to perform codeless deep learningDesign and build deep learning workflows quickly and more easily using the KNIME GUIDiscover different deployment options without using a single line of code with KNIME Analytics PlatformBook Description KNIME Analytics Platform is an open source software used to create and design data science workflows. This book is a comprehensive guide to the KNIME GUI and KNIME deep learning integration, helping you build neural network models without writing any code. It’ll guide you in building simple and complex neural networks through practical and creative solutions for solving real-world data problems. Starting with an introduction to KNIME Analytics Platform, you’ll get an overview of simple feed-forward networks for solving simple classification problems on relatively small datasets. You’ll then move on to build, train, test, and deploy more complex networks, such as autoencoders, recurrent neural networks (RNNs), long short-term memory (LSTM), and convolutional neural networks (CNNs). In each chapter, depending on the network and use case, you’ll learn how to prepare data, encode incoming data, and apply best practices. By the end of this book, you’ll have learned how to design a variety of different neural architectures and will be able to train, test, and deploy the final network. What you will learnUse various common nodes to transform your data into the right structure suitable for training a neural networkUnderstand neural network techniques such as loss functions, backpropagation, and hyperparametersPrepare and encode data appropriately to feed it into the networkBuild and train a classic feedforward networkDevelop and optimize an autoencoder network for outlier detectionImplement deep learning networks such as CNNs, RNNs, and LSTM with the help of practical examplesDeploy a trained deep learning network on real-world dataWho this book is for This book is for data analysts, data scientists, and deep learning developers who are not well-versed in Python but want to learn how to use KNIME GUI to build, train, test, and deploy neural networks with different architectures. The practical implementations shown in the book do not require coding or any knowledge of dedicated scripts, so you can easily implement your knowledge into practical applications. No prior experience of using KNIME is required to get started with this book.

Data Mining with R

Author : Luis Torgo
Publisher : CRC Press
ISBN 13 : 1315399091
Total Pages : 426 pages
Book Rating : 4.3/5 (153 download)

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Book Synopsis Data Mining with R by : Luis Torgo

Download or read book Data Mining with R written by Luis Torgo and published by CRC Press. This book was released on 2016-11-30 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Mining with R: Learning with Case Studies, Second Edition uses practical examples to illustrate the power of R and data mining. Providing an extensive update to the best-selling first edition, this new edition is divided into two parts. The first part will feature introductory material, including a new chapter that provides an introduction to data mining, to complement the already existing introduction to R. The second part includes case studies, and the new edition strongly revises the R code of the case studies making it more up-to-date with recent packages that have emerged in R. The book does not assume any prior knowledge about R. Readers who are new to R and data mining should be able to follow the case studies, and they are designed to be self-contained so the reader can start anywhere in the document. The book is accompanied by a set of freely available R source files that can be obtained at the book’s web site. These files include all the code used in the case studies, and they facilitate the "do-it-yourself" approach followed in the book. Designed for users of data analysis tools, as well as researchers and developers, the book should be useful for anyone interested in entering the "world" of R and data mining. About the Author Luís Torgo is an associate professor in the Department of Computer Science at the University of Porto in Portugal. He teaches Data Mining in R in the NYU Stern School of Business’ MS in Business Analytics program. An active researcher in machine learning and data mining for more than 20 years, Dr. Torgo is also a researcher in the Laboratory of Artificial Intelligence and Data Analysis (LIAAD) of INESC Porto LA.

Fundamentals of Machine Learning for Predictive Data Analytics, second edition

Author : John D. Kelleher
Publisher : MIT Press
ISBN 13 : 0262361108
Total Pages : 853 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis Fundamentals of Machine Learning for Predictive Data Analytics, second edition by : John D. Kelleher

Download or read book Fundamentals of Machine Learning for Predictive Data Analytics, second edition written by John D. Kelleher and published by MIT Press. This book was released on 2020-10-20 with total page 853 pages. Available in PDF, EPUB and Kindle. Book excerpt: The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning.

Artificial Intelligence-Aided Materials Design

Author : Rajesh Jha
Publisher : CRC Press
ISBN 13 : 1000541339
Total Pages : 363 pages
Book Rating : 4.0/5 (5 download)

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Book Synopsis Artificial Intelligence-Aided Materials Design by : Rajesh Jha

Download or read book Artificial Intelligence-Aided Materials Design written by Rajesh Jha and published by CRC Press. This book was released on 2022-03-15 with total page 363 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes the application of artificial intelligence (AI)/machine learning (ML) concepts to develop predictive models that can be used to design alloy materials, including hard and soft magnetic alloys, nickel-base superalloys, titanium-base alloys, and aluminum-base alloys. Readers new to AI/ML algorithms can use this book as a starting point and use the MATLAB® and Python implementation of AI/ML algorithms through included case studies. Experienced AI/ML researchers who want to try new algorithms can use this book and study the case studies for reference. Offers advantages and limitations of several AI concepts and their proper implementation in various data types generated through experiments and computer simulations and from industries in different file formats Helps readers to develop predictive models through AI/ML algorithms by writing their own computer code or using resources where they do not have to write code Covers downloadable resources such as MATLAB GUI/APP and Python implementation that can be used on common mobile devices Discusses the CALPHAD approach and ways to use data generated from it Features a chapter on metallurgical/materials concepts to help readers understand the case studies and thus proper implementation of AI/ML algorithms under the framework of data-driven materials science Uses case studies to examine the importance of using unsupervised machine learning algorithms in determining patterns in datasets This book is written for materials scientists and metallurgists interested in the application of AI, ML, and data science in the development of new materials.

Practical Natural Language Processing with Python

Author : Mathangi Sri
Publisher : Apress
ISBN 13 : 9781484262450
Total Pages : 253 pages
Book Rating : 4.2/5 (624 download)

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Book Synopsis Practical Natural Language Processing with Python by : Mathangi Sri

Download or read book Practical Natural Language Processing with Python written by Mathangi Sri and published by Apress. This book was released on 2020-12-01 with total page 253 pages. Available in PDF, EPUB and Kindle. Book excerpt: Work with natural language tools and techniques to solve real-world problems. This book focuses on how natural language processing (NLP) is used in various industries. Each chapter describes the problem and solution strategy, then provides an intuitive explanation of how different algorithms work and a deeper dive on code and output in Python. Practical Natural Language Processing with Python follows a case study-based approach. Each chapter is devoted to an industry or a use case, where you address the real business problems in that industry and the various ways to solve them. You start with various types of text data before focusing on the customer service industry, the type of data available in that domain, and the common NLP problems encountered. Here you cover the bag-of-words model supervised learning technique as you try to solve the case studies. Similar depth is given to other use cases such as online reviews, bots, finance, and so on. As you cover the problems in these industries you’ll also cover sentiment analysis, named entity recognition, word2vec, word similarities, topic modeling, deep learning, and sequence to sequence modelling. By the end of the book, you will be able to handle all types of NLP problems independently. You will also be able to think in different ways to solve language problems. Code and techniques for all the problems are provided in the book. What You Will Learn Build an understanding of NLP problems in industry Gain the know-how to solve a typical NLP problem using language-based models and machine learning Discover the best methods to solve a business problem using NLP - the tried and tested ones Understand the business problems that are tough to solve Who This Book Is For Analytics and data science professionals who want to kick start NLP, and NLP professionals who want to get new ideas to solve the problems at hand.