Feature Engineering Made Easy
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Feature Engineering Made Easy
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Author : Sinan Ozdemir
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-01-22
Feature Engineering Made Easy written by Sinan Ozdemir and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-01-22 with Computers categories.
A perfect guide to speed up the predicting power of machine learning algorithms Key Features Design, discover, and create dynamic, efficient features for your machine learning application Understand your data in-depth and derive astonishing data insights with the help of this Guide Grasp powerful feature-engineering techniques and build machine learning systems Book Description Feature engineering is the most important step in creating powerful machine learning systems. This book will take you through the entire feature-engineering journey to make your machine learning much more systematic and effective. You will start with understanding your data—often the success of your ML models depends on how you leverage different feature types, such as continuous, categorical, and more, You will learn when to include a feature, when to omit it, and why, all by understanding error analysis and the acceptability of your models. You will learn to convert a problem statement into useful new features. You will learn to deliver features driven by business needs as well as mathematical insights. You'll also learn how to use machine learning on your machines, automatically learning amazing features for your data. By the end of the book, you will become proficient in Feature Selection, Feature Learning, and Feature Optimization. What you will learn Identify and leverage different feature types Clean features in data to improve predictive power Understand why and how to perform feature selection, and model error analysis Leverage domain knowledge to construct new features Deliver features based on mathematical insights Use machine-learning algorithms to construct features Master feature engineering and optimization Harness feature engineering for real world applications through a structured case study Who this book is for If you are a data science professional or a machine learning engineer looking to strengthen your predictive analytics model, then this book is a perfect guide for you. Some basic understanding of the machine learning concepts and Python scripting would be enough to get started with this book.
Feature Engineering Bookcamp
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Author : Sinan Ozdemir
language : en
Publisher: Simon and Schuster
Release Date : 2022-10-18
Feature Engineering Bookcamp written by Sinan Ozdemir and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-18 with Computers categories.
Deliver huge improvements to your machine learning pipelines without spending hours fine-tuning parameters! This book’s practical case-studies reveal feature engineering techniques that upgrade your data wrangling—and your ML results. In Feature Engineering Bookcamp you will learn how to: Identify and implement feature transformations for your data Build powerful machine learning pipelines with unstructured data like text and images Quantify and minimize bias in machine learning pipelines at the data level Use feature stores to build real-time feature engineering pipelines Enhance existing machine learning pipelines by manipulating the input data Use state-of-the-art deep learning models to extract hidden patterns in data Feature Engineering Bookcamp guides you through a collection of projects that give you hands-on practice with core feature engineering techniques. You’ll work with feature engineering practices that speed up the time it takes to process data and deliver real improvements in your model’s performance. This instantly-useful book skips the abstract mathematical theory and minutely-detailed formulas; instead you’ll learn through interesting code-driven case studies, including tweet classification, COVID detection, recidivism prediction, stock price movement detection, and more. About the technology Get better output from machine learning pipelines by improving your training data! Use feature engineering, a machine learning technique for designing relevant input variables based on your existing data, to simplify training and enhance model performance. While fine-tuning hyperparameters or tweaking models may give you a minor performance bump, feature engineering delivers dramatic improvements by transforming your data pipeline. About the book Feature Engineering Bookcamp walks you through six hands-on projects where you’ll learn to upgrade your training data using feature engineering. Each chapter explores a new code-driven case study, taken from real-world industries like finance and healthcare. You’ll practice cleaning and transforming data, mitigating bias, and more. The book is full of performance-enhancing tips for all major ML subdomains—from natural language processing to time-series analysis. What's inside Identify and implement feature transformations Build machine learning pipelines with unstructured data Quantify and minimize bias in ML pipelines Use feature stores to build real-time feature engineering pipelines Enhance existing pipelines by manipulating input data About the reader For experienced machine learning engineers familiar with Python. About the author Sinan Ozdemir is the founder and CTO of Shiba, a former lecturer of Data Science at Johns Hopkins University, and the author of multiple textbooks on data science and machine learning. Table of Contents 1 Introduction to feature engineering 2 The basics of feature engineering 3 Healthcare: Diagnosing COVID-19 4 Bias and fairness: Modeling recidivism 5 Natural language processing: Classifying social media sentiment 6 Computer vision: Object recognition 7 Time series analysis: Day trading with machine learning 8 Feature stores 9 Putting it all together
Feature Engineering And Selection
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Author : Max Kuhn
language : en
Publisher: CRC Press
Release Date : 2019-07-25
Feature Engineering And Selection written by Max Kuhn and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-25 with Business & Economics categories.
The process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset of predictors for improving model performance. A variety of example data sets are used to illustrate the techniques along with R programs for reproducing the results.
The Art Of Feature Engineering
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Author : Pablo Duboue
language : en
Publisher: Cambridge University Press
Release Date : 2020-06-25
The Art Of Feature Engineering written by Pablo Duboue and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-25 with Computers categories.
A practical guide for data scientists who want to improve the performance of any machine learning solution with feature engineering.
Software Engineering Made Easy
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Author : Marco Gähler
language : en
Publisher: Springer Nature
Release Date : 2025-07-04
Software Engineering Made Easy written by Marco Gähler and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-04 with Computers categories.
Learn how to write good code for humans. This user-friendly book is a comprehensive guide to writing clear and bug-free code. It integrates established programming principles and outlines expert-driven rules to prevent you from over-complicating your code. You’ll take a practical approach to programming, applicable to any programming language and explore useful advice and concrete examples in a concise and compact form. Sections on Single Responsibility Principle, naming, levels of abstraction, testing, logic (if/else), interfaces, and more, reinforce how to effectively write low-complexity code. While many of the principles addressed in this book are well-established, it offers you a single resource. Software Engineering Made Easy modernizes classic software programming principles with quick tips relevant to real-world applications. Most importantly, it is written with a keen awareness of how humans think. The end-result is human-readable code that improves maintenance, collaboration, and debugging—critical for software engineers working together to make purposeful impacts in the world. What You Will Learn Understand the essence of software engineering. Simplify your code using expert techniques across multiple languages. See how to structure classes. Manage the complexity of your code by using level abstractions. Review test functions and explore various types of testing. Who This Book Is For Intermediate programmers who have a basic understanding of coding but are relatively new to the workforce. Applicable to any programming language, but proficiency in C++ or Python is preferred. Advanced programmers may also benefit from learning how to deprogram bad habits and de-complicate their code.
Applied Text Mining
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Author : Usman Qamar
language : en
Publisher: Springer Nature
Release Date : 2024-06-10
Applied Text Mining written by Usman Qamar and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-10 with Computers categories.
This textbook covers the concepts, theories, and implementations of text mining and natural language processing (NLP). It covers both the theory and the practical implementation, and every concept is explained with simple and easy-to-understand examples. It consists of three parts. In Part 1 which consists of three chapters details about basic concepts and applications of text mining are provided, including eg sentiment analysis and opinion mining. It builds a strong foundation for the reader in order to understand the remaining parts. In the five chapters of Part 2, all the core concepts of text analytics like feature engineering, text classification, text clustering, text summarization, topic mapping, and text visualization are covered. Finally, in Part 3 there are three chapters covering deep-learning-based text mining, which is the dominating method applied to practically all text mining tasks nowadays. Various deep learning approaches to text mining are covered, includingmodels for processing and parsing text, for lexical analysis, and for machine translation. All three parts include large parts of Python code that shows the implementation of the described concepts and approaches. The textbook was specifically written to enable the teaching of both basic and advanced concepts from one single book. The implementation of every text mining task is carefully explained, based Python as the programming language and Spacy and NLTK as Natural Language Processing libraries. The book is suitable for both undergraduate and graduate students in computer science and engineering.
Solar Energy Engineering Made Easy
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Author :
language : en
Publisher: Centre for Application Of Renewable Energy
Release Date : 2014-11-23
Solar Energy Engineering Made Easy written by and has been published by Centre for Application Of Renewable Energy this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-11-23 with Technology & Engineering categories.
Feature Engineering For Machine Learning
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Author : Alice Zheng
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2018-03-23
Feature Engineering For Machine Learning written by Alice Zheng and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-23 with Computers categories.
Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you’ll learn techniques for extracting and transforming features—the numeric representations of raw data—into formats for machine-learning models. Each chapter guides you through a single data problem, such as how to represent text or image data. Together, these examples illustrate the main principles of feature engineering. Rather than simply teach these principles, authors Alice Zheng and Amanda Casari focus on practical application with exercises throughout the book. The closing chapter brings everything together by tackling a real-world, structured dataset with several feature-engineering techniques. Python packages including numpy, Pandas, Scikit-learn, and Matplotlib are used in code examples. You’ll examine: Feature engineering for numeric data: filtering, binning, scaling, log transforms, and power transforms Natural text techniques: bag-of-words, n-grams, and phrase detection Frequency-based filtering and feature scaling for eliminating uninformative features Encoding techniques of categorical variables, including feature hashing and bin-counting Model-based feature engineering with principal component analysis The concept of model stacking, using k-means as a featurization technique Image feature extraction with manual and deep-learning techniques
Hands On Data Analysis With Pandas
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Author : Stefanie Molin
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-04-29
Hands On Data Analysis With Pandas written by Stefanie Molin and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-29 with Computers categories.
Get to grips with pandas by working with real datasets and master data discovery, data manipulation, data preparation, and handling data for analytical tasks Key Features Perform efficient data analysis and manipulation tasks using pandas 1.x Apply pandas to different real-world domains with the help of step-by-step examples Make the most of pandas as an effective data exploration tool Book DescriptionExtracting valuable business insights is no longer a ‘nice-to-have’, but an essential skill for anyone who handles data in their enterprise. Hands-On Data Analysis with Pandas is here to help beginners and those who are migrating their skills into data science get up to speed in no time. This book will show you how to analyze your data, get started with machine learning, and work effectively with the Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will learn how to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification using scikit-learn to make predictions based on past data. This updated edition will equip you with the skills you need to use pandas 1.x to efficiently perform various data manipulation tasks, reliably reproduce analyses, and visualize your data for effective decision making – valuable knowledge that can be applied across multiple domains.What you will learn Understand how data analysts and scientists gather and analyze data Perform data analysis and data wrangling using Python Combine, group, and aggregate data from multiple sources Create data visualizations with pandas, matplotlib, and seaborn Apply machine learning algorithms to identify patterns and make predictions Use Python data science libraries to analyze real-world datasets Solve common data representation and analysis problems using pandas Build Python scripts, modules, and packages for reusable analysis code Who this book is for This book is for data science beginners, data analysts, and Python developers who want to explore each stage of data analysis and scientific computing using a wide range of datasets. Data scientists looking to implement pandas in their machine learning workflow will also find plenty of valuable know-how as they progress. You’ll find it easier to follow along with this book if you have a working knowledge of the Python programming language, but a Python crash-course tutorial is provided in the code bundle for anyone who needs a refresher.
Machine Learning With R
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Author : Brett Lantz
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-05-29
Machine Learning With R written by Brett Lantz and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-29 with Computers categories.
Learn how to solve real-world data problems using machine learning and R Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features The 10th Anniversary Edition of the bestselling R machine learning book, updated with 50% new content for R 4.0.0 and beyond Harness the power of R to build flexible, effective, and transparent machine learning models Learn quickly with this clear, hands-on guide by machine learning expert Brett Lantz Book Description Machine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data. Machine Learning with R, Fourth Edition, provides a hands-on, accessible, and readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to know for data pre-processing, uncovering key insights, making new predictions, and visualizing your findings. This 10th Anniversary Edition features several new chapters that reflect the progress of machine learning in the last few years and help you build your data science skills and tackle more challenging problems, including making successful machine learning models and advanced data preparation, building better learners, and making use of big data. You'll also find this classic R data science book updated to R 4.0.0 with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Whether you're looking to take your first steps with R for machine learning or making sure your skills and knowledge are up to date, this is an unmissable read that will help you find powerful new insights in your data. What you will learn Learn the end-to-end process of machine learning from raw data to implementation Classify important outcomes using nearest neighbor and Bayesian methods Predict future events using decision trees, rules, and support vector machines Forecast numeric data and estimate financial values using regression methods Model complex processes with artificial neural networks Prepare, transform, and clean data using the tidyverse Evaluate your models and improve their performance Connect R to SQL databases and emerging big data technologies such as Spark, Hadoop, H2O, and TensorFlow Who this book is for This book is designed to help data scientists, actuaries, data analysts, financial analysts, social scientists, business and machine learning students, and any other practitioners who want a clear, accessible guide to machine learning with R. No R experience is required, although prior exposure to statistics and programming is helpful.