Python 3 And Feature Engineering
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Python 3 And Feature Engineering
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Author : Oswald Campesato
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2023-12-15
Python 3 And Feature Engineering written by Oswald Campesato and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-15 with Computers categories.
This book is designed for data scientists, machine learning practitioners, and anyone with a foundational understanding of Python 3.x. In the evolving field of data science, the ability to manipulate and understand datasets is crucial. The book offers content for mastering these skills using Python 3. The book provides a fast-paced introduction to a wealth of feature engineering concepts, equipping readers with the knowledge needed to transform raw data into meaningful information. Inside, you’ll find a detailed exploration of various types of data, methodologies for outlier detection using Scikit-Learn, strategies for robust data cleaning, and the intricacies of data wrangling. The book further explores feature selection, detailing methods for handling imbalanced datasets, and gives a practical overview of feature engineering, including scaling and extraction techniques necessary for different machine learning algorithms. It concludes with a treatment of dimensionality reduction, where you’ll navigate through complex concepts like PCA and various reduction techniques, with an emphasis on the powerful Scikit-Learn framework.
Feature Engineering For Machine Learning In Python
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Author : Pythquill Publishing
language : en
Publisher: Independently Published
Release Date : 2025-06-30
Feature Engineering For Machine Learning In Python written by Pythquill Publishing and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-30 with Computers categories.
Master the Foundations of Feature Engineering: Understand what features are, why they're crucial for machine learning model performance and interpretability, and navigate the complete feature engineering lifecycle from brainstorming to deployment. Set Up Your Python Environment for Success: Become proficient with essential libraries like NumPy, Pandas, and Scikit-learn for data manipulation, analysis, and feature engineering implementation. Handle Missing Data Effectively: Learn to identify and apply various imputation strategies for both numerical and categorical data, ensuring your models receive clean and complete inputs. Transform Numerical Data for Optimal Performance: Discover techniques for scaling, normalizing, discretizing, and transforming skewed numerical features to meet model assumptions and improve accuracy. Encode Categorical Data for Machine Learning Models: Explore a wide array of encoding methods, from One-Hot and Label Encoding to advanced techniques like Target and WoE Encoding, and understand when to apply each for different data types and models. Engineer Powerful Features from Text Data: Master text preprocessing, apply Bag-of-Words and TF-IDF models, and leverage word embeddings (Word2Vec, GloVe) to extract meaningful insights from unstructured text. Extract Actionable Insights from Time Series Data: Learn to create date-time components, lag features, rolling window statistics, and incorporate seasonality and trend information for robust time series modeling. Gain an Overview of Feature Engineering for Specialized Data: Get introduced to key techniques for image, geospatial, and graph data, and understand how to leverage pre-trained models for feature extraction. Select the Most Relevant Features: Implement various feature selection methods, including filter, wrapper, and embedded techniques, to reduce dimensionality, combat overfitting, and enhance model interpretability. Apply Dimensionality Reduction Techniques: Understand and utilize methods like PCA, LDA, t-SNE, and UMAP to reduce the number of features while preserving essential information. Automate and Streamline Feature Engineering Workflows: Explore tools like Featuretools and tsfresh to automate feature creation, saving time and improving efficiency. Build Robust and Reproducible Feature Engineering Pipelines: Learn to construct and manage end-to-end pipelines using Scikit-learn, ensuring consistency and preventing data leakage. Prevent Data Leakage and Build Trustworthy Models: Identify common sources of data leakage in feature engineering and implement strategies to avoid it, leading to more reliable model evaluations. Understand Feature Stores and Their Role in MLOps: Grasp the concepts of feature stores for consistent feature management, reusability, and deployment in production environments. Apply Feature Engineering to Real-World Problems: Work through practical case studies in customer churn prediction, sentiment analysis, and sales forecasting, consolidating your knowledge across different data types.
Machine Learning And Deep Learning Using Python And Tensorflow
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Author : Venkata Reddy Konasani
language : en
Publisher: McGraw Hill Professional
Release Date : 2021-04-29
Machine Learning And Deep Learning Using Python And Tensorflow written by Venkata Reddy Konasani and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-29 with Technology & Engineering categories.
Understand the principles and practices of machine learning and deep learning This hands-on guide lays out machine learning and deep learning techniques and technologies in a style that is approachable, using just the basic math required. Written by a pair of experts in the field, Machine Learning and Deep Learning Using Python and TensorFlow contains case studies in several industries, including banking, insurance, e-commerce, retail, and healthcare. The book shows how to utilize machine learning and deep learning functions in today’s smart devices and apps. You will get download links for datasets, code, and sample projects referred to in the text. Coverage includes: Machine learning and deep learning concepts Python programming and statistics fundamentals Regression and logistic regression Decision trees Model selection and cross-validation Cluster analysis Random forests and boosting Artificial neural networks TensorFlow and Keras Deep learning hyperparameters Convolutional neural networks Recurrent neural networks and long short-term memory
Hands On Feature Engineering With Python
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Author : Sahiba Chopra
language : en
Publisher:
Release Date : 2019
Hands On Feature Engineering With Python written by Sahiba Chopra and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
A hands-on course to speed up the predicting power of machine learning algorithms About This Video Get expert knowledge on different future engineering techniques on different datasets Explore feature engineering techniques used in numerical datasets Uncover and execute feature extraction popular and useful techniques Build an ensemble model based on a feature engineered dataset In Detail Feature engineering is the most important aspect of machine learning. You know that every day you put off learning the process, you are hurting your model's performance. Studies repeatedly prove that feature engineering can be much more powerful than the choice of algorithms. Yet the field of feature engineering can seem overwhelming and confusing. This course offers you the single best solution. In this course, all of the recommendations have been extensively tested and proven on real-world problems. You'll find everything included: the recommendations, the code, the data sources, and the rationale. You'll get an over-the-shoulder, step-by-step approach for every situation, and each segment can stand alone, allowing you to jump immediately to the topics most important to you. By the end of the course, you'll have a clear, concise path to feature engineering and will enable you to get improved results by applying feature engineering techniques on your datasets Downloading the example code for this course: You can download the example code files for this course on GitHub at the following link: https://github.com/PacktPublishing/Hands-On-Feature-Engineering-with-Python . If you require support please email: [email protected].
Feature Engineering For Machine Learning In Python
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Author : PYTHQUILL. PUBLISHING
language : en
Publisher:
Release Date : 2025
Feature Engineering For Machine Learning In Python written by PYTHQUILL. PUBLISHING and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025 with categories.
Python Feature Engineering Cookbook Second Edition
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Author : Soledad Galli
language : en
Publisher:
Release Date : 2022-10-31
Python Feature Engineering Cookbook Second Edition written by Soledad Galli and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-31 with categories.
Create end-to-end, reproducible feature engineering pipelines that can be deployed into production using open-source Python libraries Key Features: Learn and implement feature engineering best practices Reinforce your learning with the help of multiple hands-on recipes Build end-to-end feature engineering pipelines that are performant and reproducible Book Description: Feature engineering, the process of transforming variables and creating features, albeit time-consuming, ensures that your machine learning models perform seamlessly. This second edition of Python Feature Engineering Cookbook will take the struggle out of feature engineering by showing you how to use open source Python libraries to accelerate the process via a plethora of practical, hands-on recipes. This updated edition begins by addressing fundamental data challenges such as missing data and categorical values, before moving on to strategies for dealing with skewed distributions and outliers. The concluding chapters show you how to develop new features from various types of data, including text, time series, and relational databases. With the help of numerous open source Python libraries, you'll learn how to implement each feature engineering method in a performant, reproducible, and elegant manner. By the end of this Python book, you will have the tools and expertise needed to confidently build end-to-end and reproducible feature engineering pipelines that can be deployed into production. What You Will Learn: Impute missing data using various univariate and multivariate methods Encode categorical variables with one-hot, ordinal, and count encoding Handle highly cardinal categorical variables Transform, discretize, and scale your variables Create variables from date and time with pandas and Feature-engine Combine variables into new features Extract features from text as well as from transactional data with Featuretools Create features from time series data with tsfresh Who this book is for: This book is for machine learning and data science students and professionals, as well as software engineers working on machine learning model deployment, who want to learn more about how to transform their data and create new features to train machine learning models in a better way.
Python Feature Engineering Cookbook
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Author : Soledad Galli
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-01-22
Python Feature Engineering Cookbook written by Soledad Galli 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 2020-01-22 with Computers categories.
Extract accurate information from data to train and improve machine learning models using NumPy, SciPy, pandas, and scikit-learn libraries Key FeaturesDiscover solutions for feature generation, feature extraction, and feature selectionUncover the end-to-end feature engineering process across continuous, discrete, and unstructured datasetsImplement modern feature extraction techniques using Python's pandas, scikit-learn, SciPy and NumPy librariesBook Description Feature engineering is invaluable for developing and enriching your machine learning models. In this cookbook, you will work with the best tools to streamline your feature engineering pipelines and techniques and simplify and improve the quality of your code. Using Python libraries such as pandas, scikit-learn, Featuretools, and Feature-engine, you’ll learn how to work with both continuous and discrete datasets and be able to transform features from unstructured datasets. You will develop the skills necessary to select the best features as well as the most suitable extraction techniques. This book will cover Python recipes that will help you automate feature engineering to simplify complex processes. You’ll also get to grips with different feature engineering strategies, such as the box-cox transform, power transform, and log transform across machine learning, reinforcement learning, and natural language processing (NLP) domains. By the end of this book, you’ll have discovered tips and practical solutions to all of your feature engineering problems. What you will learnSimplify your feature engineering pipelines with powerful Python packagesGet to grips with imputing missing valuesEncode categorical variables with a wide set of techniquesExtract insights from text quickly and effortlesslyDevelop features from transactional data and time series dataDerive new features by combining existing variablesUnderstand how to transform, discretize, and scale your variablesCreate informative variables from date and timeWho this book is for This book is for machine learning professionals, AI engineers, data scientists, and NLP and reinforcement learning engineers who want to optimize and enrich their machine learning models with the best features. Knowledge of machine learning and Python coding will assist you with understanding the concepts covered in this book.
Manufacturing Engineering
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Author :
language : en
Publisher:
Release Date : 2009
Manufacturing Engineering written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Production engineering categories.
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
Mechanical Engineering
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Author :
language : en
Publisher:
Release Date : 2008
Mechanical Engineering written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Mechanical engineering categories.