Feature Engineering For Modern Machine Learning With Scikit Learn
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Feature Engineering For Modern Machine Learning With Scikit Learn
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Author : Cuantum Technologies
language : en
Publisher: Staten House
Release Date : 2024-11-06
Feature Engineering For Modern Machine Learning With Scikit Learn written by Cuantum Technologies and has been published by Staten House this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-06 with Computers categories.
This Book grants Free Access to our e-learning Platform, which includes: ✅ Free Repository Code with all code blocks used in this book ✅ Access to Free Chapters of all our library of programming published books ✅ Free premium customer support ✅ Much more... Unleash the Power of Feature Engineering for Cutting-Edge Machine Learning Transform raw data into powerful features with Feature Engineering for Modern Machine Learning with Scikit-Learn: Advanced Data Science and Practical Applications. This essential guide takes you beyond the basics, teaching you how to create, optimize, and automate features that elevate machine learning models. With a focus on real-world applications and advanced techniques, this book equips data scientists, machine learning engineers, and analytics professionals with the skills to make impactful, data-driven decisions. Why Advanced Feature Engineering is Essential In machine learning, the quality of input data determines the quality of output predictions. Advanced feature engineering is the key to uncovering hidden patterns and meaningful insights in your data, transforming it into structured inputs that drive model performance. This book provides a deep dive into creating and refining features tailored to your data's unique challenges, ensuring models are both accurate and insightful. What You'll Discover Inside Feature Engineering for Modern Machine Learning with Scikit-Learn covers every stage of advanced feature engineering, from foundational transformations to automated pipelines and cutting-edge tools: Automating Data Preparation with Scikit-Learn Pipelines: Learn to create reproducible, automated workflows that handle everything from scaling and encoding to feature selection. Advanced Feature Creation and Transformation: Master complex techniques like polynomial features, interaction terms, and dimensionality reduction, all designed to improve model accuracy. Industry-Specific Case Studies: Apply feature engineering techniques to real-world domains like healthcare, retail, and customer segmentation, gaining insights into how feature engineering adapts across fields. Modern Tools and Automation with AutoML: Explore AutoML tools like TPOT and Auto-sklearn to automate feature selection and model optimization, allowing you to focus on the highest-impact features. Deep Learning Feature Engineering: Discover techniques tailored for neural networks, including data augmentation, embeddings, and feature transformations that enhance deep learning workflows. Who Should Read This Book Whether you're an experienced data scientist or an advanced beginner looking to build cutting-edge skills, this book provides essential techniques for modern machine learning. It's ideal for anyone who wants to: Maximize model performance through impactful feature engineering. Build efficient, reproducible workflows with Scikit-Learn. Explore advanced applications across multiple domains. Elevate Your Models with Advanced Feature Engineering Feature Engineering for Modern Machine Learning with Scikit-Learn is more than just a guide-it's a toolkit for creating the data transformations that drive high-performing models. Equip yourself with the latest techniques, tools, and insights to confidently tackle real-world data science challenges and unlock the full potential of your machine learning projects. Dive into the world of feature engineering and elevate your data science expertise today!
Feature Engineering For Modern Machine Learning With Scikit Learn
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Author : Cuantum Technologies LLC
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-01-23
Feature Engineering For Modern Machine Learning With Scikit Learn written by Cuantum Technologies LLC 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 2025-01-23 with Computers categories.
Master feature engineering with Scikit-Learn! Learn to preprocess, transform, and automate data for machine learning. Boost predictive accuracy with pipelines, clustering, and advanced techniques for real-world projects. Key Features Comprehensive guide to feature engineering for Scikit-Learn Hands-on projects for real-world applications Focus on automation, pipelines, and deep learning integration Book DescriptionFeature engineering is essential for building robust predictive models. This book delves into practical techniques for transforming raw data into powerful features using Scikit-Learn. You'll explore automation, deep learning integrations, and advanced topics like feature selection and model evaluation. Learn to handle real-world data challenges, enhance accuracy, and streamline your workflows. Through hands-on projects, readers will gain practical experience with techniques such as clustering, pipelines, and feature selection, applied to domains like retail and healthcare. Step-by-step instructions ensure a comprehensive learning journey, from foundational concepts to advanced automation and hybrid modeling approaches. By combining theory with real-world applications, the book equips data professionals with the tools to unlock the full potential of machine learning models. Whether working with structured datasets or integrating deep learning features, this guide provides actionable insights to tackle any data transformation challenge effectively.What you will learn Create data-driven features for better ML models Apply Scikit-Learn pipelines for automation Use clustering and feature selection effectively Handle imbalanced datasets with advanced techniques Leverage regularization for feature selection Utilize deep learning for feature extraction Who this book is for Data scientists, machine learning engineers, and analytics professionals looking to improve predictive model performance will find this book invaluable. Prior experience with Python and basic machine learning concepts is recommended. Familiarity with Scikit-Learn is helpful but not required.
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
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.
Hands On Machine Learning With Scikit Learn Keras And Tensorflow
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Author : Aurélien Géron
language : en
Publisher: O'Reilly Media
Release Date : 2019-09-05
Hands On Machine Learning With Scikit Learn Keras And Tensorflow written by Aurélien Géron and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-05 with Computers categories.
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use Scikit-Learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets
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.
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.
Python And R For The Modern Data Scientist
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Author : Rick J. Scavetta
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-06-22
Python And R For The Modern Data Scientist written by Rick J. Scavetta 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 2021-06-22 with Computers categories.
Success in data science depends on the flexible and appropriate use of tools. That includes Python and R, two of the foundational programming languages in the field. This book guides data scientists from the Python and R communities along the path to becoming bilingual. By recognizing the strengths of both languages, you'll discover new ways to accomplish data science tasks and expand your skill set. Authors Rick Scavetta and Boyan Angelov explain the parallel structures of these languages and highlight where each one excels, whether it's their linguistic features or the powers of their open source ecosystems. You'll learn how to use Python and R together in real-world settings and broaden your job opportunities as a bilingual data scientist. Learn Python and R from the perspective of your current language Understand the strengths and weaknesses of each language Identify use cases where one language is better suited than the other Understand the modern open source ecosystem available for both, including packages, frameworks, and workflows Learn how to integrate R and Python in a single workflow Follow a case study that demonstrates ways to use these languages together
Applied Machine Learning With Scikit Learn
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Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-06-20
Applied Machine Learning With Scikit Learn written by Richard Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-20 with Computers categories.
"Applied Machine Learning with Scikit-learn" "Applied Machine Learning with Scikit-learn" is a comprehensive and in-depth guide that empowers readers to build robust machine learning solutions using the popular Scikit-learn library. The book navigates through the complete lifecycle of machine learning projects, starting from the foundational architecture and integration of Scikit-learn within the broader PyData ecosystem, to advanced data preparation, feature engineering, and the design of custom components. Readers benefit from best practices in scalability, reproducibility, and extensibility, while gaining insights into contributing to and extending the library to suit cutting-edge applications. A core strength of this book is its rigorous treatment of both supervised and unsupervised learning techniques. It offers advanced coverage on classification and regression models—including linear methods, ensemble approaches, support vector machines, and probabilistic classifiers—while addressing practical challenges like imbalanced data, custom scoring, and evaluation strategies. The unsupervised learning chapters explore clustering, dimensionality reduction, density estimation, and feature discovery, complete with methodologies for model selection, validation, and interpretation. Specialized sections on experiment tracking, hyperparameter tuning, and prevention of data leakage ensure that readers can conduct reliable analyses in research or production settings. Recognizing the growing importance of model deployment, monitoring, and integration, the book dedicates ample attention to scaling workflows, building production-grade APIs, automating model retraining, and complying with security and privacy standards. Advanced topics guide practitioners through contemporary machine learning frontiers—such as AutoML, hybrid deep learning integration, time series analysis, weakly supervised learning, and graph-based models. By merging practical implementation advice with a deep understanding of the underlying principles, "Applied Machine Learning with Scikit-learn" serves as an invaluable reference for data scientists, engineers, and researchers striving to leverage the full potential of Scikit-learn in modern machine learning endeavors.
High Performance Algorithmic Trading Using Machine Learning
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Author : Franck Bardol
language : en
Publisher: BPB Publications
Release Date : 2025-06-30
High Performance Algorithmic Trading Using Machine Learning written by Franck Bardol and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-30 with Computers categories.
DESCRIPTION Machine learning is not just an advantage; it is becoming standard practice among top-performing trading firms. As traditional strategies struggle to navigate noise, complexity, and speed, ML-powered systems extract alpha by identifying transient patterns beyond human reach. This shift is transforming how hedge funds, quant teams, and algorithmic platforms operate, and now, these same capabilities are available to advanced practitioners. This book is a practitioner’s blueprint for building production-grade ML trading systems from scratch. It goes far beyond basic return-sign classification tasks, which often fail in live markets, and delivers field-tested techniques used inside elite quant desks. It covers everything from the fundamentals of systematic trading and ML's role in detecting patterns to data preparation, backtesting, and model lifecycle management using Python libraries. You will learn to implement supervised learning for advanced feature engineering and sophisticated ML models. You will also learn to use unsupervised learning for pattern detection, apply ultra-fast pattern matching to chartist strategies, and extract crucial trading signals from unstructured news and financial reports. Finally, you will be able to implement anomaly detection and association rules for comprehensive insights. By the end of this book, you will be ready to design, test, and deploy intelligent trading strategies to institutional standards. WHAT YOU WILL LEARN ● Build end-to-end machine learning pipelines for trading systems. ● Apply unsupervised learning to detect anomalies and regime shifts. ● Extract alpha signals from financial text using modern NLP. ● Use AutoML to optimize features, models, and parameters. ● Design fast pattern detectors from signal processing techniques. ● Backtest event-driven strategies using professional-grade tools. ● Interpret ML results with clear visualizations and plots. WHO THIS BOOK IS FOR This book is for robo traders, algorithmic traders, hedge fund managers, portfolio managers, Python developers, engineers, and analysts who want to understand, master, and integrate machine learning into trading strategies. Readers should understand basic automated trading concepts and have some beginner experience writing Python code. TABLE OF CONTENTS 1. Algorithmic Trading and Machine Learning in a Nutshell 2. Data Feed, Backtests, and Forward Testing 3. Optimizing Trading Systems, Metrics, and Automated Reporting 4. Implement Trading Strategies 5. Supervised Learning for Trading Systems 6. Improving Model Capability with Features 7. Advanced Machine Learning Models for Trading 8. AutoML and Low-Code for Trading Strategies 9. Unsupervised Learning Methods for Trading 10. Unsupervised Learning with Pattern Matching 11. Trading Signals from Reports and News 12. Advanced Unsupervised Learning, Anomaly Detection, and Association Rules Appendix: APIs and Libraries for each chapter