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.
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.
Contemporary Machine Learning Methods Harnessing Scikit Learn And Tensorflow
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Author : Adam Jones
language : en
Publisher: Walzone Press
Release Date : 2025-01-03
Contemporary Machine Learning Methods Harnessing Scikit Learn And Tensorflow written by Adam Jones and has been published by Walzone Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-03 with Computers categories.
"Contemporary Machine Learning Methods: Harnessing Scikit-Learn and TensorFlow" is an indispensable resource for data scientists and machine learning practitioners eager to sharpen their skills and stay at the forefront of technology. This book offers a comprehensive exploration of modern machine learning methodologies, encompassing innovative regression and classification techniques, along with complex neural network architectures using TensorFlow. Explore practical implementations and real-world examples that demystify intricate concepts like unsupervised learning, deep learning optimizations, natural language processing, and feature engineering with clarity. Each chapter serves as a step-by-step guide to applying these contemporary methods, complete with code samples and thorough explanations. Whether you're a professional aiming to deploy machine learning solutions at an enterprise level, an academic researcher investigating computational innovations, or a postgraduate student interested in cutting-edge AI, this book equips you with the insights, tools, and expertise needed to effectively leverage machine learning technologies. Master the nuances of machine learning with "Contemporary Machine Learning Methods: Harnessing Scikit-Learn and TensorFlow" and convert data into impactful knowledge.
Mastering Machine Learning With Scikit Learn
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Author : Dr Benjamin Neudorf
language : en
Publisher: Independently Published
Release Date : 2025-08-22
Mastering Machine Learning With Scikit Learn written by Dr Benjamin Neudorf and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-22 with Computers categories.
Feeling overwhelmed by the idea of machine learning? Worried that coding or data science is just "too advanced" for you? You're not alone-and this book is your perfect starting point. Mastering Machine Learning with Scikit-Learn welcomes absolute beginners, guiding you gently from first steps to real-world results, no prior experience required. A Friendly Pathway to Modern Machine Learning If you've ever stared at lines of code and felt lost in jargon, you'll find a supportive companion here. Dr. Benjamin Neudorf draws on personal experience and a passion for teaching, transforming intimidating topics into simple, manageable lessons. You'll be gently introduced to machine learning and the powerful Scikit-Learn library, one of the most trusted tools in Python data science. What You'll Gain: Step-by-Step Confidence: Every chapter breaks big concepts into small, achievable actions, so you'll never feel stuck or left behind. Hands-On Projects: Build real machine learning models using practical examples, classic datasets, and clear explanations that demystify the process. Beginner-Friendly Explanations: No complex math or background needed-just curiosity and the willingness to learn at your own pace. Troubleshooting Support: Benefit from practical tips, quick references, and reassuring advice to help you overcome common challenges and celebrate progress. Real-World Skills: Learn how to prepare and clean data, choose and evaluate algorithms, interpret results, and build projects you'll be proud to share. Key Takeaways Include: Setting up your Python environment and installing essential tools with ease Understanding the core machine learning workflow: from raw data to working model Mastering data preparation, feature engineering, and encoding techniques Building and tuning supervised and unsupervised models (regression, classification, clustering) Evaluating and improving your models with industry-standard metrics and best practices Exploring ethical ML, avoiding common pitfalls, and growing your data science skills step by step Why This Book? Mistakes are part of the journey, and every small win is worth celebrating. This book normalizes learning curves, encourages experimentation, and helps you develop the confidence to ask questions and try new things. You'll finish not just knowing "what to do," but "why" it matters, and how to keep learning beyond these pages. Ready to unlock your potential? Start your empowering coding adventure today-discover just how approachable, practical, and even fun machine learning can be. Your journey into data science begins here, with a mentor who believes in you every step of the way.
Feature Engineering Bookcamp
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Author : Sinan Ozdemir
language : en
Publisher: Simon and Schuster
Release Date : 2022-10-04
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 2022-10-04 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. 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.
Quantitative Asset Management Factor Investing And Machine Learning For Institutional Investing
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Author : Michael Robbins
language : en
Publisher: McGraw Hill Professional
Release Date : 2023-06-24
Quantitative Asset Management Factor Investing And Machine Learning For Institutional Investing written by Michael Robbins 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 2023-06-24 with Business & Economics categories.
Augment your asset allocation strategy with machine learning and factor investing for unprecedented returns and growth Whether you’re managing institutional portfolios or private wealth, Quantitative Asset Management will open your eyes to a new, more successful way of investing—one that harnesses the power of big data and artificial intelligence. This innovative guide walks you through everything you need to know to fully leverage these revolutionary tools. Written from the perspective of a seasoned financial investor making use of technology, it details proven investing methods, striking a rare balance between providing important technical information without burdening you with overly complex investing theory. Quantitative Asset Management is organized into four thematic sections: Part I reveals invaluable lessons for planning and governance of investment decision-making. Part 2 discusses quantitative financial modeling, covering important topics like overfitting, mitigating unrealistic assumptions, managing substitutions, enhancing minority classes, and missing data imputation. Part 3 shows how to develop a strategy into an investment product, including the alpha models, risk models, implementation, backtesting, and cost optimization. Part 4 explains how to measure performance, learn from mistakes, manage risk, and survive financial tragedies. With Quantitative Asset Management, you have everything you need to build your awareness of other markets, ask the right questions and answer them effectively, and drive steady profits even through times of great uncertainty.
Machine Learning With Scikit Learn Livelessons
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Author : David Mertz
language : en
Publisher:
Release Date : 2019
Machine Learning With Scikit Learn Livelessons written by David Mertz 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.
6+ Hours of Video Instruction Learn the main concepts and techniques used in modern machine learning through numerous examples written in scikit-learn Overview Machine Learning with scikit-learn LiveLessons is your guide to the scikit-learn library, which provides a wide range of algorithms in machine learning that are unified under a common and intuitive Python API. Most of the dozens of classes provided for various kinds of models share the large majority of the same calling interface. Quite often you can easily substitute one algorithm for another with very little or no change in your underlying code. This enables you to explore the problem space quickly and often to arrive at an optimal'Äìor at least satisficing'Äìapproach to your problem domain or datasets. The scikit-learn library is built on the foundations of the numeric Python stack. It uses NumPy for its fundamental data structures and optimized performance, and it plays well with pandas and matplotlib. It is free software under a BSD license. The great bulk of machine learning programming in Python is done with scikit-learn'Äîat least outside the specialized domain of deep neural networks. About the Instructor David Mertz has been involved with the Python community for 20 years, with data science, (under various previous names) and with machine learning since way back when it was more likely to be called 'Äúartificial intelligence.'Äù He was a director of the Python Software Foundation for six years and continues to serve on, or chair, a variety of PSF working groups. He has also written quite a bit about Python: the column Charming Python for IBM developerWorks, for many years; Text Processing in Python (Addison-Wesley, 2003); and two short books for O'ÄôReilly. He created the data science training program for Anaconda, Inc., and was a senior trainer for them. Skill Level Intermediate Learn How To Use various machine learning techniques Explore a dataset Perform various types of classification Use regression, clustering, and hyperparameters Use feature engineering and feature selection Implement data pipelines Develop robust train/test splits Who Should Take This Course Programmers and statisticians interested in using Python and the scikit-learn library to implement machine learning Course Requirements Programming experience Table of Contents Introduction Lesson 1: What Is Machine Learning? Lesson 2: Exploring a Dataset Lesson 3: Classification Lesson 4: Regression Less...
Data Preparation For Machine Learning
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Author : Jason Brownlee
language : en
Publisher: Machine Learning Mastery
Release Date : 2020-06-30
Data Preparation For Machine Learning written by Jason Brownlee and has been published by Machine Learning Mastery this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-30 with Computers categories.
Data preparation involves transforming raw data in to a form that can be modeled using machine learning algorithms. Cut through the equations, Greek letters, and confusion, and discover the specialized data preparation techniques that you need to know to get the most out of your data on your next project. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover how to confidently and effectively prepare your data for predictive modeling with machine learning.
Python For Machine Learning
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Author : Thompson Carter
language : en
Publisher: Independently Published
Release Date : 2024-12-14
Python For Machine Learning written by Thompson Carter and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-14 with Computers categories.
Unlock the power of Machine Learning with this comprehensive, hands-on guide that transforms complex ML concepts into practical solutions. Whether you're a data scientist, developer, or ML enthusiast, this book delivers battle-tested strategies for implementing production-ready ML models using Python and scikit-learn. What You'll Master From data preprocessing to model deployment, discover how to build robust ML pipelines that solve real-world problems. Dive deep into classification, regression, clustering, and dimensionality reduction techniques while working with real datasets that matter. Practical Focus No more theoretical jargon - learn through hands-on projects, including sentiment analysis, customer segmentation, and predictive maintenance. Each chapter builds your expertise with industry-standard practices and optimization techniques. Perfect For - Python developers ready to level up their ML skills - Data analysts transitioning to machine learning - Students seeking practical ML implementation skills Key Features Modern Techniques Master the latest scikit-learn features, including pipeline optimization, automated ML workflows, and model evaluation strategies. Learn to fine-tune hyperparameters and build ensemble models that outperform traditional approaches. Real-World Applications Transform raw data into valuable insights using production-ready code. Implement advanced techniques for feature engineering, cross-validation, and model selection that actually work in business environments.