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Ultimate Machine Learning with Scikit-Learn: Unleash the Power of Scikit-Learn and Python to Build Cutting-Edge Predictive Modeling Applications and
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- Master the Art of Data Munging and Predictive Modeling for Machine Learning with Scikit-LearnStep into the future of data science with machine learning with Scikit-Learn for Python developers. This book is your practical, accessible guide to building machine learning pipelines for advanced analytics and applying predictive modeling applications using open-source tools in real-world scenarios.Book Description“Ultimate Machine Learning with Scikit-Learn” is a definitive resource that offers an in-depth exploration of data preparation, modeling techniques, and the theoretical foundations behind powerful machine learning algorithms using Python and Scikit-Learn.Beginning with foundational techniques, you'll dive into essential skills for effective data preprocessing, setting the stage for robust analysis. Next, logistic regression and decision trees equip you with the tools to delve deeper into predictive modeling, ensuring a solid understanding of fundamental methodologies. You will master time series data analysis, followed by effective strategies for handling unstructured data using techniques like Naive Bayes.Transitioning into real-time data streams, you'll discover dynamic approaches with K-nearest neighbors for high-dimensional data analysis with Support Vector Machines(SVMs). Alongside, you will learn to safeguard your analyses against anomalies with isolation forests and harness the predictive power of ensemble methods, in the domain of stock market data analysis.By the end of the book you will master the art of data engineering and ML pipelines, ensuring you're equipped to tackle even the most complex analytics tasks with confidence.What You’ll Learn Inside:Comprehensive coverage of Scikit-Learn machine learning and Python data science essentialsStep-by-step guides for regression analysis best practices and logistic regression and decision treesHow to handle unstructured data with Naive Bayes classifiers and advanced ensemble methodsTechniques for time series data analysis and high-dimensional data analysis with support vector machinesPractical advice on real-time data streams management, anomaly detection, and advanced Python analyticsExplanation of ML regression analysis, decision trees tutorial, and ensemble methods introduction for stock market and business insightsTips for data preprocessing, building machine learning pipelines.Who Is This Book For?Developers, students, and business analysts seeking an in-depth ML algorithms theory and application explainedProfessionals aiming to master Python data science book and apply machine learning solutions to time series modeling and anomaly detectionAnyone interested in data engineering handbook for practical, scalable insights in advanced analyticsWhy Choose This Guide?Natural, easy-to-follow explanations and code samples in Python using Scikit-LearnTotal coverage of machine learning pipelines, isolation forest techniques, and decision tree strategiesPolicy-compliant, original, and free from unauthorized names or brandsUnlock the power of machine learning in Python! Table of Contents1. Data Preprocessing with Linear Regression2. Structured Data and Logistic Regression3. Time-Series Data and Decision Trees4. Unstructured Data Handling and Naive Bayes5. Real-time Data Streams and K-Nearest Neighbors6. Sparse Distributed Data and Support Vector Machines7. Anomaly Detection and Isolation Forests8. Stock Market Data and Ensemble Methods9. Data Engineering and ML Pipelines for Advanced AnalyticsIndex
| Publisher | Orange Education Pvt. Ltd |
| Publication date | 6 May 2024 |
| Language | English |
| Print length | 395 pages |
| ISBN-10 | 8197223947 |
| ISBN-13 | 978-8197223945 |
| Dimensions | 19.05 x 2.26 x 23.5 cm |
| Book 2 of 2 | Machine Learning Engineer (ML) — Advanced Path |
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