Ultimate Machine Learning With Scikit Learn Unleash The Power Of Scikit Learn And Python To Build Cutting Edge Predictive Modeling Applications And Unlock Deeper Insights Into Machine Learning
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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 Unlock Deeper Insights Into Machine Learning
Author: Parag Saxena
language: en
Publisher: Orange Education Pvt Limited
Release Date: 2024-05-04
Master the Art of Data Munging and Predictive Modeling for Machine Learning with Scikit-Learn Key Features● Comprehensive coverage of complete predictive modeling lifecycle, from data munging to deployment ● Gain insights into the theoretical foundations underlying powerful machine learning algorithms ● Master Python's versatile Scikit-Learn library for robust data analysis 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 will learn● Master fundamental data preprocessing techniques tailored for both structured and unstructured data ● Develop predictive models utilizing a spectrum of methods including regression, classification, and clustering ● Tackle intricate data challenges by employing Support Vector Machines (SVMs), decision trees, and ensemble learning approaches ● Implement advanced anomaly detection methodologies and explore emerging techniques like neural networks ● Build efficient data pipelines optimized for handling big data and streaming analytics ● Solidify core machine learning principles through practical examples and illustrations Table of Contents1. Data Preprocessing with Linear Regression 2. Structured Data and Logistic Regression 3. Time-Series Data and Decision Trees 4. Unstructured Data Handling and Naive Bayes 5. Real-time Data Streams and K-Nearest Neighbors 6. Sparse Distributed Data and Support Vector Machines 7. Anomaly Detection and Isolation Forests 8. Stock Market Data and Ensemble Methods 9. Data Engineering and ML Pipelines for Advanced Analytics Index
Ultimate Machine Learning with Scikit-Learn
Author: Parag Saxena
language: en
Publisher: Orange Education Pvt Ltd
Release Date: 2024-05-06
TAGLINE Master the Art of Data Munging and Predictive Modeling for Machine Learning with Scikit-Learn KEY FEATURES ● Comprehensive coverage of complete predictive modeling lifecycle, from data munging to deployment ● Gain insights into the theoretical foundations underlying powerful machine learning algorithms ● Master Python's versatile Scikit-Learn library for robust data analysis 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 WILL YOU LEARN ● Master fundamental data preprocessing techniques tailored for both structured and unstructured data ● Develop predictive models utilizing a spectrum of methods including regression, classification, and clustering ● Tackle intricate data challenges by employing Support Vector Machines (SVMs), decision trees, and ensemble learning approaches ● Implement advanced anomaly detection methodologies and explore emerging techniques like neural networks ● Build efficient data pipelines optimized for handling big data and streaming analytics ● Solidify core machine learning principles through practical examples and illustrations WHO IS THIS BOOK FOR? This book is tailored for experienced and aspiring data scientists, machine learning engineers, and AI practitioners aiming to enhance their skills and create impactful solutions using Python and Scikit-Learn. Prior experience with Python and machine learning fundamentals is recommended. TABLE OF CONTENTS 1. Data Preprocessing with Linear Regression 2. Structured Data and Logistic Regression 3. Time-Series Data and Decision Trees 4. Unstructured Data Handling and Naive Bayes 5. Real-time Data Streams and K-Nearest Neighbors 6. Sparse Distributed Data and Support Vector Machines 7. Anomaly Detection and Isolation Forests 8. Stock Market Data and Ensemble Methods 9. Data Engineering and ML Pipelines for Advanced Analytics Index
Python Machine Learning
Author: Sebastian Raschka
language: en
Publisher: Packt Publishing Ltd
Release Date: 2015-09-23
Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is For If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will Learn Explore how to use different machine learning models to ask different questions of your data Learn how to build neural networks using Keras and Theano Find out how to write clean and elegant Python code that will optimize the strength of your algorithms Discover how to embed your machine learning model in a web application for increased accessibility Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Organize data using effective pre-processing techniques Get to grips with sentiment analysis to delve deeper into textual and social media data In Detail Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. Style and approach Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.