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Machine Learning Hero: Master Data Science with Python Essentials: Machine Learning with Python Hands-On Guide from Beginner to Expert
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Become a Machine Learning Hero and Master Data Science with PythonIn a world driven by data, mastering machine learning is your key to unlocking new opportunities and solving complex problems. Whether you're a beginner or a professional looking to sharpen your skills, Machine Learning Master Data Science with Python Essentials is your guide to becoming proficient in machine learning and data science.
This book provides a step-by-step journey into data science using Python, giving you the tools and knowledge to solve real-world problems confidently.
What You Will LearnMaster Python for Data Science
Learn how to use Python and essential data science libraries such as NumPy, Pandas, Matplotlib, Seaborn, and Scikit-learn to manipulate, visualize, and analyze data efficiently.
Prepare Data for Machine Learning
Explore techniques for cleaning, transforming, and preparing raw data, including handling missing values, feature scaling, and encoding categorical variables to ensure your models are accurate and reliable.
Build Classical Machine Learning Models
Understand and implement popular machine learning algorithms such
Linear and Polynomial Regression for predicting continuous outcomes.Classification algorithms like Support Vector Machines (SVMs), K-Nearest Neighbors (KNN), and Decision Trees to categorize data.K-Means Clustering for discovering patterns in data without labels.
Master Feature Engineering
Learn the art of feature engineering, transforming raw data into features that improve model performance. Feature engineering is key to building effective machine learning models that produce meaningful results.
Hands-On Projects to Solidify LearningPut theory into practice with hands-on projects designed to help you apply machine learning techniques in real-world
Predict Car Prices: Use linear regression to estimate car prices based on factors like mileage, year, and make.Customer Segmentation: Use K-Means clustering to group customers by behavior, helping businesses understand their clients better.Classification Models: Apply classification algorithms to predict survival on the Titanic, using precision, recall, and AUC-ROC metrics to evaluate performance.
These projects reinforce the key concepts and ensure you gain the practical experience needed to tackle real-world data challenges.
Who Is This Book For?Aspiring data scientists wanting to build a solid foundation in machine learning.Python developers looking to expand their skillset into data science.Professionals seeking to use machine learning to solve problems and make data-driven decisions.
✅ 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...
Become a Machine Learning Hero and Master Data Science with PythonIn a world driven by data, mastering machine learning is your key to unlocking new opportunities and solving complex problems. Whether you're a beginner or a professional looking to sharpen your skills, Machine Learning Master Data Science with Python Essentials is your guide to becoming proficient in machine learning and data science.
This book provides a step-by-step journey into data science using Python, giving you the tools and knowledge to solve real-world problems confidently.
What You Will LearnMaster Python for Data Science
Learn how to use Python and essential data science libraries such as NumPy, Pandas, Matplotlib, Seaborn, and Scikit-learn to manipulate, visualize, and analyze data efficiently.
Prepare Data for Machine Learning
Explore techniques for cleaning, transforming, and preparing raw data, including handling missing values, feature scaling, and encoding categorical variables to ensure your models are accurate and reliable.
Build Classical Machine Learning Models
Understand and implement popular machine learning algorithms such
Linear and Polynomial Regression for predicting continuous outcomes.Classification algorithms like Support Vector Machines (SVMs), K-Nearest Neighbors (KNN), and Decision Trees to categorize data.K-Means Clustering for discovering patterns in data without labels.
Master Feature Engineering
Learn the art of feature engineering, transforming raw data into features that improve model performance. Feature engineering is key to building effective machine learning models that produce meaningful results.
Hands-On Projects to Solidify LearningPut theory into practice with hands-on projects designed to help you apply machine learning techniques in real-world
Predict Car Prices: Use linear regression to estimate car prices based on factors like mileage, year, and make.Customer Segmentation: Use K-Means clustering to group customers by behavior, helping businesses understand their clients better.Classification Models: Apply classification algorithms to predict survival on the Titanic, using precision, recall, and AUC-ROC metrics to evaluate performance.
These projects reinforce the key concepts and ensure you gain the practical experience needed to tackle real-world data challenges.
Who Is This Book For?Aspiring data scientists wanting to build a solid foundation in machine learning.Python developers looking to expand their skillset into data science.Professionals seeking to use machine learning to solve problems and make data-driven decisions.
984 pages, Kindle Edition
Published October 6, 2024
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