Applied Machine Learning: A Practical Guide to Preparing Data,...

Description

Applied Machine Learning: A Practical Guide to Preparing Data, Selecting Algorithms, and Implementing Machine Learning Models in the Real World.

Transform machine learning concepts into practical business solutions with this step-by-step guide to building, evaluating, and deploying real-world predictive models.

Rather than focusing solely on theory, this hands-on book walks you through the complete machine learning lifecycle using realistic datasets and practical business scenarios. From preparing data and selecting algorithms to deploying models and monitoring their long-term performance, you’ll gain the skills needed to create machine learning solutions that deliver measurable results.

With downloadable sample code and detailed examples, you’ll work through multiple end-to-end projects while learning how to apply industry-standard tools and workflows. Whether you’re solving classification, prediction, or clustering problems, this guide helps you confidently move from raw data to production-ready models.

Inside You’ll Learn

Getting Started with Machine Learning

Set up your development environment using popular tools such as GitHub and Anaconda, and build a solid foundation for creating machine learning applications.

Data Exploration and Preparation

Discover how to collect, understand, and prepare data for modeling. Learn techniques for data visualization, statistical analysis, correlation discovery, feature engineering, handling missing values, and improving data quality before training begins.

Choosing the Right Model

Understand how to select algorithms that best fit your business objectives. Explore widely used machine learning techniques including regression, decision trees, random forests, gradient boosting, clustering, ensemble learning, and other predictive modeling approaches.

Model Evaluation and Improvement

Measure model performance using appropriate validation metrics, improve interpretability, reduce overfitting, and refine your models through iterative experimentation, feature optimization, and data enhancements.

Deployment and Performance Monitoring

Move beyond model development by learning how to integrate machine learning into real-world applications. Monitor prediction quality over time, track business impact, identify performance drift, and maintain reliable models in production environments.

Whether you’re a data analyst, developer, business professional, or aspiring machine learning engineer, this book provides a practical roadmap for applying machine learning to real business challenges—from your first dataset to long-term production success.