Description
Unlock the power of machine learning with 'Practical Machine Learning in R', your essential guide to mastering this transformative technology. Designed for professionals and students alike, this book delves into the intricacies of machine learning, providing hands-on examples using the R programming language. With insightful contributions from bestselling authors Fred Nwanganga and Mike Chapple, you will gain a comprehensive understanding of machine learning's role in artificial intelligence (AI) and how it can enhance decision-making processes through predictive analytics.
In this user-friendly resource, you'll explore key concepts such as data wrangling, classification techniques, and performance evaluation—all tailored for your success in an introductory machine learning course. Each chapter is crafted to be clear and detailed, allowing you to tackle real-world business problems using intelligent algorithms.
Dive into essential data management techniques, including data collection, exploration, and dimensionality reduction. The book also covers powerful unsupervised learning methods—helping you discover and summarize patterns with approaches like apriori, eclat, and clustering. Learn the principles behind vital classification techniques such as Nearest Neighbor, Decision Tree, and Naive Bayes. Additionally, discover how to accurately evaluate and select the best algorithms for your projects.
Whether you're self-taught or enrolled in a formal course, 'Practical Machine Learning in R' is your gateway to harnessing machine learning for innovative business solutions. Join the community of learners and professionals transforming their data into actionable insights today!
In this user-friendly resource, you'll explore key concepts such as data wrangling, classification techniques, and performance evaluation—all tailored for your success in an introductory machine learning course. Each chapter is crafted to be clear and detailed, allowing you to tackle real-world business problems using intelligent algorithms.
Dive into essential data management techniques, including data collection, exploration, and dimensionality reduction. The book also covers powerful unsupervised learning methods—helping you discover and summarize patterns with approaches like apriori, eclat, and clustering. Learn the principles behind vital classification techniques such as Nearest Neighbor, Decision Tree, and Naive Bayes. Additionally, discover how to accurately evaluate and select the best algorithms for your projects.
Whether you're self-taught or enrolled in a formal course, 'Practical Machine Learning in R' is your gateway to harnessing machine learning for innovative business solutions. Join the community of learners and professionals transforming their data into actionable insights today!