附註:Includes bibliographical references and index.
Chapter 1. An exploration of Python libraries in machine learning models for data science -- Chapter 2. Interdisciplinary application of machine learning, data science, and Python for cricket analytics -- Chapter 3. Application of machine learning for disabled persons -- Chapter 4. Performing facial recognition using ensemble learning -- Chapter 5. Advanced data-driven approaches for intelligent olfaction -- Chapter 6. Just quit: a modern way to quit smoking -- Chapter 7. Naive bayes classification for email spam detection -- Chapter 8. Using SVM and CNN as image classifiers for brain tumor dataset -- Chapter 9. Amazon product dataset community detection metrics and algorithms -- Chapter 10. Python libraries implementation for brain tumor detection using MR images using machine learning models -- Chapter 11. Predicting the severity of future earthquakes by employing the random forest algorithm.
摘要:"The world is approaching a point where big data will start to play a beneficial role in many industries and organizations. Today, analyzing data for new insights has become an everyday norm, increasing the need for data analysts touse efficient and appropriate tools to provide quick and valuable results to clients. Existing research in the field currently lacks a full coverage of all essential algorithms, leaving a knowledge void for practical implementation and codein Python with all needed libraries and links to datasets used. Advanced interdisciplinary applications of machine learning Python Libraries for data science serves as a one-stop book to help emerging data scientists gain hands-on skills needed through real-world data and completely up-to-date Python code. It covers all the technical details, from installing the needed software to importing libraries and using the latest data sets; deciding on the right model; training, testing, and evaluating the model; and including NumPy, Pandas, and matplotlib. With coverage on various machine learning algorithms like regression, linear and logical regression, classification, support vector machine (SVM), clustering, K-nearest neighbor, market basket analysis, Apriori, K-means clustering, and visualization using Seaborne, it is designed for academic researchers, undergraduate students, postgraduate students, executive education program leaders, and practitioners."--
