Hands-on Scikit-Learn for Machine Learning Applications : Data Science Fundamentals with Python

Aspiring data science professionals can learn the Scikit-Learn library along with the fundamentals of machine learning with this book. The book combines the Anaconda Python distribution with the popular Scikit-Learn library to demonstrate a wide range of supervised and unsupervised machine learning algorithms. Care is taken to walk you through the principles of machine learning through clear examples written in Python that you can try out and experiment with at home on your own machine. All applied math and programming skills required to master the content are covered in this book. In-depth knowledge of object-oriented programming is not required as working and complete examples are provided and explained. Coding examples are in-depth and complex when necessary. They are also concise, accurate, and complete, and complement the machine learning concepts introduced. Working the examples helps to build the skills necessary to understand and apply complex machine learning algorithms. Hands-on Scikit-Learn for Machine Learning Applications is an excellent starting point for those pursuing a career in machine learning. Students of this book will learn the fundamentals that are a prerequisite to competency. Readers will be exposed to the Anaconda distribution of Python that is designed specifically for data science professionals, and will build skills in the popular Scikit-Learn library that underlies many machine learning applications in the world of Python. What You'll Learn • Work with simple and complex datasets common to Scikit-Learn • Manipulate data into vectors and matrices for algorithmic processing • Become familiar with the Anaconda distribution used in data science • Apply machine learning with Classifiers, Regressors, and Dimensionality Reduction • Tune algorithms and find the best algorithms for each dataset • Load data from and save to CSV, JSON, Numpy, and Pandas formats Who This Book Is For The aspiring data scientist yearning to break into machine learning through mastering the underlying fundamentals that are sometimes skipped over in the rush to be productive. Some knowledge of object-oriented programming and very basic applied linear algebra will make learning easier, although anyone can benefit from this book. Front Matter ....Pages i-xiii Introduction to Scikit-Learn (David Paper)....Pages 1-35 Classification from Simple Training Sets (David Paper)....Pages 37-69 Classification from Complex Training Sets (David Paper)....Pages 71-104 Predictive Modeling Through Regression (David Paper)....Pages 105-136 Scikit-Learn Classifier Tuning from Simple Training Sets (David Paper)....Pages 137-163 Scikit-Learn Classifier Tuning from Complex Training Sets (David Paper)....Pages 165-188 Scikit-Learn Regression Tuning (David Paper)....Pages 189-213 Putting It All Together (David Paper)....Pages 215-237 Back Matter ....Pages 239-242 Trademarked names, logos, and images may appear in this book. Rather than use a trademark symbol with every occurrence of a trademarked name, logo, or image we use the names, logos, and images only in an editorial fashion and to the benefit of the trademark owner, with no intention of infringement of the trademark.