Book Image

Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits

By : Tarek Amr
Book Image

Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits

By: Tarek Amr

Overview of this book

Machine learning is applied everywhere, from business to research and academia, while scikit-learn is a versatile library that is popular among machine learning practitioners. This book serves as a practical guide for anyone looking to provide hands-on machine learning solutions with scikit-learn and Python toolkits. The book begins with an explanation of machine learning concepts and fundamentals, and strikes a balance between theoretical concepts and their applications. Each chapter covers a different set of algorithms, and shows you how to use them to solve real-life problems. You’ll also learn about various key supervised and unsupervised machine learning algorithms using practical examples. Whether it is an instance-based learning algorithm, Bayesian estimation, a deep neural network, a tree-based ensemble, or a recommendation system, you’ll gain a thorough understanding of its theory and learn when to apply it. As you advance, you’ll learn how to deal with unlabeled data and when to use different clustering and anomaly detection algorithms. By the end of this machine learning book, you’ll have learned how to take a data-driven approach to provide end-to-end machine learning solutions. You’ll also have discovered how to formulate the problem at hand, prepare required data, and evaluate and deploy models in production.
Table of Contents (18 chapters)
1
Section 1: Supervised Learning
8
Section 2: Advanced Supervised Learning
13
Section 3: Unsupervised Learning and More

Summary

Personally, I find the field of natural language processing very exciting. The vast majority of our knowledge as humans is contained in books, documents, and web pages. Knowing how to automatically extract this information and organize it with the help of machine learning is essential to our scientific progress and endeavors in automation. This is why multiple scientific fields, such as information retrieval, statistics, and linguistics, borrow ideas from each other and try to solve the same problem from different angles. In this chapter, we also borrowed ideas from all these fields and learned how to represent textual data in formats suitable to machine learning algorithms. We also learned about the utilities that scikit-learn provides to aid in building and optimizing end-to-end solutions. We also encountered concepts such as transfer learning, and we were able to seamlessly incorporate spaCy's language models into scikit-learn.

From the next chapter, we...