Master the essential skills needed to recognize and solve complex problems with machine
learning and deep learning. Using real-world examples that leverage the popular Python machine
learning ecosystem this book is your perfect companion for learning the art and science of
machine learning to become a successful practitioner. The concepts techniques tools
frameworks and methodologies used in this book will teach you how to think design build and
execute machine learning systems and projects successfully.Practical Machine Learning with
Python follows a structured and comprehensive three-tiered approach packed with hands-on
examples and code. Part 1 focuses on understanding machine learning concepts and tools. This
includes machine learning basics with a broad overview of algorithms techniques concepts and
applications followed by a tour of the entire Python machine learning ecosystem. Brief guides
for useful machine learning tools libraries and frameworks are also covered. Part 2 details
standard machine learning pipelines with an emphasis on data processing analysis feature
engineering and modeling. You will learn how to process wrangle summarize and visualize data
in its various forms. Feature engineering and selection methodologies will be covered in detail
with real-world datasets followed by model building tuning interpretation and deployment.
Part 3 explores multiple real-world case studies spanning diverse domains and industries like
retail transportation movies music marketing computer vision and finance. For each case
study you will learn the application of various machine learning techniques and methods. The
hands-on examples will help you become familiar with state-of-the-art machine learning tools
and techniques and understand what algorithms are best suited for any problem. Practical
Machine Learning with Python will empower you to start solving your own problems with machine
learning today!What You'll Learn Execute end-to-end machine learning projects and systems
Implement hands-on examples with industry standard open source robust machine learning tools
and frameworks Review case studies depicting applications of machine learning and deep learning
on diverse domains and industries Apply a wide range of machine learning models including
regression classification and clustering. Understand and apply the latest models and
methodologies from deep learning including CNNs RNNs LSTMs and transfer learning. Who This
Book Is For IT professionals analysts developers data scientists engineers graduate
students
