Python Machine Learning Blueprints

**A project-based approach to mastering machine learning concepts by applying them to everyday problems using Python ecosystem** Key Features Get to grips with Python's Machine Learning libraries such as scikit-learn, TensorFlow, and Keras Put machine learning key tasks and principles into practice to solve problems Learn to build projects on analytics, computer vision, and neural networks domain Book Description Machine Learning is transforming the way we understand and interact with the world around us. This book will be the perfect guide for you to put your knowledge and skills into practice and use Python ecosystem to cover key domains in machine learning. This second edition is a successful update to our first edition book covering a range of libraries from Python ecosystem including TensorFlow and Keras to implement real-world machine learning projects. This book begins by giving an overview of Python in Machine Learning and helps you in setting up for the adventure it has in store. You will learn to implement the advanced concepts and most used machine learning algorithms in real-world projects using complex datasets and optimized techniques. You will cover projects from different domains such as clustering, predictive analytics, recommendation systems, NLP, classification, SVMs and much more using frameworks such as scikit-learn, TensorFlow, Keras and more. Further, you will also learn to build an advanced chatbot and scale up things using PySpark. Finally, you will dive into deep learning and create an application using Computer Vision and Neural Networks. By the end of this book, you’ll be able to analyze data without any hassle in a way that makes a real impact. This book will open the gates for you to enter into the intricacies of deep learning. What you will learn Understand the Python data science stack and the algorithms in use Apply machine learning techniques to real-world applications Explore the power of Tensorflow and Keras using complex datasets Get up and running with topics like NLP, regression, classification, recommendation systems, and Bayesian techniques Learn to scale up a project using PySpark and build a chatbot Delve into advanced concepts like Computer Vision, Neural Networks and Deep learning Who This Book Is For This book is for machine learning practitioners, data scientists, and deep learning enthusiasts who want to take their machine learning skills to the next level by building real-world projects. Implement libraries from Python ecosystem to build a range of projects addressing various machine learning domains. Knowledge of Python programming language and machine learning concepts are recommended. About the Author **Alexander T. Combs** is an experienced data scientist, strategist, and developer with a background in financial data extraction, natural language processing, and generation, and quantitative and statistical modeling. He currently lives and works in New York City.