Product Description Unlock powerful machine learning techniques and solve any machine learning problem you come across with Python Key Features Understand the key frameworks in data science, machine learning, and deep learning Leverage scikit-learn and TensorFlow to fully explore the machine learning ecosystem Apply machine learning and deep learning algorithms to challenging real-world datasets Book Description Machine learning is becoming more and more transformational to businesses every passing day. Machine Learning with TensorFlow and scikit-learn offers you the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis using the latest cutting-edge tools. You'll begin this Learning Path by working on techniques that you need to create and contribute to the field of machine learning. Coverage of the TensorFlow deep learning library and the scikit-learn code are included, as they are two of the most popular frameworks used in machine learning. This Learning Path is loaded with several examples that show you how to leverage the open source Python libraries to create machine learning models that easily solve your everyday tasks and problems. You'll learn how to use complex Scikit-learn features and the TensorFlow computing library for intensive computation, digging deeper to gain more insights into your data than ever before. You will explore topics right from mathematical operations to implementing various supervised, unsupervised, and deep learning algorithms with scikit-learn. By the end of this Learning Path, you'll be equipped with tools that will help you maximize the potential of machine learning. This Learning Path includes content from the following Packt products: Python Machine Learning, Second Edition by Sebastian Raschka, Vahid Mirjalili TensorFlow Machine Learning Cookbook, Second Edition by Nick McClure Scikit-learn Cookbook, Second Edition by Julian Avila, Trent Hauck What you will learn Create a deep neural network using TensorFlow Uncover hidden patterns and structures in data with clustering Get to grips with the linear regression techniques with TensorFlow Implement neural networks and improve predictions Apply NLP and sentiment analysis to your data Use distance metrics to predict clustering Create your own estimator with the simple syntax of scikit-learn Explore the feed-forward neural networks available in scikit-learn Who This Book Is For Machine Learning with TensorFlow and scikit-learn is for developers, data scientists, and machine learning enthusiasts who want to learn the principles of machine learning and effectively use it with TensorFlow and scikit-learn in their everyday lives. Prior knowledge of Python is assumed. Basic knowledge of high school math and statistics will be beneficial. About the Author Sebastian Raschka, author of the bestselling book, Python Machine Learning, has many years of experience with coding in Python, and he has given several seminars on the practical applications of data science, machine learning, and deep learning, including a machine learning tutorial at SciPy - the leading conference for scientific computing in Python. While Sebastian's academic research projects are mainly centered around problem-solving in computational biology, he loves to write and talk about data science, machine learning, and Python in general, and he is motivated to help people develop data-driven solutions without necessarily requiring a machine learning background. His work and contributions have recently been recognized by the departmental outstanding graduate student award 2016-2017, as well as the ACM Computing Reviews' Best of 2016 award. In his free time, Sebastian loves to contribute to open source projects, and the methods that he has implemented are now successfully used in machine learning competitions, such as Kaggle. Vahid Mirjalili obtained his PhD in mechanical engineering working on novel methods for lar