Python Machine Learning by Example covers in detail the most important concepts, techniques, algorithms, and libraries that every data scientist or machine learning practitioner needs to know. This example-enriched guide will make your learning journey easier and happier, enabling you to solve real-world data-driven problems.
Über den Autor
Yuxi (Hayden) Liu is an experienced data scientist who's focused on developing machine learning and deep learning models and systems. He has worked in a variety of data-driven domains and has applied his expertise in reinforcement learning to computational. He is an education enthusiast and the author of a series of ML books. His first book, Python Machine Learning By Example, was a #1 bestseller on Amazon India in 2017 and 2018. His other books include R Deep Learning Projects and Hands-On Deep Learning Architectures with Python published by Packt. He also published five first-authored IEEE transaction and conference papers during his master's research at the University of Toronto.
Klappentext
Grasp machine learning concepts, techniques, and algorithms with the help of real-world examples using Python libraries such as TensorFlow and scikit-learnnnKey FeaturesnExploit the power of Python to explore the world of data mining and data analyticsnnDiscover machine learning algorithms to solve complex challenges faced by data scientists todaynnUse Python libraries such as TensorFlow and Keras to create smart cognitive actions for your projectsnnnnBook DeillegalscriptionnnThe surge in interest in machine learning (ML) is due to the fact that it revolutionizes automation by learning patterns in data and using them to make predictions and decisions. If you're interested in ML, this book will serve as your entry point to ML.nnPython Machine Learning By Example begins with an introduction to important ML concepts and implementations using Python libraries. Each chapter of the book walks you through an industry adopted application. You'll implement ML techniques in areas such as exploratory data analysis, feature engineering, and natural language processing (NLP) in a clear and easy-to-follow way.nnWith the help of this extended and updated edition, you'll understand how to tackle data-driven problems and implement your solutions with the powerful yet simple Python language and popular Python packages and tools such as TensorFlow, scikit-learn, gensim, and Keras. To aid your understanding of popular ML algorithms, the book covers interesting and easy-to-follow examples such as news topic modeling and classification, spam email detection, stock price forecasting, and more.nnBy the end of the book, you'll have put together a broad picture of the ML ecosystem and will be well-versed with the best practices of applying ML techniques to make the most out of new opportunities.nnWhat you will learnnUnderstand the important concepts in machine learning and data sciencennUse Python to explore the world of data mining and analyticsnnScale up model training using varied data complexities with Apache SparknnDelve deep into text and NLP using Python libraries such NLTK and gensimnnSelect and build an ML model and evaluate and optimize its performancennImplement ML algorithms from scratch in Python, TensorFlow, and scikit-learnnnnnWho this book is fornnIf you're a machine learning aspirant, data analyst, or data engineer highly passionate about machine learning and want to begin working on ML assignments, this book is for you. Prior knowledge of Python coding is assumed and basic familiarity with statistical concepts will be beneficial although not necessary.
