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Building Machine Learning Systems with Python: Explore machine learning and deep learning techniques for building intelligent systems using scikit-learn and TensorFlow, 3rd Edition - Couverture souple

 
9781788623223: Building Machine Learning Systems with Python: Explore machine learning and deep learning techniques for building intelligent systems using scikit-learn and TensorFlow, 3rd Edition

Synopsis

Build intelligent end-to-end machine learning systems with Python

Key Features

  • Use scikit-learn and TensorFlow to train your machine learning models
  • Implement popular supervised and unsupervised machine learning algorithms in Python
  • Discover best practices for building production-grade machine learning systems from scratch

Book Description

Machine learning enables systems to make predictions based on historical data. Python is one of the most popular languages used to develop machine learning applications, thanks to its extensive library support. This updated third edition of Building Machine Learning Systems with Python helps you get up to speed with the latest trends in artificial intelligence (AI).

With this guide's hands-on approach, you'll learn to build state-of-the-art machine learning models from scratch. Complete with ready-to-implement code and real-world examples, the book starts by introducing the Python ecosystem for machine learning. You'll then learn best practices for preparing data for analysis and later gain insights into implementing supervised and unsupervised machine learning techniques such as classification, regression and clustering. As you progress, you'll understand how to use Python's scikit-learn and TensorFlow libraries to build production-ready and end-to-end machine learning system models, and then fine-tune them for high performance.

By the end of this book, you'll have the skills you need to confidently train and deploy enterprise-grade machine learning models in Python.

What you will learn

  • Build a classification system that can be applied to text, images, and sound
  • Solve regression-related problems using scikit-learn and TensorFlow
  • Recommend products to users based on their previous purchases
  • Explore different methods of applying deep neural networks to your data
  • Understand recent advances in computer vision and natural language processing (NLP)
  • Deploy Amazon Web Services (AWS) to run data models on the cloud

Who this book is for:

This book is for data scientists, machine learning developers, and Python developers who want to learn how to build increasingly complex machine learning systems. Prior knowledge of Python programming is expected.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

À propos des auteurs

Luis Pedro Coelho is a computational biologist who analyzes DNA from microbial communities to characterize their behavior. He has also worked extensively in bioimage informatics - the application of machine learning techniques for the analysis of images of biological specimens. His main focus is on the processing and integration of large-scale datasets. He has a PhD from Carnegie Mellon University and has authored several scientific publications. In 2004, he began developing in Python and has contributed to several open source libraries. He is currently a faculty member at Fudan University in Shanghai.

Willi Richert has a PhD in machine learning/robotics, where he has used reinforcement learning, hidden Markov models, and Bayesian networks to let heterogeneous robots learn by imitation. Now at Microsoft, he is involved in various machine learning areas, such as deep learning, active learning, or statistical machine translation. Willi started as a child with BASIC on his Commodore 128. Later, he discovered Turbo Pascal, then Java, then C++-only to finally arrive at his true love: Python.

Matthieu Brucher is a computer scientist who specializes in high-performance computing and computational modeling and currently works for JPMorgan in their quantitative research branch. He is also the lead developer of Audio ToolKit, a library for real-time audio signal processing. He has a PhD in machine learning and signals processing from the University of Strasbourg, two Master of Science degreesone in digital electronics and signal processing and another in automation from the University of Paris XI and Supelec, as well as a Master of Music degree from Bath Spa University.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

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Paperback. Etat : New. 3rd Revised edition. Get more from your data by creating practical machine learning systems with PythonKey FeaturesDevelop your own Python-based machine learning systemDiscover how Python offers multiple algorithms for modern machine learning systemsExplore key Python machine learning libraries to implement in your projectsBook DescriptionMachine learning allows systems to learn things without being explicitly programmed to do so. Python is one of the most popular languages used to develop machine learning applications, which take advantage of its extensive library support. This third edition of Building Machine Learning Systems with Python addresses recent developments in the field by covering the most-used datasets and libraries to help you build practical machine learning systems.Using machine learning to gain deeper insights from data is a key skill required by modern application developers and analysts alike. Python, being a dynamic language, allows for fast exploration and experimentation. This book shows you exactly how to find patterns in your raw data. You will start by brushing up on your Python machine learning knowledge and being introduced to libraries. You'll quickly get to grips with serious, real-world projects on datasets, using modeling and creating recommendation systems. With Building Machine Learning Systems with Python, you'll gain the tools and understanding required to build your own systems, all tailored to solve real-world data analysis problems.By the end of this book, you will be able to build machine learning systems using techniques and methodologies such as classification, sentiment analysis, computer vision, reinforcement learning, and neural networks.What you will learnBuild a classification system that can be applied to text, images, and soundEmploy Amazon Web Services (AWS) to run analysis on the cloudSolve problems related to regression using scikit-learn and TensorFlowRecommend products to users based on their past purchasesUnderstand different ways to apply deep neural networks on structured dataAddress recent developments in the field of computer vision and reinforcement learningWho this book is forBuilding Machine Learning Systems with Python is for data scientists, machine learning developers, and Python developers who want to learn how to build increasingly complex machine learning systems. You will use Python's machine learning capabilities to develop effective solutions. Prior knowledge of Python programming is expected. N° de réf. du vendeur LU-9781788623223

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