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Building Machine Learning Systems with Python - Third Edition
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491_9781788623223

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Build intelligent end-to-end machine learning systems with PythonKey FeaturesUse scikit-learn and TensorFlow to train your machine learning modelsImplement popular supervised and unsupervised machine learning algorithms in PythonDiscover best practices for building production-grade machine learning systems from scratchBook DescriptionMachine 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 guides hands-on approach, youll 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. Youll 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, youll understand how to use Pythons 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, youll have the skills you need to confidently train and deploy enterprise-grade machine learning models in Python. What you will learnBuild a classification system that can be applied to text, images, and soundSolve regression-related problems using scikit-learn and TensorFlowRecommend products to users based on their previous purchasesExplore different methods of applying deep neural networks to your dataUnderstand recent advances in computer vision and natural language processing (NLP)Deploy Amazon Web Services (AWS) to run data models on the cloudWho this book is for:?This book is fo
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