تحلیل داده‌های پیشرفته با استفاده از پایتون: با الگوهای معماری، طبقه‌بندی متن و تصویر و تکنیک‌های بهینه‌سازی

Understand advanced data analytics concepts such as time series and principal component analysis with ETL, supervised learning, and PySpark using Python. This book covers architectural patterns in data analytics, text and image classification, optimization techniques, natural language processing (NLP), and computer vision in the cloud environment. Generic design patterns in Python programming is clearly explained, emphasizing architectural practices such as hot potato anti-patterns. You'll review recent advances in databases such as Neo4j, Elasticsearch, and MongoDB. You'll then study feature engineering in images and texts with implementing business logic and see how to build Machine Learning and Deep Learning models using transfer learning. Advanced Analytics with Python, 2nd edition features a chapter on clustering with a neural network, regularization techniques, and algorithmic design patterns in data analytics with reinforcement learning. Finally, the recommender system in PySpark explains how to optimize models for a specific application. What You'll Learn: Build intelligent systems for enterprise Review time series analysis, classifications, regression, and clustering Explore supervised learning, unsupervised learning, reinforcement learning, and transfer learning Use cloud platforms like GCP and AWS in data analytics Understand Covers design patterns in Python Who This Book Is For: Data scientists and software developers interested in the field of data analytics. Understand advanced data analytics concepts such as time series and principal component analysis with ETL, supervised learning, and PySpark using Python. This book covers architectural patterns in data analytics, text and image classification, optimization techniques, natural language processing, and computer vision in the cloud environment. Generic design patterns in Python programming is clearly explained, emphasizing architectural practices such as hot potato anti-patterns. You'll review recent advances in databases such as Neo4j, Elasticsearch, and MongoDB. You'll then study feature engineering in images and texts with implementing business logic and see how to build machine learning and deep learning models using transfer learning. Advanced Analytics with Python, 2nd edition features a chapter on clustering with a neural network, regularization techniques, and algorithmic design patterns in data analytics with reinforcement learning. Finally, the recommender system in PySpark explains how to optimize models for a specific application. -- Provided by publisher A Birds Eye View to AI System Sayan Mukhopadhyay, Pratip Samanta Pages 1-22 ETL with Python Sayan Mukhopadhyay, Pratip Samanta Pages 23-52 Feature Engineering and Supervised Learning Sayan Mukhopadhyay, Pratip Samanta Pages 53-79 Unsupervised Learning: Clustering Sayan Mukhopadhyay, Pratip Samanta Pages 81-113 Deep Learning and Neural Networks Sayan Mukhopadhyay, Pratip Samanta Pages 115-159 Time Series Sayan Mukhopadhyay, Pratip Samanta Pages 161-184 Analytics at Scale Sayan Mukhopadhyay, Pratip Samanta Pages 185-241 Back Matter Pages 243-249