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Full Description
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 analyticswith 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.
Contents
Chapter 1: Overview of Python Language.- Chapter 2: ETL with Python.- Chapter 3: Supervised Learning and Unsupervised Learning with Python.- Chapter 4: Clustering with Python.- Chapter 5: Deep Learning & Neural Networks.- Chapter 6: Time Series Analysis.- Chapter 7: Analytics in Scale.
