Advanced Data Analytics Using Python : With Machine Learning, Deep Learning and NLP Examples

Gain a broad foundation of advanced data analytics concepts and discover the recent revolution in databases such as Neo4j, Elasticsearch, and MongoDB. This book discusses how to implement ETL techniques including topical crawling, which is applied in domains such as high-frequency algorithmic trading and goal-oriented dialog systems. You’ll also see examples of machine learning concepts such as semi-supervised learning, deep learning, and NLP. __Advanced Data Analytics Using Python__ also covers important traditional data analysis techniques such as time series and principal component analysis. After reading this book you will have experience of every technical aspect of an analytics project. You’ll get to know the concepts using Python code, giving you samples to use in your own projects. **What You Will Learn** * Work with data analysis techniques such as classification, clustering, regression, and forecasting * Handle structured and unstructured data, ETL techniques, and different kinds of databases such as Neo4j, Elasticsearch, MongoDB, and MySQL * Examine the different big data frameworks, including Hadoop and Spark * Discover advanced machine learning concepts such as semi-supervised learning, deep learning, and NLP **Who This Book Is For** Data scientists and software developers interested in the field of data analytics. "Gain a broad foundation of advanced data analytics concepts and discover the recent revolution in databases such as Neo4j, Elasticsearch, and MongoDB. This book discusses how to implement ETL techniques including topical crawling, which is applied in domains such as high-frequency algorithmic trading and goal-oriented dialog systems. You'll also see practical examples of machine learning concepts such as semi-supervised learning, deep learning, computer vision and NLP. Practical Data Analytics with Python also covers important traditional data analysis techniques such as time series, principal component analysis through examples from real industry projects. After reading this book you will have experience of every technical aspect of an industrial analytics project. You'll get to know the concepts using Python code, thoroughly explained in each case."-- Provided by publisher Chapter 1: Introduction Chapter 2: ETL with Python Chapter 3: Supervised Learning with Python Chapter 4: Unsupervised Learning with Python Chapter 5: Deep Learning & Neural Networks Chapter 6: Time Series Analysis Chapter 7: Python in Emerging Technologies