Parvus Analytics
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Applied Machine Learning and Data Analytics
This book constitutes the refereed proceedings of the 5th International Conference on Applied Machine Learning and Data Analytics, AMLDA 2022, held in Reynosa, Tamaulipas, Mexico, during December 22–23, 2022. The 16 full papers and 4 short papers included in this book were carefully reviewed and selected from 89 submissions. They were organized in topical sections as follows: Machine learning, Healthcare and medical imaging informatics; biometrics; forensics; precision agriculture; risk management; robotics and satellite imaging.
Mastering Predictive Analytics with Python
Author: Joseph Babcock
language: en
Publisher: Packt Publishing Ltd
Release Date: 2016-08-31
Exploit the power of data in your business by building advanced predictive modeling applications with Python About This Book Master open source Python tools to build sophisticated predictive models Learn to identify the right machine learning algorithm for your problem with this forward-thinking guide Grasp the major methods of predictive modeling and move beyond the basics to a deeper level of understanding Who This Book Is For This book is designed for business analysts, BI analysts, data scientists, or junior level data analysts who are ready to move from a conceptual understanding of advanced analytics to an expert in designing and building advanced analytics solutions using Python. You're expected to have basic development experience with Python. What You Will Learn Gain an insight into components and design decisions for an analytical application Master the use Python notebooks for exploratory data analysis and rapid prototyping Get to grips with applying regression, classification, clustering, and deep learning algorithms Discover the advanced methods to analyze structured and unstructured data Find out how to deploy a machine learning model in a production environment Visualize the performance of models and the insights they produce Scale your solutions as your data grows using Python Ensure the robustness of your analytic applications by mastering the best practices of predictive analysis In Detail The volume, diversity, and speed of data available has never been greater. Powerful machine learning methods can unlock the value in this information by finding complex relationships and unanticipated trends. Using the Python programming language, analysts can use these sophisticated methods to build scalable analytic applications to deliver insights that are of tremendous value to their organizations. In Mastering Predictive Analytics with Python, you will learn the process of turning raw data into powerful insights. Through case studies and code examples using popular open-source Python libraries, this book illustrates the complete development process for analytic applications and how to quickly apply these methods to your own data to create robust and scalable prediction services. Covering a wide range of algorithms for classification, regression, clustering, as well as cutting-edge techniques such as deep learning, this book illustrates not only how these methods work, but how to implement them in practice. You will learn to choose the right approach for your problem and how to develop engaging visualizations to bring the insights of predictive modeling to life Style and approach This book emphasizes on explaining methods through example data and code, showing you templates that you can quickly adapt to your own use cases. It focuses on both a practical application of sophisticated algorithms and the intuitive understanding necessary to apply the correct method to the problem at hand. Through visual examples, it also demonstrates how to convey insights through insightful charts and reporting.
Python: Advanced Predictive Analytics
Author: Joseph Babcock
language: en
Publisher: Packt Publishing Ltd
Release Date: 2017-12-27
Gain practical insights by exploiting data in your business to build advanced predictive modeling applications About This Book A step-by-step guide to predictive modeling including lots of tips, tricks, and best practices Learn how to use popular predictive modeling algorithms such as Linear Regression, Decision Trees, Logistic Regression, and Clustering Master open source Python tools to build sophisticated predictive models Who This Book Is For This book is designed for business analysts, BI analysts, data scientists, or junior level data analysts who are ready to move on from a conceptual understanding of advanced analytics and become an expert in designing and building advanced analytics solutions using Python. If you are familiar with coding in Python (or some other programming/statistical/scripting language) but have never used or read about predictive analytics algorithms, this book will also help you. What You Will Learn Understand the statistical and mathematical concepts behind predictive analytics algorithms and implement them using Python libraries Get to know various methods for importing, cleaning, sub-setting, merging, joining, concatenating, exploring, grouping, and plotting data with pandas and NumPy Master the use of Python notebooks for exploratory data analysis and rapid prototyping Get to grips with applying regression, classification, clustering, and deep learning algorithms Discover advanced methods to analyze structured and unstructured data Visualize the performance of models and the insights they produce Ensure the robustness of your analytic applications by mastering the best practices of predictive analysis In Detail Social Media and the Internet of Things have resulted in an avalanche of data. Data is powerful but not in its raw form; it needs to be processed and modeled, and Python is one of the most robust tools out there to do so. It has an array of packages for predictive modeling and a suite of IDEs to choose from. Using the Python programming language, analysts can use these sophisticated methods to build scalable analytic applications. This book is your guide to getting started with predictive analytics using Python. You'll balance both statistical and mathematical concepts, and implement them in Python using libraries such as pandas, scikit-learn, and NumPy. Through case studies and code examples using popular open-source Python libraries, this book illustrates the complete development process for analytic applications. Covering a wide range of algorithms for classification, regression, clustering, as well as cutting-edge techniques such as deep learning, this book illustrates explains how these methods work. You will learn to choose the right approach for your problem and how to develop engaging visualizations to bring to life the insights of predictive modeling. Finally, you will learn best practices in predictive modeling, as well as the different applications of predictive modeling in the modern world. The course provides you with highly practical content from the following Packt books: 1. Learning Predictive Analytics with Python 2. Mastering Predictive Analytics with Python Style and approach This course aims to create a smooth learning path that will teach you how to effectively perform predictive analytics using Python. Through this comprehensive course, you'll learn the basics of predictive analytics and progress to predictive modeling in the modern world.