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ali n predictive analytics modern enterprise 2024

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Textbook in PDF format The surging predictive analytics market is expected to grow from $10.5 billion today to $28 billion by 2026. With the rise in automation across industries, the increase in data-driven decision-making, and the proliferation of IoT devices, predictive analytics has become an operational necessity in today's forward-thinking companies. If you're a data professional, you need to be aligned with your company's business activities more than ever before. This practical book provides the background, tools, and best practices necessary to help you design, implement, and operationalize predictive analytics on-premises or in the cloud. Pandas is an open source Python package that makes it easy to perform data wrangling in Python. It is built on top of the NumPy library that we discussed in the previous section. Pandas makes it easy to work with data for the purpose of Data Science and data analytics. Just as for NumPy the main object provided by the library was an n-dimensional array, for Pandas the main object is a Pandas DataFrame. TensorFlow was created and made open source by Google. It is one of the most-used platforms worldwide for Machine Learning (ML). Tensorflow.org defines TensorFlow as “an end-to-end platform for Machine Learning.” TensorFlow has wide-ranging applications, including structured data processing, automated image classification, advanced optical character recognition (OCR), video analysis, and sentiment analysis, to name a few. It provides the tools for data ingestion and processing, ML model creation, ML model training, ML model deployment, and ML model life cycle management. Support for graphics processing units (GPUs) and tensor processing units (TPUs) allows users to work on compute-intensive deep neural networks. The fact that models can be deployed on a wide range of devices, including mobile phones, client machines and servers, edge devices, and the cloud, is one of the many reasons for its high rate of adoption among data professionals and enterprises. Explore ways that predictive analytics can provide direct input back to your business Understand mathematical tools commonly used in predictive analytics Learn the development frameworks used in predictive analytics applications Appreciate the role of predictive analytics in the machine learning process Examine industry implementations of predictive analytics Build, train, and retrain predictive models using Python and TensorFlow Who Is This Book For? The primary audience for this book is data professionals and technical managers who want to learn the scientific foundation for predictive analytics and its application in today’s enterprise. The book will allow readers to navigate the life cycle of developing predictive analytics capabilities using multiple frameworks, techniques, and platforms. Readers can further hone their understanding of predictive analytics via industry-leading examples that are relevant today. The book is designed to be simple but not basic; comprehensive but not complex. If you are looking for a book that you can pick up on the go to start learning about predictive analytics, this one’s for you. I assume no prior knowledge on the part of the reader except for a basic understanding of programming in Python, a high-level understanding of foundational mathematics, and a high-level understanding of Amazon Web Services (AWS). Contents: Preface 1. Data Analytics in the Modern Enterprise 2. Predictive Analytics: An Operational Necessity 3. The Mathematics and Algorithms Behind Predictive Analytics 4. Working with Data 5. Python and scikit-learn for Predictive Analytics 6. TensorFlow and Keras for Predictive Analytics 7. Predictive Analytics for Business Problem-Solving 8. Exploring AWS Cloud Provider Services for AI/ML 9. Food for Thought

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ali n predictive analytics modern enterprise 2024

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