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- Understand predictive analytics along with its challenges and best practices
- Embedded with assessments that will help you revise the concepts you have learned in this book
Predictive analytics discovers hidden patterns from structured and unstructured data for automated decision making in business intelligence. Predictive decisions are becoming a huge trend worldwide, catering to wide industry sectors by predicting which decisions are more likely to give maximum results. TensorFlow, Google's brainchild, is immensely popular and extensively used for predictive analysis.
This book is a quick learning guide on all the three types of machine learning, that is, supervised, unsupervised, and reinforcement learning with TensorFlow. This book will teach you predictive analytics for high-dimensional and sequence data. In particular, you will learn the linear regression model for regression analysis. You will also learn how to use regression for predicting continuous values. You will learn supervised learning algorithms for predictive analytics. You will explore unsupervised learning and clustering using K-meansYou will then learn how to predict neighborhoods using K-means, and then, see another example of clustering audio clips based on their audio features.
This book is ideal for developers, data analysts, machine learning practitioners, and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow.
This book is embedded with useful assessments that will help you revise the concepts you have learned in this book.
What you will learn
- Learn TensorFlow features in a real-life problem, followed by detailed TensorFlow installation and configuration
- Explore computation graphs, data, and programming models also get an insight into an example of implementing linear regression model for predictive analytics
- Solve the Titanic survival problem using logistic regression, random forests, and SVMs for predictive analytics
- Dig deeper into predictive analytics and find out how to take advantage of it to cluster records belonging to the certain group or class for a dataset of unsupervised observations
- Learn several examples of how to apply reinforcement learning algorithms for developing predictive models on real-life datasets
Who this book is for
This book is aimed at developers, data analysts, machine learning practitioners, and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow.
Md. Rezaul Karim has more than 8 years of experience in the area of research and development with a solid knowledge of algorithms and data structures in C/C++, Java, Scala, R, and Python focusing Big Data technologies: Spark, Kafka, DC/OS, Docker, Mesos, Zeppelin, Hadoop, and MapReduce and Deep Learning technologies: TensorFlow, DeepLearning4j and H2O-Sparking Water. His research interests include Machine Learning, Deep Learning, Semantic Web/Linked Data, Big Data, and Bioinformatics. He is a research scientist at Fraunhofer FIT, Germany. He is also a Ph.D. candidate at the RWTH Aachen University, Aachen, Germany. He holds a BSc and an MSc degree in Computer Science. Before joining the Fraunhofer FIT, he had been working as a researcher at Insight Centre for Data Analytics, Ireland. Before that, he worked as a lead engineer with Samsung Electronics' distributed R&D Institutes in Korea, India, Vietnam, Turkey, and Bangladesh. Before that, he worked as a research assistant in the Database Lab at Kyung Hee University, Korea. He also worked as an R&D engineer with BMTech21 Worldwide, Korea. Even before that, he worked as a software engineer with i2SoftTechnology, Dhaka, Bangladesh. He is the author of the following book titles with Packt Publishing: . Large-Scale Machine Learning with Spark . Deep Learning with TensorFlow . Scala and Spark for Big Data Analytics . Predictive Analytics with TensorFlow
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