Md. Rezaul Karim (Packt Publishing - TensorFlow powerful predictive analytics with tensorflow: a fast-paced guide on supervised, unsupervised, and reinforcement learning with tensorflow

• Predictive Analytics with TensorFlow by Md. Rezaul Karim

We have the best work of the following esteemed author to ensure that your learning journey is smooth:

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:

Maps are vital for your journey, especially when you're holidaying in another continent. When it comes to learning, a roadmap helps you in giving a definitive path for progressing towards the goal. So, here you're presented with a roadmap before you begin your journey.

This book is meticulously designed and developed in order to empower you with all the right and relevant information on TensorFlow. We've created this Learning Path for you that consists of four lessons:

, From Data to Decisions Getting Started with TensorFlow , provides a detailed description of the main TensorFlow features in a real-life problem, followed by detailed discussions about TensorFlow installation and configuration. It then covers computation graphs, data, and programming models before getting started with TensorFlow. The last part of the lesson contains an example of implementing linear regression model for predictive analytics.

, Putting Data in Place Supervised Learning for Predictive Analytics , covers some TensorFlow-based supervised learning techniques from a theoretical and practical perspective. In particular, the linear regression model for regression analysis will be covered on a real dataset. It then shows how you could solve the Titanic survival problem using logistic regression, random forests, and SVMs for predictive analytics.

, Clustering Your Data Unsupervised Learning for Predictive Analytics , digs deeper into predictive analytics and finds out how you can take advantage of it to cluster records belonging to the certain group or class for a dataset of unsupervised observations. It will then provide some practical examples of unsupervised learning. Particularly, clustering techniques using TensorFlow will be discussed with some hands-on examples.

, Using Reinforcement Learning for Predictive Analytics , talks about designing machine learning systems driven by criticism and rewards. It will show several examples on how to apply reinforcement learning algorithms for developing predictive models on real-life datasets.

This book is aimed at data analysts, data scientists, and machine learning practitioners who want to build powerful, robust, and accurate predictive models with the power of TensorFlow. Some of the prerequisites that is required before you begin this book are:

Despite the huge availability of data and significant investments, many business organizations still go on gut feel because they neither make the proper use of the data nor do they take appropriate and effective business decisions. TensorFlow, on the other hand, can be used to help take the business decision from this huge collection of data. TensorFlow is mathematical software and an open source software library for Machine Intelligence, developed in 2011 by the Google Brain Team and it can be used to help us analyze data to predict the effective business outcome. Although the initial target of TensorFlow was to conduct research in machine learning and in deep neural networks, however, the system is general enough to be applicable in a wide variety of other domains as well.