Predictive Analytics with TensorFlow [Video]

More Information
Learn
  • Get a solid and theoretical understanding of linear algebra, statistics, and probability for predictive modeling
  • Develop predictive models using classification, regression, and clustering algorithms
  • Develop predictive models for NLP
  • Learn how to use reinforcement learning for predictive analytics
  • Use factorization machines for advanced recommendation systems
  • Get hands-on with deep learning architectures to master advanced predictive analytics
  • Learn how to use deep neural networks for predictive analytics
  • See how to use recurrent neural networks for predictive analytics
  • Master convolutional neural networks for emotion recognition, image classification, and sentiment analysis
About

Predictive analytics discovers hidden patterns in structured and unstructured data for automated decision-making in business intelligence. This course will help you build, tune, and deploy predictive models with TensorFlow in three main divisions. The first division covers linear algebra, statistics, and probability theory for predictive modeling. The second division covers developing predictive models via supervised (classification and regression) and unsupervised (clustering) algorithms. It then explains how to develop predictive models for NLP and covers reinforcement learning algorithms. Lastly, this division covers developing a factorization machine-based recommendation system. The third division covers deep learning architectures for advanced predictive analytics, including deep neural networks and recurrent neural networks for high-dimensional and sequence data. Finally, you'll use convolutional neural networks for predictive modeling for emotion recognition, image classification, and sentiment analysis.

Style and Approach

TensorFlow, a popular library for machine learning, embraces open-source innovation and community engagement, but has the support, guidance, and stability of a large corporation. This course has a step-by-step approach towards achieving the goal.

Features
  • A quick guide to gaining hands-on experience with deep learning in different domains such as digit/image classification and text
  • Build your own smart, predictive models with TensorFlow using an easy-to-follow approach
  • Understand deep learning and predictive analytics along with its challenges and best practices
Course Length 5 hours 21 minutes
ISBN 9781789615494
Date Of Publication 29 Aug 2018

Authors

Md. Rezaul Karim

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 Institute for Applied Information Technology-FIT, Germany. He is also a Ph.D. candidate at the RWTH Aachen University, Aachen, Germany.

He holds a BS and an MS degree in Computer Engineering. Before joining the Fraunhofer-FIT, he had been working as a Researcher at the 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.