Predictive Analytics with TensorFlow

Course details

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.

About the Author

Md. Rezaul Karim is a Research Scientist at Fraunhofer FIT, Germany. He is also a PhD candidate at RWTH Aachen University, Aachen, Germany. He holds a BSc and an MSc degree in Computer Science. Before joining Fraunhofer FIT, he worked as a Researcher at Insight Centre for Data Analytics, Ireland. Before this, he worked as a Lead Engineer at Samsung Electronics' distributed R&D Institutes in Korea, India, Turkey, and Bangladesh. Previously, he worked as a Research Assistant at the database lab, Kyung Hee University, Korea. He also worked as an R&D engineer with BMTech21 Worldwide, Korea. Before this, he worked as a Software Engineer with i2SoftTechnology, Dhaka, Bangladesh. He has more than 8 years' experience in the area of research and development with a solid understanding of algorithms and data structures in C, C++, Java, Scala, R, and Python. He has published several books, articles, and research papers concerning big data and virtualization technologies, such as Spark, Kafka, DC/OS, Docker, Mesos, Zeppelin, Hadoop, and MapReduce. He is also equally competent with deep learning technologies such as TensorFlow, DeepLearning4j, and H2O. His research interests include machine learning, deep learning, the semantic web, linked data, big data, and bioinformatics. Also he is the author of the following book titles:

• Large-Scale Machine Learning with Spark (Packt Publishing Ltd.)

• Deep Learning with TensorFlow (Packt Publishing Ltd.)

• Scala and Spark for Big Data Analytics (Packt Publishing Ltd.)