About the course
Accomplish the power of data in your business by building advanced predictive modelling applications with Tensorflow.
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.
What You Will 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
Md. Rezaul Karim
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.)
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