Course intended for
This course is designated for data analysts who wish to enter the world of machine learning. The course is also addressed to programmers who wish to begin their adventure with machine learning. The course facilitates familiarisation with a quickly developing domain of predictive data analysis which is becoming a standard due to data flooding. In addition participants may better understand the use of machine learning tools and techniques in the environment of Python, a mature production language.
The participants will get familiar with the basics of machine learning and the related tools in Python. The course utilises a combination of various tools, which in turn enables execution of more complex analyses and predictions using machine learning algorithms. Furthermore, algorithms and models of machine learning in Python may very often be easily transferred to the production environment in a relatively short time.
Course intended for
Text Mining constitute at least 70% of all data generated in IT systems. Such data is rarely used for analytical purposes or knowledge discovery. This course covers the problems related to the processing and analysis of Text Mining. The course is addressed to:
programmers who wish to use the knowledge discovery methods using Text Mining in their systems,
analysts who wish to develop their analytical workshop by a Text Mining analysis tool,
those interested in using statistical tools and machine learning methods when working with Text Mining.
Basic programming knowledge in any language is required (for example Python, R, matlab etc.).
Participants will learn a number of tools designated for working with Text Mining. A number of examples of their use will be presented which cover the majority of topics from that domain.The basic languages in working with texts will be presented: R, Python and Java.
Course intended for:
Training course is aimed at developers and data analysts who want to learn the concepts of deep neural networks.
The course objective is to equip participants with knowledge about deep neural networks. The participants will be able to program and debug deep neural network, including convolutional and the recurrent neural networks. Neural network architecture discussed during the training will be presented by means of basic concepts of computer vision (classification) and processing of natural language (sentiment analysis, machine translation). Additionally, newest research along with the most popular uses of deep learning will be presented, such as automated generation of description for images and transfer of style between images.