Machine Learning Engineer to Machine Learning Scientist: A journey with Image Processing

This talk bridges the relation between image processing and machine learning. There are numerous youngsters who all are willing to build a career in this particular domain and are desperately looking for a proper initiation. Some of them are even employed as machine learning engineers and are wondering how to become a machine learning scientist to deal with new challenges rather than becoming a tool based application developer. The talk provides an idea about the research driven approach towards machine learning with content based image data. It provides certain clues to understand cutting edge research and to address open problems with one's own research propositions. It also covers research best practices and tips to pursue quality research in real time. 

Hence, the talk distinctively includes:

• Difference between ML Engineer and ML Scientist

• Demystifying Innovation in ML with Image Data

• Transformation steps for any enthusiast to ML Scientist 

• Roadmap for research in image processing based machine learning

• Bringing your research to prominence

• Best practices of Research in Image Recognition