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HACCP stands for Hazard Analysis and Critical Control Point. It is an internationally recognised food safety management system that is used to identify, evaluate and control hazards which are significant for food safety. Rather than being a reactive system, HACCP is pro-active. The focus is on prevention of hazards rather than relying mainly on end-product testing. This course will start by covering some of the key terms you will need to understand before moving on to identifying critical control points and analysing some of the key hazards. It’ll show you how to determine control points and how to avoid cross-contamination in the food chain. It will also cover some of the control measures that can be taken along with how to address a problem is a critical limit is breached. Towards the end of the course all this will be brought together by taking you step by step through the implementation of HACCP and see how the 7 principles of HACCP are integrated into the whole process.
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The course is crafted to reflect the in-demand skills in the marketplace that will help you in mastering the key concepts and methodologies of RL and deep RL, along with several practical implementations. This course will help you know the theory and practical aspects of reinforcement and deep reinforcement learning.
This course first equips you with the fundamentals of Python and then progresses to teach you how to use various libraries such as NumPy, Pandas, Seaborn, Bokeh, and so on. This course contains several mini projects so that, by the end of this course, you will be equipped with the essential tools you need to become a visualization expert.