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Globally impacting millions of individuals, depression is a mental health issue. The development of artificial intelligence and machine learning has created fresh opportunities for mental health problem management and detection. One useful method to use data science for mental health purposes for Depression Recognition by Deep Learning . This blog offers a thorough, detailed, Python step-by-step approach on creating a machine learning mental health model to find depression. This guide will get you going on Python AI projects for academic, research, or commercial interests. Step 1: Understanding the Problem and Data Collection Clearly defined problems are crucial before developing a depression detecting system. Using a variety of data sources such as social media content, polls, or clinical databases the aim is to determine if someone is exhibiting depressive symptoms. One very important stage is data collecting. Among the freely accessible datasets for depression identification...