Kanth Mentorship Show
"Kanth Mentorship Podcast" is a podcast series hosted by Rajeev Kanth that focuses on career transition and development, particularly in Data Analytics, Data Science, Machine Learning, Data Engineering, Cloud Computing, AWS, Azure, DataBricks, SQL, Power BI, Tableau, Python Generative AI, Deep Learning, Computer Vision, LLMs, and Artificial Intelligence. The podcast covers various topics, including interview preparation, industry insights, and career path guidance, aimed at various individuals, including students, working professionals, and those re-entering the workforce.
Kanth provides personalized guidance for career transitions, discussing suitable pathways based on individual backgrounds and challenges. The focus is on the strategic enhancement of resumes and interview preparation, especially for jobs in India, the USA, the UK, Canada, and the UAE.
For more details and to listen to the podcast episodes, you can visit their pages on Apple Podcasts and Spotify.
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Kanth Mentorship Show
AI Live Project : Reinforcement Learning Solution (For Banking Industry)
Machine Learning is a field that has crazy advancements in the past couple of years. This trend and advancements have created a lot of Job opportunities in the industry. The need for Machine Learning Engineers are high in demand due to evolving technology and generation of huge amounts of data like Big Data. Reinforcement learning is an area of Machine Learning, It's about taking suitable action to maximize reward in a particular situation and is to be employed by various software and machines to find the best possible behavior that it should take in a specific situation. Reinforcement learning differs from the supervised learning in a way.
In supervised learning the training data has the answer key with it so the model is trained with the correct answer itself whereas in reinforcement learning there is no answer but the reinforcement agent decides what to do to perform the given task. In the absence of training data set it bounds to learn from its experience. There are two important learning models in reinforcement learning :
(a) Markov Decision Process. (b) Q-learning
Reinforcement learning solves a different kind of problem. In reinforcement learning, there is an agent that interacts with a certain environment, thus changing its state and receives rewards (or) penalties for its input. It's goal is to find patterns of actions by trying them all and comparing the results that yield the most reward points.
One of the key features of reinforcement learning is that the agents actions may not affect the immediate state of the environment but it impacts the subsequent ones.
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