Reinforced learning in Machine learning|| Explanation in Malayalam

#reinforcedlearning #Machinelearning#Malayalam#brAInTek#typesofmachinelearning
Reinforced learning in Machine learning|| Non Technical explanation in Malayalam
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Reinforcement learning is the training of machine learning models to make a sequence of decisions. The agent learns to achieve a goal in an uncertain, potentially complex environment. In reinforcement learning, an artificial intelligence faces a game-like situation. The computer employs trial and error to come up with a solution to the problem. To get the machine to do what the programmer wants, the artificial intelligence gets either rewards or penalties for the actions it performs. Its goal is to maximize the total reward.
Although the designer sets the reward policy-that is, the rules of the game-he gives the model no hints or suggestions for how to solve the game. It's up to the model to figure out how to perform the task to maximize the reward, starting from totally random trials and finishing with sophisticated tactics and superhuman skills. By leveraging the power of search and many trials, reinforcement learning is currently the most effective way to hint machine's creativity. In contrast to human beings, artificial intelligence can gather experience from thousands of parallel gameplays if a reinforcement learning algorithm is run on a sufficiently powerful computer infrastructure.
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This covers the below Topics:
1. Introduction to Machine learning- Need for AI/ML, why to learn AI/ML, machine learning types, supervised,unsupervised and reinforced learning, application, difference between Human thinking Vs Machine thinking, difference between programming vs Machine learning.
• Machine Learning cours...
2. Mathematics for Machine learning- Trigonometry, linear algebra, matrices, calculus & probability.
• Mathematics for Machin...
3.Python for Machine learning- variables, different libraries needed for data science such as numpy, pandas, matplotlib, etc
Python basics: • Python for beginners M...
Python Intermediate: • Python for Machine Lea...
Python Projects : • Python Programming Tut...
4. Deep-dive into machine learning- How ML algorithm works, the concept of cost function and gradient descent, practical examples for linear regression and Classifications, ML Agorithams and its usage.
5.Introduction to OpenCV- Image/video processing with OpenCV
6. Face recognition- Building a security alarm system using ML techniques
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Superb explanation sir. Thank you

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[@richardjoemartin]

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[@fidhashirin]

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