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In this course you will gain knowledge on 3 important approaches of machine learning.

• Supervised learning: This is used for solving a range of data science problems and interpret their outputs like decision tree, regression, classification, neural network etc.
• Unsupervised learning: This is used to analyzing data, looking for patterns and identifying structures in data like randomize, optimization, clustering, feature selection etc.
• Reinforcement learning: This will teach you the algorithms for designing processes like Markov decision processes, game theory etc.

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• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning

Duration: 4 months

Skill Level: Intermediate

Mode: Online Self- Paced

Fees: Free

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[su_tab title=”Eligibility”]

Basic knowledge of Probability Theory, Linear Algebra and Statistics.

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[su_tab title=”Contact”]

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