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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.
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning

Duration: 4 months

Skill Level: Intermediate

Mode: Online Self- Paced

Fees: Free

Basic knowledge of Probability Theory, Linear Algebra and Statistics.

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