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
Basic knowledge of Probability Theory, Linear Algebra and Statistics.