Machine Learning with Python: from Linear Models to Deep Learning
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Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
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English | Self Study, Virtual Classroom | Video and Text Based |
Courses and Certificate Fees
Fees Informations | Certificate Availability | Certificate Providing Authority |
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INR 25183 | yes | MIT Cambridge |
The Syllabus
- Introduction
- Linear classifiers, separability, perceptron algorithm
- Maximum margin hyperplane, loss, regularization
- Stochastic gradient descent, over-fitting, generalization
- Linear regression
- Recommender problems, collaborative filtering
- Non-linear classification, kernels
- Learning features, Neural networks
- Deep learning, back propagation
- Recurrent neural networks
- Recurrent neural networks
- Generalization, complexity, VC-dimension
- Unsupervised learning: clustering
- Generative models, mixtures
- Mixtures and the EM algorithm
- Learning to control: Reinforcement learning
- Reinforcement learning continued
- Applications: Natural Language Processing
- Automatic Review Analyzer
- Digit Recognition with Neural Networks
- Reinforcement Learning
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