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Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study, Virtual Classroom | Video and Text Based |
Courses and Certificate Fees
| Fees Informations | Certificate Availability | Certificate Providing Authority |
|---|---|---|
| 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
Instructors
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13 Feb'24, 06:25 AM
19 Dec'24, 09:48 AM
19 Dec'24, 06:44 AM
17 Dec'24, 12:40 PM
17 Dec'24, 04:01 AM
17 Dec'24, 03:43 AM
16 Dec'24, 12:25 PM
16 Dec'24, 11:46 AM
13 Dec'24, 12:28 PM
13 Dec'24, 09:28 AM
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