Machine Learning with Python from Linear Models to Deep Learning at MIT Cambridge
Interested in this College?
Get updates on Eligibility, Admission, Placements Fees Structure
Compare 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 24805 | 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
Articles