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Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study | Video and Text Based |
Courses and Certificate Fees
| Certificate Availability |
|---|
| no |
The Syllabus
- What is Machine Learning (ML) ?
- ML and Finance; not ML for Finance
- Classical Machine Learning: Introduction
- Supervised Learning
- Our first predictor
- Notational conventions
- Linear Regression
- The Recipe for Machine Learning
- The Regression Loss Function
- Bias and Variance
- Data Transformations: Introduction and mechanics
- Logistic Regression
- Non-numeric variables: text, images
- Multinomial Classification
- The Classification Loss Function
- Baseline model
- The Dummy Variable Trap
- Transformations
- Loss functions: mathematics
- Entropy, Cross Entropy, KL Divergence
- Decision Trees
- Naive Bayes
- Ensembles
- Feature Importance
- Support Vector Classifiers
- Gradient Descent
- Interpretation: Linear Models
- Unsupervised Learning
- Dimensionality Reduction
- Clustering
- Principal Components
- Pseudo Matrix Factorization: preview of Deep Learning
Instructors
Articles
24 Jun'24, 10:39 AM
24 Jun'24, 10:27 AM
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27 May'24, 06:15 AM
20 May'24, 06:39 AM
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15 Apr'24, 10:30 AM
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4 Mar'24, 09:29 AM
29 Feb'24, 06:05 AM
27 Feb'24, 10:32 AM
26 Feb'24, 12:08 PM
13 Feb'24, 06:25 AM
24 Jun'24, 10:39 AM
24 Jun'24, 10:27 AM
24 Jun'24, 10:22 AM
24 Jun'24, 10:18 AM
24 Jun'24, 10:13 AM
24 Jun'24, 10:09 AM
24 Jun'24, 10:03 AM
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