Covers: theory of Estimator Function

0- What is an estimator? Enumerate the different ways one can estimate data.

Read section 2.8 as a way to understand estimator functions and the different flavours they can come in for supervised learning problems.

Trevor Hastie, Robert Tibshirani, Jerome Friedman

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- Objectives
- Learn about linear regression.
- Potential Use Cases
- Linear regression is a fundamental regression technique.
- Who is this for ?
- INTERMEDIATE

Click on each of the following **annotated items** to see details.

BOOK_CHAPTER 1. Linear regression: An Intro

- The theoretical underpinnings of linear regression.

30 minutes

BOOK_CHAPTER 2. Classes of Restricted Estimators

- What is an estimator? Enumerate the different ways one can estimate data.

10 minutes

VIDEO 3. Variance-Covariance Matrix: An In Depth Tutorial

- What is the variance covariance matrix and how does it describe the underlying distribution of data?

20 minutes

ARTICLE 4. F-Statistic: A quick refresher

- How do you determine the F-statistic and why do we use it?

10 minutes

ARTICLE 5. Scikit-learn implementation of Linear Regression

- Implementing linear regression in scikit learn.

10 minutes

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