# Linear Regression: Absolute Fundamentals

Linear Regression: Absolute Fundamentals

*Linear Regression: Absolute Fundamentals Ideas on Machine Learning & Linear Regression using scikit-learn in Python and predicting the positive cases for COVID19*

What you'll learn

- An idea on Machine Learning and Linear Regression

Requirements

- Yes, A basic knowledge in Python 3 is preferred for technical part.

Description

Hey everyone! I welcome you all to my course Machine Learning Absolute Fundamentals for Linear Regression. This course is targeted for Beginner Python Developers who want to kickstart their journey in Machine Learning. In this course, we are going to use a linear regression model from scikit-learn library in Python to predict the total no. of positive cases for COVID19 in a particular state in India.

After completing this course, you'll be able to:

- 1. Define Machine Learning
- 2. Define what is a dataset
- 3. Explain what does Machine Learning do?
- 4. Explain the concept of linear regression
- 5. Explain what is the line of best fit and cost function (MSE)
- 6. Use pandas library functions to read the dataset and to preprocess it
- 7. Splitting data for training and testing
- 8. Create a linear regression model using sklearn and train it
- 9. Evaluate the model and predict the values
- 10. Visualising data using matplotlib

n linear regression, the relationships are modeled using linear predictor functions whose unknown model parameters are estimated from the data. Such models are called linear models. Most commonly, the conditional mean of the response given the values of the explanatory variables (or predictors) is assumed to be an affine function of those values; less commonly, the conditional median or some other quantile is used. Like all forms of regression analysis, linear regression focuses on the conditional probability distribution of the response given the values of the predictors, rather than on the joint probability distribution of all of these variables, which is the domain of multivariate analysis.

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