# Linear Regression: Absolute Fundamentals

Ideas on Machine Learning & Linear Regression using scikit-learn in Python and predicting the positive cases for COVID19
English
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.

## Who this course is for:

• Beginner Python developers who are curious about Machine Learning

## Course content

1 section8 lectures1h 40m total length
• Introduction to Machine Learning
25:24
• Understanding Linear Regression through graphs
17:08
12:52
06:24
• Cleaning the data
02:07
• The Model
31:50
• Visualising the data
01:47
• Concluding Remarks
03:11

## Instructor

Engineer | Course Instructor

Self motivated budding electronics and communication engineer who can work on multiple roles. Interested in Modelling digital circuits using hardware description languages. Have a strong grasp of Verilog, Computer Architecture, C, C++, Java, Embedded C, Python, Data Structures, Algorithms, Machine Learning and Deep Learning. Loves to teach and so being a course instructor in Udemy, Learnfly and Guruface Inc. Cross Platform Application developer specialized in Google Flutter with Dart Programming and using Firebase as the backend. Skilled to work in tools such as MATLAB, Simulink, Xilinx Vivado, TinkerCAD, Proteus Design Suite, Camtasia and Altium Designer for PCB Design