Linear Regression: Absolute Fundamentals

Name: Linear Regression: Absolute Fundamentals
Rating: 4.2 (107 reviews)

Explore COVID-19 positive case prediction with scikit-learn's Linear Regression in Python.

Created bySujithkumar MA

Last updated 7/2025

English

What you'll learn

Machine Learning and Linear Regression: Gain insights into the world of Machine Learning, with a focus on Linear Regression.
Fundamentals of Machine Learning: Grasp the essential concepts and principles that underpin machine learning algorithms.
Starting with Data Science in Python: Learn how to initiate your journey into Data Science using Python as a versatile tool.
Regression Mathematics: Dive into the mathematical foundations of regression analysis, a key technique in predictive modeling

Course content

1 section • 13 lectures • 2h 31m total length

Introduction to Machine Learning25:24
Learn how machine learning models map inputs x to outputs y using a data set of features, trained on data, to predict house prices with linear regression.
Understanding Linear Regression through graphs17:08
Explore univariate linear regression fundamentals through graphs, including the line of best fit, cost function minimization, and predicting outputs from a single input.
Demand vs Price Problem to understand Linear Regression9:26
Learn how simple linear regression models demand as a function of price, derive beta1 and beta0 for a line of best fit, and forecast demand from price.
Introduction to Optimizers7:32
Explore how linear regression fits data by tuning weights and biases to minimize the cost function with gradient descent, from simple to multiple and even polynomial regression.
The Gradient Descent Algorithm17:58
Explore gradient descent to minimize the mean squared error in linear regression by updating weights beta and beta naught with the learning rate alpha using the cost function j(beta).
Downloading the dataset and importing Libraries12:52
Download the dataset for linear regression and import libraries in Python to prepare your data. Read Excel data and use matplotlib for plotting to visualize preprocessing steps.
Reading the dataset6:24
Identify input features and the output target in a dataset, clean empty columns, and prepare a csv for modeling, then explore univariate inputs and their effects on predictions.
Cleaning the data2:07
Clean the data by removing not a number values and empty spaces, drop columns that have none, and rewrite the cleaned data for modeling.
The Model31:50
Split data into training and testing sets. Train a linear regression model using defined input features and output targets, then evaluate and predict outcomes.
Visualising the data1:47
Visualize the input and output with matplotlib to understand the data; use a scatter plot with labeled axes to show how the model fits the line and 96 percent accuracy.
Overfitting and Underfitting6:57
Learn how overfitting and underfitting affect linear regression, with more data to combat underfitting and shorter training time to prevent overfitting.
Concluding Remarks3:11
Explore fitting a line with linear regression by minimizing distance from data points and achieving 96 percent accuracy through data preparation and feature selection for the line of best fit.
[UPDATE] - Why does Linear Regression fail for a classification problem8:37
Explore why linear regression fails for classification and how logistic regression provides a bounded probability model for binary and multi-class problems, illustrated by dog vs cat.

Requirements

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

Description

Greetings, everyone! We're excited to announce that our "Machine Learning Absolute Fundamentals for Linear Regression" course is now open to all students. This course is specifically designed for novice Python developers who are eager to embark on their journey into the world of machine learning. In this instructional module, we will dive into the practical application of a linear regression model, harnessing the power of the Python scikit-learn library, to predict the total number of COVID-19 positive cases within a specific Indian state.

By the end of this course, you will have the knowledge and skills to:

Gain a fundamental understanding of what machine learning is, demystifying its core concepts and principles.
Define what a dataset entails and comprehend its significance in the context of machine learning.
Explore the pivotal functions and roles of machine learning in various domains and applications.
Attain a comprehensive grasp of the concept of linear regression, a foundational machine learning technique for predictive modeling.
Elaborate on the cost function and delve into the concept of the line of greatest fit, often measured by the Mean Squared Error (MSE).
Learn how to effectively manipulate and preprocess your dataset using the versatile pandas library functions, ensuring that it's ready for machine learning.
Master the art of partitioning your data into training and testing subsets, a critical step in model evaluation.
Harness the power of Scikit-Learn to create a robust linear regression model and efficiently train it on your dataset.
Evaluate the performance of your model and make data-driven predictions, enabling you to foresee future COVID-19 positive cases with confidence.
Develop your data visualization skills using Matplotlib, allowing you to communicate your findings effectively through compelling graphical representations.

Diving deeper into the realm of linear regression, we find that this technique leverages linear predictor functions to model relationships within data. The essence of linear regression lies in the estimation of unknown parameters from the available dataset. These models, aptly named linear models, offer valuable insights into the conditional mean of the response variable. Typically, this conditional mean is viewed as an affine function of the explanatory variables, commonly referred to as predictors. Occasionally, in specific applications, other quantiles such as the conditional median are employed.

Who this course is for:

Beginner Python developers who are curious about Machine Learning