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Linear Regression & Supervised Learning in Python

EDUCBA via Coursera

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Details

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

2 weeks, 3 hours a week
Sessions

Self-Paced
Level

Beginner
Subtitles

English, Kazakh, Arabic, German, Korean, Portuguese, Japanese, Italian, Indonesian, French, Spanish

Found in

Part of

Applied Python: Web Dev, Machine Learning & Cryptography

Overview

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Build practical skills in linear regression, Python, and supervised machine learning through a structured, project-driven course. Designed for beginners and aspiring data professionals, this course guides you through the complete regression workflow—from identifying a machine learning use case and setting up essential Python libraries to exploring data, training a model, and evaluating its predictions. You’ll use exploratory data analysis (EDA) and graphical techniques to interpret univariate and bivariate distributions, examine relationships between independent and dependent variables, and identify outliers and patterns in variable spread. You’ll then prepare data, construct a simple linear regression model, generate predictions, compare predicted and real-world values, and apply evaluation metrics to assess model accuracy and effectiveness. What makes this course distinctive is its focused progression from data understanding to model validation, supported by practical demonstrations and structured assessments aligned with Bloom’s Taxonomy. By the end, you’ll be able to analyze regression data, build and evaluate a linear regression model in Python, and interpret performance results with confidence. Enroll to establish a practical foundation in Python-based regression analysis and predictive modeling.

Syllabus

Foundations of Linear Regression in Python
- This module introduces learners to the foundational concepts and workflow involved in developing a linear regression model using Python. The lessons walk through identifying the use case, importing the essential libraries, performing exploratory data analysis (EDA), and understanding data behavior through visualizations. Learners will analyze univariate and bivariate distributions and investigate data quality elements such as outliers and variable spread—setting the stage for building reliable and interpretable predictive models.
Modeling and Prediction Techniques
- This module guides learners through the essential steps involved in preparing, training, and evaluating a simple linear regression model in Python. It introduces the importance of understanding variable relationships through bivariate analysis, implements a base model for initial predictions, and interprets model output using prediction comparisons and evaluation metrics. By the end of this module, learners will be able to conduct a basic machine learning run and assess their model’s performance against real-world data.