What you'll get
- 5+ Hours
- 2 Courses
- Course Completion Certificates
- Self-paced Courses
- Technical Support
- Case Studies
Synopsis
- Core concepts of linear regression and its role in data analysis
- How to build linear regression models using Python
- Data preprocessing techniques
- Model evaluation methods and performance optimization strategies
- Advanced concepts like regularization, feature selection, and multicollinearity handling
- Practical application of linear regression on real-world datasets
- How to develop end-to-end prediction models
- Data preparation and feature engineering skills
- Effective data visualization techniques
- Strong understanding of the scikit-learn machine learning library
Content
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Project on Linear Regression in Python | 2h 28m | ✔ | View Curriculum |
| House Price Prediction using Linear Regression | 3h 2m | ✔ | View Curriculum |
Description
This course provides a simple, practical, and hands-on introduction to linear regression and its use in real-world data science projects. Designed for beginners and aspiring data professionals, it provides a clear, step-by-step learning path to master linear regression techniques in Python.
You will begin by understanding the project objectives, scope, and key tools used in data analysis. From there, the course focuses on essential Python libraries. It guides you through core exploratory data analysis techniques, including graphical univariate analysis, boxplots for outlier detection, and bivariate analysis to uncover relationships between variables.
As the course progresses, you will apply machine learning concepts to build, train, and evaluate linear regression models. Through hands-on exercises, you will learn to prepare data, apply regression algorithms, make predictions, and evaluate model performance with ease and confidence.
By the end of this course, you will analyze datasets, build predictive models, and extract meaningful insights to support data-driven decision-making. Whether you are a student, analyst, or professional seeking to strengthen your analytical skills, this course will help you effectively apply linear regression in Python-based workflows.
Requirements
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Basic knowledge of Python programming
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Basic statistics and machine learning knowledge
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Familiarity with NumPy, pandas, and Matplotlib
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Ability to work with Jupyter Notebook or any Python IDE
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Comfort with handling CSV or Excel datasets
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Fundamental knowledge of algebra and calculus
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Interest in data analysis and problem-solving.
Target Audience
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Data analysts, data scientists, and business professionals using data for decision-making
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Students in data science, statistics, or related fields
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Professionals transitioning into analytics roles
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Python learners interested in linear regression
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Engineers, IT professionals, and technical managers exploring data analytics
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Anyone wanting to understand the basics of data and analytics.
