What will you in the Machine Learning: Regression Course
Understand the fundamentals of regression in machine learning.
Learn how to implement simple and multiple linear regression.
Apply techniques like ridge and lasso regression for model regularization.
Explore feature selection strategies and non-parametric methods.
Use k-nearest neighbors and kernel regression for flexible modeling.
Optimize and evaluate models using cross-validation and error analysis.
Gain hands-on experience with Python and Jupyter notebooks
Program Overview
1. Simple Linear Regression
Duration: 2 hours
Fit a line to data using gradient descent and closed-form solutions.
Analyze residuals and understand the impact of outliers.
2. Multiple Regression
Duration: 2 hours
Add multiple features and polynomial terms to your models.
Interpret coefficients and improve prediction accuracy.
3. Assessing Performance
Duration: 2.5 hours
Learn about training/test errors, loss functions, and error metrics.
Understand the bias-variance tradeoff and model complexity.
4. Ridge Regression
Duration: 2 hours
Apply L2 regularization to reduce overfitting.
Use cross-validation to choose the optimal regularization parameter.
5. Feature Selection and Lasso Regression
Duration: 2.5 hours
Explore exhaustive and greedy feature selection methods.
Implement L1 regularization (Lasso) for sparsity and simplicity.
6. Nearest Neighbors and Kernel Regression
Duration: 2 hours
Use non-parametric methods to model complex patterns.
Compare performance with traditional regression techniques.
7. Summary and Final Review
Duration: 1 hour
Recap all regression techniques and applications.
Prepare for future topics in supervised learning and beyond.
Get certificate
Job Outlook
Data Scientists: Strengthen prediction models using advanced regression methods.
Machine Learning Engineers: Build efficient, scalable regression-based applications.
Business Analysts: Use regression to support data-driven strategy decisions.
Researchers: Apply regression in scientific and social science studies.
Product Analysts: Improve forecasting and product performance analytics.
Explore More Learning Paths
Advance your regression and machine learning skills with these carefully curated courses designed to help you model, predict, and analyze complex datasets effectively.
Related Courses
Linear Regression and Modeling Course – Learn to construct and interpret linear regression models for data-driven decision-making.
Supervised Machine Learning: Regression and Classification Course – Explore supervised learning techniques to predict outcomes and classify data accurately.
Regression Models Course – Gain hands-on experience in building, evaluating, and applying regression models to real-world datasets.
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What Is Data Management? – Learn essential data management practices to ensure reliable machine learning analysis.
Specification: Machine Learning: Regression Course
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