Regression Algorithms for Machine Learning

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• Regression Algorithms for Machine Learning 47:00

  • 1: Module 1 Introduction to Machine Learning 04:00
  • 2: Module 2 Linear Regression 04:00
  • 3: Module 3 Logistic Regression 04:00
  • 4: Module 4 Decision Trees and Random Forests 04:00
  • 5: Module 5 Support Vector Machines (SVMs) 04:00
  • 6: Module 6 k-Nearest Neighbors (k-NN) 05:00
  • 7: Module 7 Naive Bayes 04:00
  • 8: Module 8 Clustering 06:00
  • 9: Module 9 Dimensionality Reduction 07:00
  • 10: Module 10 Neural Networks 05:00
  • 11: Assessment -

In this course, students will explore the fundamentals and complexities of regression algorithms, a core topic in machine learning. Emphasis is placed on both linear and non-linear regression methods such as Linear Regression, Polynomial Regression, Ridge Regression, Lasso Regression, Elastic Net, Logistic Regression (for classification scenarios), and Support Vector Regression (SVR). Regression algorithms form a critical part of machine learning by enabling predictive analytics and continuous output modeling, which are vital in fields like finance, healthcare, marketing, and engineering.

Throughout the course, the concept of regression will be revisited through various lenses—starting from the basics of data distribution and correlation, moving into cost functions, gradient descent optimization, overfitting, underfitting, and regularization techniques. The role of regression algorithms in supervised learning will be emphasized, highlighting how they are applied to both small and large datasets across various industries.

Key performance evaluation metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared will be covered to enable effective model assessment. The course will also touch on the differences between regression algorithms and other machine learning models like decision trees, neural networks, and ensemble methods to provide context.

By the end, learners will understand how to choose appropriate regression algorithms for specific machine learning problems and how to interpret the results effectively. The use of regression algorithms will be reinforced across examples involving time series prediction, resource optimization, and risk analysis, ensuring learners understand the broader applicability of these models in machine learning.

This course is suitable for individuals interested in IT and machine learning who want to deepen their knowledge of regression algorithms. It is ideal for data analysts, aspiring data scientists, statisticians, and professionals looking to gain a firm grounding in predictive modeling using regression techniques. A basic understanding of statistics, algebra, and machine learning principles is recommended.

The Regression Algorithms for Machine Learning course is open to everyone. Anyone with a desire to learn about the subject is welcome to enrol in this course without any reservation.

Completing this course prepares learners for roles such as machine learning engineer, data analyst, data scientist, and AI specialist, with a strong foundation in applying regression algorithms in real-world machine learning scenarios.

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