Linear Regression and Machine Learning

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Reed Courses Certificate of Completion

Digital certificate - Included

• Linear Regression and Machine Learning 50:00

  • 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 10:00
  • 10: Module 10 Neural Networks 05:00
  • 11: Assessment -

Linear Regression and Machine Learning" covers the theoretical and practical aspects of linear regression as a fundamental machine learning method. The course starts with an introduction to supervised learning, then delves into the concepts of simple and multiple linear regression. Learners will study how linear regression is used to model relationships between variables, interpret coefficients, minimize error functions, and apply gradient descent optimization.

Key components of the course include loss functions, overfitting and underfitting, regularization techniques such as Lasso and Ridge regression, and model evaluation metrics including R-squared and Mean Squared Error. A strong focus is placed on how linear regression forms the basis for many advanced machine learning models and how it can be applied across various IT-related domains, from business intelligence to software engineering.

Throughout the course, the keyword machine learning is revisited frequently, especially in contexts like supervised learning, data preprocessing, and the implementation of scalable algorithms. This ensures that learners grasp the foundational role of linear regression in machine learning pipelines. Other machine learning techniques are compared to linear regression, offering insights into classification, support vector machines, and decision trees to highlight the strengths and limitations of linear models.

By examining real-world scenarios, mathematical principles, and model diagnostics, learners gain an appreciation for how linear regression supports critical decision-making processes and predictive modeling within the machine learning landscape. The course ties machine learning theory with statistical reasoning to create a solid understanding of how linear regression operates in data-driven environments.

This course is ideal for students and professionals in IT seeking to develop a foundational understanding of machine learning with a particular focus on linear regression. It is suitable for individuals aiming to strengthen their skills in algorithm development, statistical modeling, and predictive analysis. Whether you're pursuing data analytics, AI system development, or software design within the IT sector, this course will provide a critical stepping stone in the machine learning journey.

The Linear Regression and 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.

Completion of this course prepares learners for roles such as Machine Learning Analyst, Data Scientist, Predictive Modeler, and AI Engineer, particularly in IT-driven industries where linear regression and machine learning are core components of data strategy.

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