Overview

Machine Learning – Regression

Machine Learning plays a pivotal role in predictive analytics, and regression is a fundamental technique within the field. This two-day course is designed for beginners and focuses on teaching the core concepts and practical application of regression algorithms. Participants will learn how to build, evaluate, and interpret regression models, making it an ideal starting point for a data science journey.

Skills Covered

By the end of this course, participants will:

  • Understand the concept of regression and its importance in machine learning.
  • Be proficient in both linear and nonlinear regression techniques.
  • Gain practical experience in data preprocessing and feature engineering for regression.
  • Be able to evaluate and interpret regression models using relevant metrics.
  • Develop the skills to apply regression to real-world problems.
  • Feel confident to continue exploring more advanced machine learning techniques.

Prerequisites

There are no prerequisites required to attend this course.

Target Audience

  • Beginner level courses for aspiring Data Scientist.
  • Anyone who is interested in data science.

Course Curriculum

Module 1: Introduction to Linear Regression

  • Understanding regression in machine learning.
  • Introduction to linear regression: Simple and multiple linear regression.
  • Data preprocessing and feature engineering for regression.
  • Model building and training for linear regression.
  • Evaluation metrics: Mean Squared Error (MSE), R-squared, and more.
  • Practical exercises and projects with linear regression.

Module 2: Nonlinear Regression Techniques

  • Introduction to nonlinear regression techniques.
  • Polynomial regression and regularization.
  • Decision tree regression.
  • Support Vector Regression (SVR) and K-Nearest Neighbors (KNN) regression.
  • Model evaluation for nonlinear regression.
  • Hands-on projects and application of nonlinear regression

Dates & Locations

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Exam & Certification

This course is not associated with any Certification.

Training & Certification Guide

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