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Practical Machine Learning
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Practical Machine Learning

This course is part of multiple programs. Learn more.

Course Cost

Free course

Intermediate

Skill Level

7 Hours

Self-paced Video lessons

This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Data Science Specialization or Data Science: Statistics and Machine Learning Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.

4.5

1,53,417 Enrolled

English

4.5

1,53,417 Enrolled

English

What you'll learn

  • Build and apply prediction functions

  • Implement cross-validation techniques

  • Use the caret package effectively

  • Develop machine learning models

  • Evaluate model performance

  • Handle preprocessing and feature creation

Skills you'll gain

Machine Learning
Prediction Models
Random Forests
Classification Trees
Cross Validation
Feature Engineering
Model Evaluation
R Programming
Caret Package
Statistical Analysis

This course includes:

4.1 Hours PreRecorded video

5 quizzes

Access on Mobile, Tablet, Desktop

FullTime access

Shareable certificate

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Share your certificate with prospective employers and your professional network on LinkedIn.

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Top companies offer this course to their employees

Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.

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There are 4 modules in this course

This comprehensive course focuses on practical applications of machine learning, covering the complete process of building prediction functions. Students learn about training and test sets, overfitting, error rates, and various machine learning methods including regression, classification trees, Naive Bayes, and random forests. The curriculum emphasizes hands-on experience with the caret package in R and includes preprocessing, feature creation, algorithm implementation, and model evaluation.

Prediction, Errors, and Cross Validation

Module 1 · 2 Hours to complete

The Caret Package

Module 2 · 2 Hours to complete

Predicting with trees, Random Forests, & Model Based Predictions

Module 3 · 1 Hours to complete

Regularized Regression and Combining Predictors

Module 4 · 2 Hours to complete

Fee Structure

Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Data Science: Statistics and Machine Learning, Data Science

Reviews

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Faculties

These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.

Practical Machine Learning

Intermediate

Skill Level

7 Hours

Self-paced Video lessons

Course Cost

Free course

Completion

CERTIFICATE

Frequently asked Questions

Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.