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Data for Machine Learning
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Data for Machine Learning

This course is part of Machine Learning: Algorithms in the Real World.

Course Cost

Free course

Intermediate

Skill Level

10 Hours

Self-paced 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 Machine Learning: Algorithms in the Real World 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.

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4.4

8,457 Enrolled

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English

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4.4

8,457 Enrolled

olive-leaves-logo

English

What you'll learn

  • Prepare and clean data for machine learning applications

  • Implement effective feature engineering techniques

  • Handle data quality issues and missing values

  • Identify and address data bias and imbalance

  • Optimize data transformation for ML models

Skills you'll gain

Data Preprocessing
Feature Engineering
Machine Learning
Python Programming
Statistical Analysis
Data Warehousing
Data Quality
Transfer Learning
Bias Detection
Data Transformation

This course includes:

3.6 Hours PreRecorded video

14 quizzes

Access on Mobile, Tablet, Desktop

FullTime access

Shareable certificate

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Certificate
Certificate

Get a Completion Certificate

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

CREATED BY

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PROVIDED BY

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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 data preparation for machine learning applications. Students learn to identify, clean, and transform raw data into effective features. Topics include data quality assessment, handling missing values, feature engineering, bias detection, and managing imbalanced datasets. The curriculum emphasizes practical skills through hands-on programming assignments and real-world case studies.

What Does Good Data look like?

Module 1 · 2 Hours to complete

Preparing your Data for Machine Learning Success

Module 2 · 2 Hours to complete

Feature Engineering for MORE Fun & Profit

Module 3 · 5 Hours to complete

Bad Data

Module 4 · 1 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: Machine Learning: Algorithms in the Real World

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.

Data for Machine Learning

Intermediate

Skill Level

10 Hours

Self-paced 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.