Data for Machine Learning
Master data preparation and feature engineering for machine learning. Learn to identify, clean, and transform data for optimal ML performance.
Master data preparation and feature engineering for machine learning. Learn to identify, clean, and transform data for optimal ML performance.
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.
4.4
(97 ratings)
8,457 already enrolled
Instructors:
English
پښتو, বাংলা, اردو, 3 more
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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
This course includes:
3.6 Hours PreRecorded video
14 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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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
Instructor
Senior Scientific Advisor at the Alberta Machine Intelligence Institute (Amii), working to nurture productive relationships between industry and academia
working to nurture productive relationships between industry and academia and mainly focused on reinforcement learning, received her Master’s in Computing Science under the supervision of Dr. Richard Sutton, one of the field’s pioneer
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4.4 course rating
97 ratings
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.