Exploratory Data Analysis for Machine Learning
Master data cleaning, feature engineering, and statistical analysis for machine learning. Learn EDA techniques with IBM's comprehensive course.
Master data cleaning, feature engineering, and statistical analysis for machine learning. Learn EDA techniques with IBM's comprehensive course.
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 IBM Machine Learning Professional Certificate 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.
4.6
(1,780 ratings)
1,14,073 already enrolled
Instructors:
English
پښتو, বাংলা, اردو, 2 more
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What you'll learn
Retrieve and clean data from multiple sources and formats
Apply feature selection and engineering techniques
Conduct comprehensive exploratory data analysis
Perform statistical hypothesis testing
Handle missing values and detect outliers
Implement various feature scaling methods
Skills you'll gain
This course includes:
3.2 Hours PreRecorded video
12 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
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There are 5 modules in this course
This comprehensive course introduces learners to the fundamentals of exploratory data analysis (EDA) for machine learning. Students learn essential techniques for data retrieval from various sources including SQL, NoSQL databases, APIs, and cloud platforms. The curriculum covers data cleaning methods, feature selection and engineering, handling missing values and outliers, and feature scaling. Advanced topics include statistical hypothesis testing and inferential statistics. Through hands-on labs and practical exercises, learners develop skills in using Python for data analysis and preparation for machine learning models.
A Brief History of Modern AI and its Applications
Module 1 · 1 Hours to complete
Retrieving and Cleaning Data
Module 2 · 2 Hours to complete
Exploratory Data Analysis and Feature Engineering
Module 3 · 4 Hours to complete
Inferential Statistics and Hypothesis Testing
Module 4 · 3 Hours to complete
(Optional) HONORS Project
Module 5 · 1 Hours to complete
Fee Structure
Instructors
Pioneering Data Scientist Bridging AI Research and Education
Dr. Joseph Santarcangelo, a Data Scientist at IBM, brings a unique blend of academic excellence and practical expertise to the field of data science and artificial intelligence. With a Ph.D. in Electrical Engineering, his groundbreaking research focused on the intersection of machine learning, signal processing, and computer vision to understand how video content influences human cognitive processes. At IBM, he has established himself as a prominent educator and course developer, creating comprehensive learning materials that have reached hundreds of thousands of students worldwide. His teaching portfolio encompasses a wide range of technical subjects, from foundational Python programming to advanced topics in artificial intelligence, machine learning, and computer vision. Santarcangelo's ability to translate complex technical concepts into accessible learning experiences has made him an influential figure in data science education, maintaining consistently high ratings from learners while continuing to push the boundaries of applied machine learning and artificial intelligence research.
Passionate Data Science Educator and Developer
Svitlana (Lana) Kramar is a dedicated Data Science Content Developer at IBM, currently pursuing her Master’s Degree in Data Science and Analytics at the University of Calgary. With a strong commitment to making data science accessible, she focuses on creating educational content that empowers learners to harness the power of data analytics and machine learning. Lana enjoys traveling and immersing herself in new languages and cultures, which enriches her perspective on global data challenges. Her enthusiasm for teaching drives her to share innovative data science tools and techniques, aiming to improve everyday tasks and enhance overall quality of life. Through her work at IBM and contributions to online learning platforms, she continues to inspire others in the field of data science.
Testimonials
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4.6 course rating
1,780 ratings
Frequently asked questions
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