In this course, AWS Certified Machine Learning - Specialty (MLS-C01): Exploratory Data Analysis, you’ll learn to prepare, sanitize, and visualize data. First, you’ll explore how to handle missing data, detect outliers, format inconsistent data, and augment imbalanced data. Next, you’ll discover how to extract features from various datasets using feature engineering techniques like encoding, normalization, and dimensionality reduction. Finally, you’ll learn how to effectively visualize data and create various types of graphs and charts. When you’re finished with this course, you’ll have the skills and knowledge to effectively prepare the data needed to develop a high-quality machine-learning model that produces accurate predictions.
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About the author
Saravanan Dhandapani
I have worked in IT design, development, and architecture for over a decade for some of the top fortune 100 companies. I have designed and architected enterprise applications and developed scalable and portable software. I am a Google Certified Professional Architect. Critical areas where I have worked are architecture and design using Java, ESB, Tomcat, ReactJS, JavaScript, Linux, Oracle, SVN, GIT, and so on, and cloud technologies, including AWS and GCP.
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