AWS Certified Machine Learning - Specialty (MLS-C01) Cert Prep: 2 Exploratory Data Analysis
18mIntermediate2023-02-27
Authors
Noah Gift
MLOps Expert | Solopreneur | Author | Adjunct Professor | CTO
Course details
Join MLOps expert and CTO Noah Gift to learn all about the exploratory data analysis portion of the AWS Certified Machine Learning – Specialty (MLS-C01) certification. In this course, Noah explains how data preparation, feature engineering, and data visualization are essential for machine learning. He starts by covering data preparation for modeling, detailing how to identify and handle missing data; format, normalize, augment, and scale data; and data labeling tools. He then gets into feature engineering, the process of identifying and extracting features from data sets. Finally, Noah covers data visualization, illustrating graphs and clustering visualizations like scatterplots, histograms, box plots, and elbow plots.
Skills covered
Machine LearningAmazon Web Services (AWS)AmazonCloud ServicesData AnalysisCloud PlatformsCert PrepArtificial Intelligence (AI)Cloud ComputingData ScienceBusiness Analysis and StrategyBusiness Software and Tools
Concepts
0. Introduction
- 01 - Domain 2 - Exploratory data analysis
1. Sanitize and Prepare Data for Modeling
- 02 - Identify and handle missing data, corrupt data, stop words, and more
- 03 - Formatting, normalizing, augmenting, and scaling data
- 04 - Labeled data
- 05 - Data labeling tools
2. Perform Feature Engineering
- 06 - Identify and extract features from data sets
- 07 - Analyze and evaluate feature engineering concepts
3. Analyze and Visualize Data for Machine Learning
- 08 - Graphing - Scatterplot, time series, histogram, and boxplot
- 09 - Clustering - Hierarchical, diagnosing, elbow plot, and cluster size
Conclusion
- 10 - Next steps