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Data Prep for Machine Learning in Python
Corporate Finance Institute via Coursera
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Overview
Machine learning models rely on good data to produce meaningful insights. For that reason, data prep is one of the most critical skills for machine learning. In this course, youll learn how to import and clean data before populating missing values using imputation. Youll learn how to visualize histograms, scatter charts, and box plots to identify trends of interest before using the analysis to select the most important features. Feature engineering techniques such as one hot encoding, binning and scaling...
Syllabus
- Introduction to Data Prep
- In this course, well learn how to import and clean data before populating missing values using imputation. Well learn how to visualize histograms, scatter charts, and box plots to identify trends of interest before using the analysis to select the most important features. Feature engineering techniques such as one hot encoding, binning and scaling will us transform the structure of our data to produce higher quality machine learning insights.
- Importing & Cleaning Data
- Exploratory Data Analysis
- Train-Test Split (Recap)
- Week 1 Challenge
- Feature Engineering Part 1 - Encoding & Transformation
- Feature Engineering Part 2 - Outliers, Binning, and Scaling
- Feature Selection
- Course Conclusion
- Week 2 Challenge
Corporate Finance Institute via Coursera
5 hours 47 minutes
Paid Certificate Available
English
On-Demand
Advanced
Instructor
CFI (Corporate Finance Institute)
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