Python and the Anaconda Package Management System
Different Types of Data Science Problems
Loading the Case Study Data with Jupyter and pandas
Data Quality Assurance and Exploration
Exploring the Financial History Features in the Dataset
Activity 1: Exploring Remaining Financial Features in the Dataset
Introduction
Model Performance Metrics for Binary Classification
Activity 2: Performing Logistic Regression with a New Feature and Creating a Precision-Recall Curve
Introduction
Examining the Relationships between Features and the Response
Univariate Feature Selection: What It Does and Doesn't Do
Building Cloud-Native Applications
Activity 3: Fitting a Logistic Regression Model and Directly Using the Coefficients
Introduction
Estimating the Coefficients and Intercepts of Logistic Regression
Cross Validation: Choosing the Regularization Parameter and Other Hyperparameters
Activity 4: Cross-Validation and Feature Engineering with the Case Study Data
Introduction
Decision trees
Random Forests: Ensembles of Decision Trees
Activity 5: Cross-Validation Grid Search with Random Forest
Introduction
Review of Modeling Results
Dealing with Missing Data: Imputation Strategies
Activity 6: Deriving Financial Insights
Final Thoughts on Delivering the Predictive Model to the Client

Learner Outcomes
By the end of this course, you will have the skills you need to confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from data.

There are no prerequisites for this course.