# Course details

Applied Predictive Modeling

### Applied Predictive Modeling

Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level

Overview/Description

In this course, you will explore machine learning predictive modeling and commonly used models like regressions, clustering, and Decision Trees that are applied in Python with the scikit-learn package. Begin this 13-video course with an overview of predictive modeling and recognize its characteristics. You will then use Python and related data analysis libraries including NumPy, Pandas, Matplotlib, and Seaborn, to perform exploratory data analysis. Next, you will examine regression methods, recognizing the key features of Linear and Logistic regressions, then apply both a linear and a logistic regression with Python. Learn about clustering methods, including the key features of hierarchical clustering and K-Means clustering, then learn how to apply hierarchical clustering and K-Means clustering with Python. Examine the key features of Decision Trees and Random Forests, then apply a Decision Tree and a Random Forest with Python. In the concluding exercise, learners will be asked to apply linear regression, logistic regression, hierarchical clustering, Decision Trees, and Random Forests with Python.

Expected Duration (hours)
1.1

Lesson Objectives

Applied Predictive Modeling

• Course Overview
• recognize characteristics of predictive modeling
• use Python and related data analysis libraries to perform exploratory data analysis
• recognize key features of Linear and Logistic regressions
• apply a linear regression with Python
• apply a logistic regression with Python
• recognize key features of hierarchical clustering and K-Means clustering
• apply hierarchical clustering with Python
• apply K-Means clustering with Python
• recognize key features of Decision Trees and Random Forests
• apply a Decision Tree with Python
• apply a Random Forest with Python
• apply linear regression, logistic regression, hierarchical clustering, Decision Trees, and Random Forests with Python
• Course Number:
it_mlapmldj_01_enus

Expertise Level
Intermediate