# Applied Predictive Modeling

Python Anaconda 2018.12    |    Intermediate
• 13 Videos | 1h 13m 12s
• Includes Assessment
Likes 16
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.

## WHAT YOU WILL LEARN

• 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

## IN THIS COURSE

• 1.
Course Overview
• 2.
Overview of Predictive Modeling
• 3.
Exploratory Data Analysis
• 4.
Overview of Regression Methods
• 5.
Linear Regression in Python
• 6.
Logistic Regression in Python
• 7.
Overview of Clustering Methods
• 8.
Hierarchical Clustering in Python
• 9.
K-Means Clustering in Python
• 10.
Overview of Decision Trees and Random Forests
• 11.
Decision Trees in Python
• 12.
Random Forests in Python
• 13.
Exercise: Apply Predictive Models

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