# Applied Predictive Modeling

Python Anaconda    |    Intermediate
• 13 videos | 1h 7m 42s
• Includes Assessment
• Earns a Badge
Rating 4.1 of 21 users (21)
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

• After completing this video, you will be able to recognize characteristics of predictive modeling.
• 3.  Exploratory Data Analysis
In this video, you will learn how to use Python and related data analysis libraries to perform exploratory data analysis.
• 4.  Overview of Regression Methods
Upon completion of this video, you will be able to recognize key features of Linear and Logistic regressions.
• 5.  Linear Regression in Python
Learn how to apply a linear regression in Python.
• 6.  Logistic Regression in Python
During this video, you will learn how to apply a logistic regression in Python.
• 7.  Overview of Clustering Methods
After completing this video, you will be able to recognize key features of hierarchical and K-Means clustering.
• 8.  Hierarchical Clustering in Python
In this video, you will learn how to apply hierarchical clustering with Python.
• 9.  K-Means Clustering in Python
In this video, you will learn how to apply K-Means clustering with Python.
• 10.  Overview of Decision Trees and Random Forests
After completing this video, you will be able to recognize key features of Decision Trees and Random Forests.
• 11.  Decision Trees in Python
During this video, you will learn how to apply a Decision Tree in Python.
• 12.  Random Forests in Python
Learn how to apply a Random Forest in Python.
• 13.  Exercise: Apply Predictive Models
In this video, you will apply linear regression, logistic regression, hierarchical clustering, decision trees, and random forests with Python.

## EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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