Applied Predictive Modeling
Python Anaconda 2018.12
| Intermediate
- 13 Videos | 1h 13m 12s
- Includes Assessment
- Earns a Badge
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
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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
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1.Course Overview1m 30sUP NEXT
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2.Overview of Predictive Modeling5m 55s
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3.Exploratory Data Analysis6m 20s
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4.Overview of Regression Methods4m 51s
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5.Linear Regression in Python7m 2s
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6.Logistic Regression in Python5m 56s
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7.Overview of Clustering Methods6m 42s
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8.Hierarchical Clustering in Python4m 39s
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9.K-Means Clustering in Python3m 28s
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10.Overview of Decision Trees and Random Forests6m 6s
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11.Decision Trees in Python4m 49s
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12.Random Forests in Python3m 39s
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13.Exercise: Apply Predictive Models6m 47s
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
Skillsoft is providing you the opportunity to earn a digital badge upon successful completion of this course, which can be shared on any social network or business platform
Digital badges are yours to keep, forever.
