Applied Predictive Modeling
Python Anaconda
| Intermediate
- 13 videos | 1h 7m 42s
- 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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1m 30s
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5m 55sAfter completing this video, you will be able to recognize characteristics of predictive modeling. FREE ACCESS
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3. Exploratory Data Analysis6m 20sIn this video, you will learn how to use Python and related data analysis libraries to perform exploratory data analysis. FREE ACCESS
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4. Overview of Regression Methods4m 51sUpon completion of this video, you will be able to recognize key features of Linear and Logistic regressions. FREE ACCESS
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5. Linear Regression in Python7m 2sLearn how to apply a linear regression in Python. FREE ACCESS
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6. Logistic Regression in Python5m 56sDuring this video, you will learn how to apply a logistic regression in Python. FREE ACCESS
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7. Overview of Clustering Methods6m 42sAfter completing this video, you will be able to recognize key features of hierarchical and K-Means clustering. FREE ACCESS
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8. Hierarchical Clustering in Python4m 39sIn this video, you will learn how to apply hierarchical clustering with Python. FREE ACCESS
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9. K-Means Clustering in Python3m 28sIn this video, you will learn how to apply K-Means clustering with Python. FREE ACCESS
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10. Overview of Decision Trees and Random Forests6m 6sAfter completing this video, you will be able to recognize key features of Decision Trees and Random Forests. FREE ACCESS
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11. Decision Trees in Python4m 49sDuring this video, you will learn how to apply a Decision Tree in Python. FREE ACCESS
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12. Random Forests in Python3m 39sLearn how to apply a Random Forest in Python. FREE ACCESS
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13. Exercise: Apply Predictive Models6m 47sIn this video, you will apply linear regression, logistic regression, hierarchical clustering, decision trees, and random forests with Python. FREE ACCESS
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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