Applied Predictive Modeling

Python Anaconda 2018.12    |    Intermediate
  • 13 Videos | 1h 13m 12s
  • Includes Assessment
  • Earns a Badge
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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

  • Playable
    1. 
    Course Overview
    1m 30s
    UP NEXT
  • Playable
    2. 
    Overview of Predictive Modeling
    5m 55s
  • Locked
    3. 
    Exploratory Data Analysis
    6m 20s
  • Locked
    4. 
    Overview of Regression Methods
    4m 51s
  • Locked
    5. 
    Linear Regression in Python
    7m 2s
  • Locked
    6. 
    Logistic Regression in Python
    5m 56s
  • Locked
    7. 
    Overview of Clustering Methods
    6m 42s
  • Locked
    8. 
    Hierarchical Clustering in Python
    4m 39s
  • Locked
    9. 
    K-Means Clustering in Python
    3m 28s
  • Locked
    10. 
    Overview of Decision Trees and Random Forests
    6m 6s
  • Locked
    11. 
    Decision Trees in Python
    4m 49s
  • Locked
    12. 
    Random Forests in Python
    3m 39s
  • Locked
    13. 
    Exercise: Apply Predictive Models
    6m 47s

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