Applied Predictive Modeling

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

  • 1m 30s
  • 5m 55s
    After completing this video, you will be able to recognize characteristics of predictive modeling. FREE ACCESS
  • Locked
    3.  Exploratory Data Analysis
    6m 20s
    In this video, you will learn how to use Python and related data analysis libraries to perform exploratory data analysis. FREE ACCESS
  • Locked
    4.  Overview of Regression Methods
    4m 51s
    Upon completion of this video, you will be able to recognize key features of Linear and Logistic regressions. FREE ACCESS
  • Locked
    5.  Linear Regression in Python
    7m 2s
    Learn how to apply a linear regression in Python. FREE ACCESS
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    6.  Logistic Regression in Python
    5m 56s
    During this video, you will learn how to apply a logistic regression in Python. FREE ACCESS
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    7.  Overview of Clustering Methods
    6m 42s
    After completing this video, you will be able to recognize key features of hierarchical and K-Means clustering. FREE ACCESS
  • Locked
    8.  Hierarchical Clustering in Python
    4m 39s
    In this video, you will learn how to apply hierarchical clustering with Python. FREE ACCESS
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    9.  K-Means Clustering in Python
    3m 28s
    In 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 Forests
    6m 6s
    After completing this video, you will be able to recognize key features of Decision Trees and Random Forests. FREE ACCESS
  • Locked
    11.  Decision Trees in Python
    4m 49s
    During this video, you will learn how to apply a Decision Tree in Python. FREE ACCESS
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    12.  Random Forests in Python
    3m 39s
    Learn how to apply a Random Forest in Python. FREE ACCESS
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    13.  Exercise: Apply Predictive Models
    6m 47s
    In this video, you will apply linear regression, logistic regression, hierarchical clustering, decision trees, and random forests with Python. FREE ACCESS

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