Supervised Learning Models

Machine Learning    |    Beginner
  • 13 videos | 33m 13s
  • Includes Assessment
  • Earns a Badge
Rating 4.5 of 184 users Rating 4.5 of 184 users (184)
Supervised learning is one of the most popular techniques in machine learning. Explore supervised learning models and how to use them to solve problems.

WHAT YOU WILL LEARN

  • Describe the difference between classification and regression models and the use for each of them
    Describe how decision trees can be applied regression problems
    Describe the cart decision tree learning algorithm and how it's different from c4.5
    Describe the random forests machine learning
    Use scikit learn to build a random forest model in python
    Describe the logistic regression model
    Use scikit learn to fit a logistic regression model
  • Describe support vector machine models
    Describe how to use kernel methods with support vector machines to model more complex data
    Use scikit learn to train and support vector machines in python
    Describe the naïve bayes classifiers and how to train them
    Use scikit learn to fit a naïve bayes classifier in python
    Describe different supervised learning models in python

IN THIS COURSE

  • 3m 8s
    After completing this video, you will be able to describe the difference between classification and regression models and when to use each of them. FREE ACCESS
  • 1m 55s
    Upon completion of this video, you will be able to describe how decision trees can be applied to regression problems. FREE ACCESS
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    3.  CART Decision Tree Learning
    2m 8s
    Upon completion of this video, you will be able to describe the CART decision tree learning algorithm and how it differs from C4.5. FREE ACCESS
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    4.  Random Forests
    1m 56s
    Upon completion of this video, you will be able to describe the random forests machine learning algorithm. FREE ACCESS
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    5.  Random Forests with Python
    3m 28s
    During this video, you will learn how to use SciKit Learn to build a random forest model in Python. FREE ACCESS
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    6.  Logistic Regression Model
    2m 7s
    Upon completion of this video, you will be able to describe the logistic regression model. FREE ACCESS
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    7.  Logistic Regression in Python
    3m 14s
    In this video, you will learn how to use SciKit Learn to fit a logistic regression model. FREE ACCESS
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    8.  Support Vector Machines (SVM)
    1m 32s
    After completing this video, you will be able to describe support vector machine models. FREE ACCESS
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    9.  Kernel Methods for SVMs
    1m 42s
    Upon completion of this video, you will be able to describe how to use support vector machines with kernel methods to model more complex data. FREE ACCESS
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    10.  Support Vector Machines in Python
    3m 45s
    Learn how to use SciKit Learn to train and support vector machines in Python. FREE ACCESS
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    11.  Naïve Bayes Classifiers
    2m 39s
    After completing this video, you will be able to describe Naïve Bayes classifiers and how to train them. FREE ACCESS
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    12.  Naïve Bayes Classifiers in Python
    3m 40s
    Learn how to use SciKit Learn to fit a Naive Bayes classifier in Python. FREE ACCESS
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    13.  Exercise: Supervised Learning in Python
    1m 59s
    Upon completion of this video, you will be able to describe different supervised learning models. FREE ACCESS

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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