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Supervised Machine Learning: Classification
  • Level Professional
  • المدة 25 ساعات hours
  • الطبع بواسطة IBM
  • Offered by

عن

This course introduces you to one of the main types of modeling families of supervised Machine Learning: Classification. You will learn how to train predictive models to classify categorical outcomes and how to use error metrics to compare across different models. The hands-on section of this course focuses on using best practices for classification, including train and test splits, and handling data sets with unbalanced classes. By the end of this course you should be able to: -Differentiate uses and applications of classification and classification ensembles -Describe and use logistic regression models -Describe and use decision tree and tree-ensemble models -Describe and use other ensemble methods for classification -Use a variety of error metrics to compare and select the classification model that best suits your data -Use oversampling and undersampling as techniques to handle unbalanced classes in a data set   Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Classification techniques in a business setting.   What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.

الوحدات

Everything you need to know before starting this course

    • 1 Videos
    • 2 Readings
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1 Videos

  • Welcome

2 Readings

  • About this course
  • Optional: Download data assets

Logistic Regression: Introduction to Classification and Error Metrics

    • 8 Videos
    • 1 Assignment
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1 Assignment

  • Logistic Regression

8 Videos

  • Introduction: What is Classification?
  • Introduction to Logistic Regression
  • Classification with Logistic Regression
  • Logistic Regression with Multi-Classes
  • Implementing Logistic Regression Models
  • Confusion Matrix, Accuracy, Specificity, Precision, and Recall
  • Classification Error Metrics: ROC and Precision-Recall Curves
  • Implementing the Calculation of ROC and Precision-Recall Curves

Logistic Regression Labs

    • 3 Videos
    • 1 Readings
    • 2 ExternalTool
    • 1 Assignment
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1 Assignment

  • Logistic Regression Labs

2 External Tool

  • Demo Lab: Logistic Regression
  • Practice Lab: Logistic Regression

3 Videos

  • [Optional] Logistic Regression Lab - Part 1
  • [Optional] Logistic Regression Lab - Part 2
  • [Optional] Logistic Regression Lab - Part 3

1 Readings

  • [Optional] Download Assets for Demo Lab: Logistic Regression

End of module review & evaluation

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Module 1 Graded Quiz Logisitic Regression

1 Readings

  • Summary/Review

K Nearest Neighbors

    • 5 Videos
    • 1 Assignment
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1 Assignment

  • K Nearest Neighbors

5 Videos

  • K Nearest Neighbors for Classification
  • K Nearest Neighbors Decision Boundary
  • K Nearest Neighbors Distance Measurement
  • K Nearest Neighbors Pros and Cons
  • K Nearest Neighbors with Feature Scaling

K Nearest Neighbors Labs

    • 3 Videos
    • 2 ExternalTool
    • 1 Assignment
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1 Assignment

  • K Nearest Neighbors Labs

2 External Tool

  • Demo Lab: K Nearest Neighbors
  • Practice Lab: K Nearest Neighbors

3 Videos

  • [Optional] K Nearest Neighbors Notebook - Part 1
  • [Optional] K Nearest Neighbors Notebook - Part 2
  • [Optional] K Nearest Neighbors Notebook - Part 3

End of module review & evaluation

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Module 2 Graded Quiz - KNN

1 Readings

  • Summary/Review

Support Vector Machines

    • 4 Videos
    • 1 Assignment
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1 Assignment

  • Support Vector Machines

4 Videos

  • Introduction to Support Vector Machines
  • Classification with Support Vector Machines
  • The Support Vector Machines Cost Function
  • Regularization in Support Vector Machines

Support Vector Machines Kernels

    • 5 Videos
    • 1 Assignment
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1 Assignment

  • Support Vector Machines Kernels

5 Videos

  • Introduction to Support Vector Machines Gaussian Kernels
  • Support Vector Machines Gaussian Kernels - Part 1
  • Support Vector Machines Gaussian Kernels - Part 2
  • Support Vector Machines Workflow
  • Implementing Support Vector Machines Kernal Models

Support Vector Machines Labs

    • 3 Videos
    • 2 ExternalTool
    • 1 Assignment
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1 Assignment

  • Support Vector Machines Labs

2 External Tool

  • Demo Lab: Support Vector Machines
  • Practice Lab: Support Vector Machines

3 Videos

  • [Optional] Support Vector Machines Notebook - Part 1
  • [Optional] Support Vector Machines Notebook - Part 2
  • [Optional] Support Vector Machines Notebook - Part 3

End of module review

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Module 3 Graded Quiz: Support Vector Machines

1 Readings

  • Summary/Review

Decision Trees

    • 6 Videos
    • 1 Assignment
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1 Assignment

  • Decision Trees

6 Videos

  • Overview of Classifiers
  • Introduction to Decision Trees
  • Building a Decision Tree
  • Entropy-based Splitting
  • Other Decision Tree Splitting Criteria
  • Pros and Cons of Decision Trees

Decision Trees Labs

    • 3 Videos
    • 1 Readings
    • 2 ExternalTool
    • 1 Assignment
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1 Assignment

  • Decision Trees Labs

2 External Tool

  • Demo Lab: Decision Trees
  • Practice Lab: Decision Trees

3 Videos

  • [Optional] Decision Trees Notebook - Part 1
  • [Optional] Decision Trees Notebook - Part 2
  • [Optional] Decision Trees Notebook - Part 3

1 Readings

  • [Optional] Download Assets for Demo Lab: Decision Trees

End of module review

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Module 4 Graded Quiz: Decision Trees

1 Readings

  • Summary/Review

Ensemble Based Methods and Bagging

    • 3 Videos
    • 1 Assignment
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1 Assignment

  • Bagging

3 Videos

  • Ensemble Based Methods and Bagging - Part 1
  • Ensemble Based Methods and Bagging - Part 2
  • Ensemble Based Methods and Bagging - Part 3

Random Forest

    • 1 Videos
    • 1 ExternalTool
    • 1 Assignment
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1 Assignment

  • Random Forest

1 External Tool

  • Practice Lab: Random Forest

1 Videos

  • Random Forest

Bagging Labs

    • 3 Videos
    • 1 Readings
    • 2 ExternalTool
    • 1 Assignment
Show info about module content

1 Assignment

  • Bagging Labs

2 External Tool

  • Demo Lab: Bagging
  • Practice Lab: Bagging

3 Videos

  • [Optional] Bagging Notebook - Part 1
  • [Optional] Bagging Notebook - Part 2
  • [Optional] Bagging Notebook - Part 3

1 Readings

  • [Optional] Download Assets for Demo Lab: Bagging

Boosting and Stacking

    • 5 Videos
    • 1 Assignment
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1 Assignment

  • Boosting and Stacking

5 Videos

  • Review of Bagging
  • Overview of Boosting
  • Adaboost and Gradient Boosting Overview
  • Adaboost and Gradient Boosting Syntax
  • Stacking

Boosting and Stacking Labs

    • 3 Videos
    • 1 Readings
    • 4 ExternalTool
    • 1 Assignment
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1 Assignment

  • Boosting and Stacking Labs

4 External Tool

  • Demo Lab: Boosting and Stacking
  • Practice Lab: Ada Boost
  • Practice Lab: Stacking For Classification with Python
  • Practice Lab: (Optional) Gradient Boosting

3 Videos

  • [Optional] Boosting Notebook - Part 1
  • [Optional] Boosting Notebook - Part 2
  • [Optional] Boosting Notebook - Part 3

1 Readings

  • [Optional] Download Assets for Demo Lab: Boosting and Stacking

End of module review & evaluation

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Module 5 Graded Quiz

1 Readings

  • Summary/Review

Model Interpretability

    • 4 Videos
    • 1 ExternalTool
    • 1 Assignment
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1 Assignment

  • Practice: Model interpretability

1 External Tool

  • Practice Lab: Model Interpretability

4 Videos

  • Model Interpretability
  • Examples of Self-Interpretable and Non-Self-Interpretable Models
  • Model-Agnostic Explanations
  • Surrogate Models

Modeling Unbalanced Classes

    • 6 Videos
    • 1 ExternalTool
    • 1 Assignment
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1 Assignment

  • Modeling Unbalanced Classes

1 External Tool

  • Practice Lab: Modeling Imbalanced Classes

6 Videos

  • Introduction to Unbalanced Classes
  • Upsampling and Downsampling
  • Modeling Approaches: Weighting and Stratified Sampling
  • Modeling Approaches: Random and Synthetic Oversampling
  • Modeling Approaches: Nearing Neighbor Methods
  • Modeling Approaches: Blagging

End of Module Review

    • 1 Readings
    • 1 PeerReview
    • 1 Assignment
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1 Assignment

  • Module 6 Graded Quiz

1 Peer Review

  • Course Final Project

1 Readings

  • Summary/Review

Auto Summary

Explore supervised machine learning with a focus on classification in this Coursera course, led by expert instructors. Delve into logistic regression, decision trees, and ensemble methods, and master techniques for handling unbalanced classes. Ideal for aspiring data scientists with Python and data analysis skills, this professional-level course spans 1500 minutes and is available with a Starter subscription.

Mark J Grover
Instructors

Mark J Grover

Svitlana (Lana) Kramar
Instructors

Svitlana (Lana) Kramar

Joseph Santarcangelo
Instructors

Joseph Santarcangelo

Miguel Maldonado
Instructors

Miguel Maldonado