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Supervised Learning: Classification

Approx. 11 hours to complete

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Course Summary

This course teaches you the basics of supervised learning and classification. You will learn how to build and evaluate predictive models using various techniques such as decision trees, logistic regression, and k-nearest neighbors.

Key Learning Points

Understand the fundamentals of supervised learning and classification
Build and evaluate predictive models using different algorithms
Learn how to use Python libraries such as scikit-learn

Job Positions & Salaries of people who have taken this course might have

- USA: $113,309
- India: ₹1,050,000
- Spain: €45,000
- USA: $113,309
- India: ₹1,050,000
- Spain: €45,000
- USA: $112,006
- India: ₹1,200,000
- Spain: €42,000
- USA: $113,309
- India: ₹1,050,000
- Spain: €45,000
- USA: $112,006
- India: ₹1,200,000
- Spain: €42,000
- USA: $62,453
- India: ₹400,000
- Spain: €24,000

Learning Outcomes

Understand the principles of supervised learning and classification
Build and evaluate predictive models using various techniques
Apply your knowledge to real-world problems

Prerequisites or good to have knowledge before taking this course

Basic knowledge of Python programming
Familiarity with statistics and probability

Course Difficulty Level

Intermediate

Course Format

Online
Self-paced
Video lectures
Hands-on exercises

Similar Courses

Unsupervised Learning
Deep Learning
Applied Data Science with Python

Notable People in This Field

Andrew Ng
Yann LeCun

Related Books

Description

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.

Outline

Logistic Regression
Welcome
Optional: How to create a project in IBM Watson Studio
Introduction: What is Classification?
Introduction to Logistic Regression
Classification with Logistic Regression
Confusion Matrix, Accuracy, Specificity, Precision, and Recall
Classification Error Metrics: ROC and Precision-Recall Curves
Logistic Regression Lab - Part 1
Logistic Regression Lab - Part 2
Logistic Regression Lab - Part 3
About this course
Optional: Introduction to IBM Watson Studio
Optional: Overview of IBM Watson Studio
Optional: Download data assets
Logistic Regression Demo (Activity)
Summary/Review
Logistic Regression
Logistic Regression Demo
End of Module

K Nearest Neighbors
K Nearest Neighbors for Classification
K Nearest Neighbors Decision Boundary
K Nearest Neighbors Distance Measurement
K Nearest Neighbors with Feature Scaling
K Nearest Neighbors Notebook - Part 1
K Nearest Neighbors Notebook - Part 2
K Nearest Neighbors Notebook - Part 3
K Nearest Neighbors Demo (Activity)
Summary/Review
K Nearest Neighbors
N Nearest Neighbors Demo
End of Module

Support Vector Machines
Introduction to Support Vector Machines
Classification with Support Vector Machines
The Support Vector Machines Cost Function
Regularization in Support Vector Machines
Introduction to Support Vector Machines Gaussian Kernels
Support Vector Machines Gaussian Kernels - Part 1
Support Vector Machines Gaussian Kernels - Part 2
Implementing Support Vector Machines Kernel Models
Support Vector Machines Notebook - Part 1
Support Vector Machines Notebook - Part 2
Support Vector Machines Notebook - Part 3
Support Vector Machines Demo (Activity)
Summary/Review
Support Vector Machines
Support Vector Machines Kernels
Support Vector Machines Demo
End of Module

Decision Trees
Introduction to Decision Trees
Building a Decision Tree
Entropy-based Splitting
Other Decision Tree Splitting Criteria
Pros and Cons of Decision Trees
Decision Trees Notebook - Part 1
Decision Trees Notebook - Part 2
Decision Trees Notebook - Part 3
Decision Trees Demo (Activity)
Summary/Review
Decision Trees
Decision Trees Demo
End of Module

Ensemble Models
Ensemble Based Methods and Bagging - Part 1
Ensemble Based Methods and Bagging - Part 2
Ensemble Based Methods and Bagging - Part 3
Random Forest
Bagging Notebook - Part 1
Bagging Notebook - Part 2
Bagging Notebook - Part 3
Review of Bagging
Overview of Boosting
Adaboost and Gradient Boosting Overview
Adaboost and Gradient Boosting Syntax
Stacking
Boosting Notebook - Part 1
Boosting Notebook - Part 2
Boosting Notebook - Part 3
Bagging Demo (Activity)
Boosting and Stacking Demo (Activity)
Summary/Review
Bagging
Random Forest
Bagging Demo
Boosting and Stacking
Boosting and Stacking Demo
End of Module

Modeling Unbalanced Classes
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
Summary/Review
Modeling Unbalanced Classes
End of Module

Summary of User Reviews

Learn about supervised learning classification in this Coursera course. Users have rated this course highly and find it to be informative and practical. One key aspect that many users thought was good is the course's emphasis on real-world applications.

Pros from User Reviews

Informative and practical
Great emphasis on real-world applications
Well-structured course material
Engaging and interactive content
Useful assignments and quizzes

Cons from User Reviews

Some users found the course to be too basic
Lack of personalized feedback from instructors
Some technical issues with the platform
Limited depth in certain topics
Not enough hands-on coding exercises

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