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Supervised Machine Learning: Classification

Approx. 11 hours to complete

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Course Summary

This course teaches the fundamentals of supervised machine learning classification, including decision trees, random forests, and support vector machines. You will learn how to implement these algorithms in Python and apply them to real-world datasets.

Key Learning Points

Understand the basics of supervised machine learning classification
Learn how to implement decision trees, random forests, and support vector machines in Python
Apply your knowledge to real-world datasets
Gain practical experience through hands-on coding exercises

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

- USA: $112,000
- India: ₹1,021,000
- Spain: €43,000
- USA: $112,000
- India: ₹1,021,000
- Spain: €43,000
- USA: $62,000
- India: ₹481,000
- Spain: €27,000
- USA: $112,000
- India: ₹1,021,000
- Spain: €43,000
- USA: $62,000
- India: ₹481,000
- Spain: €27,000
- USA: $121,000
- India: ₹1,421,000
- Spain: €48,000

Learning Outcomes

Understand the basics of supervised machine learning classification
Implement decision trees, random forests, and support vector machines in Python
Apply your knowledge to real-world datasets

Prerequisites or good to have knowledge before taking this course

Basic knowledge of Python programming
Understanding of fundamental statistics concepts

Course Difficulty Level

Intermediate

Course Format

Online Course
Self-paced Learning
Hands-on Coding Exercises

Similar Courses

Unsupervised Learning
Neural Networks and Deep Learning

Related Education Paths

Notable People in This Field

Andrew Ng
Fei-Fei Li

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

Supervised Machine Learning: Classification is a highly recommended course for those interested in learning about the basics of supervised machine learning. Users have praised the course's comprehensive coverage of the subject matter, providing a solid foundation in the principles of machine learning.

Key Aspect Users Liked About This Course

Comprehensive coverage of the principles of machine learning

Pros from User Reviews

Clear and concise explanations of complex concepts
Great practical exercises to reinforce learning
In-depth coverage of different algorithms and their applications

Cons from User Reviews

Some users found the course too basic and lacking in advanced topics
The pace of the course may be too slow for those with prior experience in machine learning
The course may be too technical for beginners without a strong background in mathematics

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