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Mark J Grover,
Miguel Maldonado,
Svitlana (Lana) Kramar,
and
Joseph Santarcangelo
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.
Read more
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.
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/jixqq8/supervised
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What's inside
Syllabus
Logistic Regression
Logistic regression is one of the most studied and widely used classification algorithms, probably due to its popularity in regulated industries and financial settings. Although more modern classifiers might likely output models with higher accuracy, logistic regressions are great baseline models due to their high interpretability and parametric nature. This module will walk you through extending a linear regression example into a logistic regression, as well as the most common error metrics that you might want to use to compare several classifiers and select that best suits your business problem.
Read more
Syllabus
Logistic Regression
Logistic regression is one of the most studied and widely used classification algorithms, probably due to its popularity in regulated industries and financial settings. Although more modern classifiers might likely output models with higher accuracy, logistic regressions are great baseline models due to their high interpretability and parametric nature. This module will walk you through extending a linear regression example into a logistic regression, as well as the most common error metrics that you might want to use to compare several classifiers and select that best suits your business problem.
Read more
K Nearest Neighbors
K Nearest Neighbors is a popular classification method because they are easy computation and easy to interpret. This module walks you through the theory behind k nearest neighbors as well as a demo for you to practice building k nearest neighbors models with sklearn.
Support Vector Machines
This module will walk you through the main idea of how support vector machines construct hyperplanes to map your data into regions that concentrate a majority of data points of a certain class. Although support vector machines are widely used for regression, outlier detection, and classification, this module will focus on the latter.
Decision Trees
Decision tree methods are a common baseline model for classification tasks due to their visual appeal and high interpretability. This module walks you through the theory behind decision trees and a few hands-on examples of building decision tree models for classification. You will realize the main pros and cons of these techniques. This background will be useful when you are presented with decision tree ensembles in the next module.
Ensemble Models
Ensemble models are a very popular technique as they can assist your models be more resistant to outliers and have better chances at generalizing with future data. They also gained popularity after several ensembles helped people win prediction competitions. Recently, stochastic gradient boosting became a go-to candidate model for many data scientists.
This model walks you through the theory behind ensemble models and popular tree-based ensembles.
Modeling Unbalanced Classes
Some classification models are better suited than others to outliers, low occurrence of a class, or rare events. The most common methods to add robustness to a classifier are related to stratified sampling to re-balance the training data. This module will walk you through both stratified sampling methods and more novel approaches to model data sets with unbalanced classes.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Examines core Supervised Machine Learning Classification techniques commonly used in the field
Course led by a team of instructors with industry experience
Designed for aspiring data scientists
Develops and builds on fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics
Includes hands-on exercises and examples for foundational models
Emphasizes best practices for classification
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Save Supervised Machine Learning: Classification to your list so you can find it easily later:
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Reviews summary
Well-received course on supervised machine learning classification
Learners say Supervised Machine Learning: Classification is a well-received course that largely positive reviews. Students appreciate the detailed explanations, thorough walkthroughs, and engaging assignments. The course covers a comprehensive range of topics, including classification algorithms, ensemble methods, and model explanability. Students also value the practical application through labs and assignments. However, some learners noted that the peer-review community could be more active and that the theoretical underpinnings of some algorithms could be explored further.
Labs and coding examples help reinforce learning.
"Well structured training. Lab sessions and assignments are well planned to get clarity on concepts and practical application."
"Great course and very well structured. I'm really impressed with the instructor who give thorough walkthrough to the code."
"The guided demos; although excellent, are flawed."
Course covers a wide range of classification methods.
"This course provided a very useful overview of a wide range of classification techniques using scikit-learn, including the best practice in using the techniques and theoretical underpinning of them."
"Great course, well structured. The presentation of the different methods is very clear and well separated to understand the differences."
"Excellent theoretical and practical understanding in classification algorithms."
Instructors present concepts and algorithms clearly.
"Fantastic presentations and detailed course material make this course really worth it!"
"The course is very well structured, and the explanations very clear."
"The instructor from videos is amazing. Great tutor."
Peer review feedback can be inconsistent.
"The course is very well structured, and the explanations very clear. I would only suggest enhancing the peer-review community since it takes a long time to get a review sometimes."
"Everything is satisfactory except for the peer review section. The initial submission faced challenges, primarily attributed to an unfair assessment by one of the peer reviewers."
"Very good course, full of information. The downside is that passing tests largely require very good knowledge of English."
Theoretical foundations of some algorithms could be explored further.
"The instructor was very good in drilling deep in the code snippets, explaining what every line does clearly, but on theoretic side of every algorithm, I see the handling was poor, lacks the depth and clarity, many times I looked at an external sources to understand how a model works."
"The course content is very great in the coding area and it is very helping. but a shortage that is clear is the theory behind every algorithm, the handling of it wasn't that much perfect."
"there is a lot of information with machine learning strategies and explain how to think in front of results. Super Course ! JSON files made me confusion, it mentions notebook jupiter files but not."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Supervised Machine Learning: Classification with these
activities:
Review Calculus
Show steps
Sharpen your calculus knowledge, which is important for understanding machine learning algorithms and their behavior.
Browse courses on
Calculus
Show steps
-
Review your class notes or online materials
-
Practice solving differential equations
-
Work through practice problems or assignments
Work through Coursera tutorials on classification
Show steps
Enhance your understanding of classification techniques through guided tutorials, providing practical examples and demonstrations.
Show steps
-
Explore the provided Coursera tutorials
-
Review materials and participate in hands-on exercises
-
Complete practice problems or quizzes to reinforce concepts
Read Introduction to Statistical Learning
Show steps
Expand your knowledge and deepen your understanding by reading this comprehensive book, which provides a solid theoretical foundation and practical insights into statistical learning, including classification techniques.
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
-
Obtain a copy of the book
-
Review relevant chapters covering classification
-
Work through practice exercises and problems
Four other activities
Expand to see all activities and additional details
Show all seven activities
Participate in study groups focused on Logistic Regression
Show steps
Enhance your understanding and problem-solving skills through peer interaction, collaborating with others to grasp complex concepts related to Logistic Regression.
Show steps
-
Identify a study group or start your own
-
Meet regularly to discuss course material
-
Work together on exercises or projects
Solve classification problems using Python
Show steps
Strengthen your practical skills in classification by working through Python-based problems, enhancing your ability to apply concepts to real-world scenarios.
Show steps
-
Review the course material on classification algorithms
-
Identify a dataset suitable for classification
-
Develop a Python script to implement the classification algorithm
Attend a workshop on Machine Learning Classification
Show steps
Accelerate your learning by attending a workshop led by experts in the field, gaining hands-on experience and expanding your knowledge of classification techniques.
Show steps
-
Research and identify relevant workshops
-
Register and attend the workshop
-
Actively participate and engage with the instructors
Develop a presentation on Ensemble Models
Show steps
Deepen your understanding and strengthen your communication skills by creating a presentation on Ensemble Models, effectively conveying complex concepts and their applications.
Show steps
-
Review the course material on Ensemble Models
-
Research and gather additional information
-
Develop an outline and structure for your presentation
Review Calculus
Show steps
Sharpen your calculus knowledge, which is important for understanding machine learning algorithms and their behavior.
Browse courses on
Calculus
Show steps
Show steps
Show steps
- Review your class notes or online materials
- Practice solving differential equations
- Work through practice problems or assignments
Work through Coursera tutorials on classification
Show steps
Enhance your understanding of classification techniques through guided tutorials, providing practical examples and demonstrations.
Show steps
Show steps
Show steps
- Explore the provided Coursera tutorials
- Review materials and participate in hands-on exercises
- Complete practice problems or quizzes to reinforce concepts
Read Introduction to Statistical Learning
Show steps
Expand your knowledge and deepen your understanding by reading this comprehensive book, which provides a solid theoretical foundation and practical insights into statistical learning, including classification techniques.
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
Show steps
Show steps
- Obtain a copy of the book
- Review relevant chapters covering classification
- Work through practice exercises and problems
Four other activities
Expand to see all activities and additional details
Show all seven activities
Participate in study groups focused on Logistic Regression
Show steps
Enhance your understanding and problem-solving skills through peer interaction, collaborating with others to grasp complex concepts related to Logistic Regression.
Show steps
Show steps
Show steps
- Identify a study group or start your own
- Meet regularly to discuss course material
- Work together on exercises or projects
Solve classification problems using Python
Show steps
Strengthen your practical skills in classification by working through Python-based problems, enhancing your ability to apply concepts to real-world scenarios.
Show steps
Show steps
Show steps
- Review the course material on classification algorithms
- Identify a dataset suitable for classification
- Develop a Python script to implement the classification algorithm
Attend a workshop on Machine Learning Classification
Show steps
Accelerate your learning by attending a workshop led by experts in the field, gaining hands-on experience and expanding your knowledge of classification techniques.
Show steps
Show steps
Show steps
- Research and identify relevant workshops
- Register and attend the workshop
- Actively participate and engage with the instructors
Develop a presentation on Ensemble Models
Show steps
Deepen your understanding and strengthen your communication skills by creating a presentation on Ensemble Models, effectively conveying complex concepts and their applications.
Show steps
Show steps
Show steps
- Review the course material on Ensemble Models
- Research and gather additional information
- Develop an outline and structure for your presentation
Career center
Learners who complete Supervised Machine Learning: Classification will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists are responsible for collecting, cleaning, and analyzing data to help businesses make informed decisions. This course can help you develop the skills needed to build predictive models that can be used to classify data, which is a key task for Data Scientists. Additionally, this course covers topics such as error metrics and handling unbalanced classes, which are important for ensuring the accuracy and reliability of your models.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, and deploy machine learning models. This course can help you develop the skills needed to train and evaluate classification models, which is a key task for Machine Learning Engineers. Additionally, this course covers topics such as ensemble methods and modeling unbalanced classes, which are important for improving the performance and robustness of your models.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to help businesses make better decisions. This course can help you develop the skills needed to use data to identify trends, patterns, and opportunities. Additionally, this course covers topics such as error metrics and handling unbalanced classes, which are important for ensuring the accuracy and reliability of your analysis.
Business Analyst
Business Analyst
Business Analysts use data to help businesses make better decisions. This course can help you develop the skills needed to use data to identify trends, patterns, and opportunities. Additionally, this course covers topics such as error metrics and handling unbalanced classes, which are important for ensuring the accuracy and reliability of your analysis.
Market Research Analyst
Market Research Analyst
Market Research Analysts collect and analyze data to help businesses understand their customers and make better decisions. This course can help you develop the skills needed to use data to identify trends, patterns, and opportunities. Additionally, this course covers topics such as error metrics and handling unbalanced classes, which are important for ensuring the accuracy and reliability of your analysis.
Statistician
Statistician
Statisticians use data to help businesses make better decisions. This course can help you develop the skills needed to use data to identify trends, patterns, and opportunities. Additionally, this course covers topics such as error metrics and handling unbalanced classes, which are important for ensuring the accuracy and reliability of your analysis.
Risk Analyst
Risk Analyst
Risk Analysts use data to help businesses identify and manage risks. This course can help you develop the skills needed to use data to identify trends, patterns, and opportunities. Additionally, this course covers topics such as error metrics and handling unbalanced classes, which are important for ensuring the accuracy and reliability of your analysis.
Epidemiologist
Epidemiologist
Epidemiologists use data to study the causes and patterns of disease. This course can help you develop the skills needed to use data to identify trends, patterns, and opportunities. Additionally, this course covers topics such as error metrics and handling unbalanced classes, which are important for ensuring the accuracy and reliability of your analysis.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use data to help businesses make better decisions. This course can help you develop the skills needed to use data to identify trends, patterns, and opportunities. Additionally, this course covers topics such as error metrics and handling unbalanced classes, which are important for ensuring the accuracy and reliability of your analysis.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use data to help businesses make better decisions. This course can help you develop the skills needed to use data to identify trends, patterns, and opportunities. Additionally, this course covers topics such as error metrics and handling unbalanced classes, which are important for ensuring the accuracy and reliability of your analysis.
Computer Programmer
Computer Programmer
Computer Programmers write and maintain software applications. This course can help you develop the skills needed to write software applications that can be used to classify data. Additionally, this course covers topics such as error metrics and handling unbalanced classes, which are important for ensuring the accuracy and reliability of your software applications.
Software Engineer
Software Engineer
Software Engineers design, build, and test software applications. This course can help you develop the skills needed to build software applications that can be used to classify data. Additionally, this course covers topics such as error metrics and handling unbalanced classes, which are important for ensuring the accuracy and reliability of your software applications.
Systems Analyst
Systems Analyst
Systems Analysts design and implement computer systems. This course can help you develop the skills needed to design and implement computer systems that can be used for classification tasks. Additionally, this course covers topics such as error metrics and handling unbalanced classes, which are important for ensuring the accuracy and reliability of your systems.
Database Administrator
Database Administrator
Database Administrators manage and maintain databases. This course can help you develop the skills needed to manage and maintain databases that can be used to store and retrieve data for classification models. Additionally, this course covers topics such as error metrics and handling unbalanced classes, which are important for ensuring the accuracy and reliability of your databases.
Information Security Analyst
Information Security Analyst
Information Security Analysts protect computer systems from unauthorized access and attacks. This course can help you develop the skills needed to protect computer systems from unauthorized access and attacks. Additionally, this course covers topics such as error metrics and handling unbalanced classes, which are important for ensuring the accuracy and reliability of your security systems.
For more career information including salaries, visit:
OpenCourser.com/course/jixqq8/supervised
Reading list
We've selected 14 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Supervised Machine Learning: Classification.
Provides a comprehensive introduction to machine learning, covering a wide range of topics including supervised and unsupervised learning, regression, and classification. It valuable resource for anyone interested in learning more about the fundamentals of machine learning.
Provides a comprehensive introduction to statistical learning, covering a wide range of topics including supervised and unsupervised learning, regression, and classification. It valuable resource for anyone interested in learning more about the fundamentals of machine learning.
Save
Provides a comprehensive introduction to pattern recognition and machine learning, covering a wide range of topics including supervised and unsupervised learning, regression, and classification. It valuable resource for anyone interested in learning more about the fundamentals of machine learning.
Provides a comprehensive introduction to statistical learning, covering a wide range of topics including supervised and unsupervised learning, regression, and classification. It valuable resource for anyone interested in learning more about the fundamentals of machine learning.
Provides a practical guide to machine learning using Python, covering a wide range of topics including supervised and unsupervised learning, regression, and classification. It valuable resource for anyone interested in learning more about the fundamentals of machine learning.
Provides a comprehensive introduction to machine learning from a probabilistic perspective, covering a wide range of topics including supervised and unsupervised learning, regression, and classification. It valuable resource for anyone interested in learning more about the fundamentals of machine learning.
Provides a practical guide to machine learning using Python, covering a wide range of topics including supervised and unsupervised learning, regression, and classification. It valuable resource for anyone interested in learning more about the fundamentals of machine learning.
Provides a comprehensive introduction to artificial intelligence, covering a wide range of topics including machine learning, natural language processing, and computer vision. It valuable resource for anyone interested in learning more about the fundamentals of artificial intelligence.
Save
Provides a comprehensive introduction to deep learning, covering a wide range of topics including convolutional neural networks, recurrent neural networks, and generative adversarial networks. It valuable resource for anyone interested in learning more about the fundamentals of deep learning.
Provides a practical guide to machine learning using Python, covering a wide range of topics including supervised and unsupervised learning, regression, and classification. It valuable resource for anyone interested in learning more about the fundamentals of machine learning.
Provides a practical guide to machine learning, covering a wide range of topics including supervised and unsupervised learning, regression, and classification. It valuable resource for anyone interested in learning more about the fundamentals of machine learning.
Provides a practical guide to machine learning for hackers, covering a wide range of topics including supervised and unsupervised learning, regression, and classification. It valuable resource for anyone interested in learning more about the fundamentals of machine learning.
Provides a practical guide to machine learning using R, covering a wide range of topics including supervised and unsupervised learning, regression, and classification. It valuable resource for anyone interested in learning more about the fundamentals of machine learning.
Provides a practical guide to machine learning using Python, covering a wide range of topics including supervised and unsupervised learning, regression, and classification. It valuable resource for anyone interested in learning more about the fundamentals of machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/jixqq8/supervised
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Level
Intermediate
Via
Coursera
Institution
IBM
Instructors
Mark J Grover
Miguel Maldonado
Svitlana (Lana) Kramar
Joseph Santarcangelo
Language
English
Transcripts
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آذربایجان دیلی
This course belongs to a
collection
called
IBM Introduction to Machine Learning. It's comprised of four courses:
- Supervised Machine Learning: Regression
- Exploratory Data Analysis for Machine Learning
- Supervised Machine Learning: Classification (viewing)
- Unsupervised Machine Learning
Good to know
Examines core Supervised Machine Learning Classification techniques commonly used in the field
Course led by a team of instructors with industry experience
Designed for aspiring data scientists
Develops and builds on fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics
Includes hands-on exercises and examples for foundational models
Emphasizes best practices for classification
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