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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.
By the end of this course you should be able to:
-Differentiate uses and applications of classification and classification ensembles
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
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
Traffic lights
Traffic lights
Read about what's good
what should give you pause 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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Reviews summary
Classification models: a practical approach
According to learners, this course provides a solid foundation in Supervised Machine Learning Classification, focusing on practical application in a business setting. Students particularly appreciate the coverage of essential algorithms like Logistic Regression, Decision Trees, and Ensemble Models, along with valuable insights into handling unbalanced classes. The hands-on demos and exercises are highlighted as key for cementing understanding. While the course is comprehensive, some prospective students should be aware that strong prerequisites in Python, Calculus, Linear Algebra, and Statistics are crucial for success, as the pace can be demanding. Overall, it's considered a highly relevant offering for aspiring data scientists.
Requires solid foundational knowledge in math and Python.
"Definitely brush up on your Python, Linear Algebra, and Calculus before starting; they are fundamental."
"I struggled a bit with the pace since my statistics background wasn't as strong as recommended."
"The course assumes a good understanding of programming in Python, which is fair given the topic."
Lays a strong base for aspiring data scientists.
"As an aspiring data scientist, this course provided an excellent starting point for supervised ML."
"It's a great course if you want to acquire hands-on experience with classification in a business setting."
"I feel much more confident now about applying classification techniques in my future roles."
Addresses crucial real-world issues like imbalanced datasets.
"The module on modeling unbalanced classes was extremely valuable for practical data science applications."
"Learning about oversampling and undersampling techniques felt very relevant to real business problems."
"I was happy to see the inclusion of robust methods for dealing with low occurrence classes."
Offers valuable practical application through coding exercises.
"The practical demos for building models with sklearn were incredibly helpful for me."
"I appreciated the hands-on sections that truly reinforced the theoretical concepts."
"Applying the train and test splits in code made the theory much more concrete."
Explores a wide range of essential classification algorithms.
"The course covers a really good range of classification models, from logistic regression to ensemble methods."
"I found the explanations of Decision Trees and Support Vector Machines particularly clear and easy to grasp."
"This course helped me understand the different use cases for various classification algorithms."
Some learners desire more in-depth content or a slower pace.
"I felt that some topics were covered a bit too quickly, requiring me to do outside research."
"While comprehensive, I wish there was slightly more depth in the theoretical explanations for certain algorithms."
"The course moves at a good clip, which is fine if you're comfortable, but can be fast for absolute beginners in ML."
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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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Rating
4.8
Effort
Approx. 24 hours
Level
Intermediate
Via
Coursera
Institution
IBM
Instructors
Mark J Grover
Miguel Maldonado
Svitlana (Lana) Kramar
Joseph Santarcangelo
Language
English
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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
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