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Vinita Silaparasetty
This guided project is about book genre classification using logistic regression in Julia. It is ideal for beginners who do not know what logistic regression is because this project explains these concepts in simple terms.
While you are watching me code, you...
Read more
This guided project is about book genre classification using logistic regression in Julia. It is ideal for beginners who do not know what logistic regression is because this project explains these concepts in simple terms.
While you are watching me code, you...
Read more
This guided project is about book genre classification using logistic regression in Julia. It is ideal for beginners who do not know what logistic regression is because this project explains these concepts in simple terms.
While you are watching me code, you will get a cloud desktop with all the required software pre-installed. This will allow you to code along with me. After all, we learn best with active, hands-on learning.
Special features:
1) Simple explanations of important concepts.
2) Use of images to aid in explanation.
3) Use a real world dataset.
Note: This project works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/cm35el/logistic
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Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Ideal for absolute beginners
Learners code alongside the instructor
Explores machine learning concepts in an easy-to-understand manner
Employs a real-world dataset
Benefits learners seeking a strong foundation in logistic regression
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Save Logistic Regression for Classification using Julia to your list so you can find it easily later:
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Reviews summary
Limited availability with unresponsive instructor
This short beginner-friendly course about logistic regression in Julia may not be easy for all learners to access. Some reviewers mentioned having difficulty getting in touch with the instructor and being unable to continue working on the project after just one day. However, other students were able to follow along with no issue and appreciated the simple explanations.
Simple explanations of concepts
"Simple explanations of important concepts."
Unable to continue project after first day
"Able to start video first day. Video no longer available afterwards."
"Big rip off."
Instructor is difficult to contact
"Not able to contact instructor."
"She is not accessible from LinkedIn or Twitter."
"No email address to contact her."
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 Logistic Regression for Classification using Julia with these
activities:
Attend Virtual Study Sessions
Show steps
Engage with peers and discuss logistic regression concepts
Show steps
-
Join or create a virtual study group
-
Attend sessions and participate in discussions
Complete Practice Problems
Show steps
Practice logistic regression problems to solidify understanding
Show steps
-
Attempt to solve practice problems on your own
-
Review solutions to check your work
Read 'An Introduction to Statistical Learning'
Show steps
Gain a deeper understanding of statistical learning concepts, including logistic regression
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
-
Read the relevant chapters on logistic regression
-
Work through the practice exercises
Two other activities
Expand to see all activities and additional details
Show all five activities
Predict Book Genres
Show steps
Apply logistic regression to a real-world dataset to classify book genres
Show steps
-
Gather a dataset of books with their genres
-
Build a logistic regression model to classify the genres
-
Test and evaluate the accuracy of the model
Participate in Logistic Regression Hackathon
Show steps
Apply logistic regression skills in a competitive setting
Show steps
-
Find a logistic regression hackathon
-
Register and participate in the competition
Attend Virtual Study Sessions
Show steps
Engage with peers and discuss logistic regression concepts
Show steps
Show steps
Show steps
- Join or create a virtual study group
- Attend sessions and participate in discussions
Complete Practice Problems
Show steps
Practice logistic regression problems to solidify understanding
Show steps
Show steps
Show steps
- Attempt to solve practice problems on your own
- Review solutions to check your work
Read 'An Introduction to Statistical Learning'
Show steps
Gain a deeper understanding of statistical learning concepts, including logistic regression
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
Show steps
Show steps
- Read the relevant chapters on logistic regression
- Work through the practice exercises
Two other activities
Expand to see all activities and additional details
Show all five activities
Predict Book Genres
Show steps
Apply logistic regression to a real-world dataset to classify book genres
Show steps
Show steps
Show steps
- Gather a dataset of books with their genres
- Build a logistic regression model to classify the genres
- Test and evaluate the accuracy of the model
Participate in Logistic Regression Hackathon
Show steps
Apply logistic regression skills in a competitive setting
Show steps
Show steps
Show steps
- Find a logistic regression hackathon
- Register and participate in the competition
Career center
Learners who complete Logistic Regression for Classification using Julia will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist is someone who collects, analyzes, and interprets data, and then presents that data in a way that is easy to understand. Logistic regression is a statistical classification method that is used to predict the probability of an event occurring. This makes this course a great way to build upon a foundation in data analysis and prepare for a career in data science.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to make inferences about the world. Logistic regression is a powerful statistical technique that is used to predict the probability of an event occurring. This makes this course a valuable tool for statisticians who want to expand their skillset.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and develop machine learning models, which are algorithms that can learn from data and make predictions. Logistic regression is one of the most common machine learning algorithms and this course provides a solid foundation for those who want to build a career in machine learning engineering.
Market Researcher
Market Researcher
Market Researchers collect and analyze data about consumers and markets. Logistic regression can be used to predict the probability of a consumer purchasing a product, which can be helpful information for market researchers. This course can help Market Researchers develop the skills they need to succeed in their careers.
Business Analyst
Business Analyst
A Business Analyst uses data to help businesses make better decisions. Logistic regression can be used to predict the probability of a customer making a purchase, which can be valuable information for businesses. This course can help Business Analysts build a foundation in logistic regression and prepare for a successful career in business analysis.
Financial Analyst
Financial Analyst
Financial Analysts use data to make investment decisions. Logistic regression can be used to predict the probability of a stock price going up or down, which can be helpful information for financial analysts. This course can help Financial Analysts develop the skills they need to succeed in their careers.
Data Analyst
Data Analyst
Data Analysts work with data to find insights that can help businesses make better decisions. Logistic regression is a commonly used statistical technique that can be used to predict the probability of an event occurring, and is a valuable tool for data analysts. This course can help Data Analysts build a solid foundation in logistic regression, which can increase their value and help them advance their careers.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve business problems. Logistic regression is a statistical technique that is often used in operations research, and this course can help Operations Research Analysts develop the skills they need to succeed in their careers.
Risk Analyst
Risk Analyst
Risk Analysts use data to assess and manage risks. Logistic regression is a statistical technique that can be used to predict the probability of a risk occurring, which can be valuable information for Risk Analysts. This course can help Risk Analysts develop the skills they need to succeed in their careers.
Data Engineer
Data Engineer
Data Engineers design and build systems for storing and processing data. Logistic regression is a statistical technique that is often used to analyze data, and this course can help Data Engineers develop the skills they need to succeed in their careers.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. Logistic regression is a statistical technique that is often used in quantitative finance, and this course can help Quantitative Analysts develop the skills they need to succeed in their careers.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess and manage risks. Logistic regression is a statistical technique that is often used in actuarial science, and this course can help Actuaries develop the skills they need to succeed in their careers.
Computer Scientist
Computer Scientist
Computer Scientists research and develop new computing technologies. Logistic regression is a statistical technique that is often used in computer science, and this course can help Computer Scientists develop the skills they need to succeed in their careers.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. Logistic regression is a statistical technique that is often used in software development, and this course can help Software Engineers develop the skills they need to succeed in their careers.
For more career information including salaries, visit:
OpenCourser.com/course/cm35el/logistic
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
Logistic Regression for Classification using Julia.
Provides a comprehensive introduction to statistical learning methods, including logistic regression. It valuable resource for those new to the field or looking to refresh their knowledge.
Provides a comprehensive overview of machine learning methods, including logistic regression. It valuable resource for those looking to gain a deeper understanding of the theoretical foundations of machine learning.
Provides a comprehensive overview of pattern recognition and machine learning methods, including logistic regression. It valuable resource for those looking to gain a deeper understanding of the field.
Save
Provides a comprehensive overview of data mining methods, including logistic regression. It valuable resource for those looking to gain a deeper understanding of the field.
Provides a comprehensive overview of statistical learning methods, including logistic regression. It valuable resource for those looking to gain a deeper understanding of the field.
Provides a comprehensive overview of statistical methods for machine learning, including logistic regression. It valuable resource for those looking to gain a deeper understanding of the field.
Provides a comprehensive overview of machine learning methods, including logistic regression. It valuable resource for those looking to gain a deeper understanding of the field.
Save
Provides a comprehensive overview of deep learning methods, including logistic regression. It valuable resource for those looking to gain a deeper understanding of the field.
Provides a comprehensive overview of reinforcement learning methods, including logistic regression. It valuable resource for those looking to gain a deeper understanding of the field.
Provides a comprehensive overview of natural language processing methods, including logistic regression. It valuable resource for those looking to gain a deeper understanding of the field.
Save
Provides a comprehensive overview of computer vision methods, including logistic regression. It valuable resource for those looking to gain a deeper understanding of the field.
Provides a comprehensive overview of speech and language processing methods, including logistic regression. It valuable resource for those looking to gain a deeper understanding of the field.
Save
Provides a comprehensive overview of time series analysis and forecasting methods, including logistic regression. It valuable resource for those looking to gain a deeper understanding of the field.
Provides a comprehensive overview of causal inference methods, including logistic regression. It valuable resource for those looking to gain a deeper understanding of the field.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/cm35el/logistic
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Via
Coursera
Institution
Coursera Project Network
Instructor
Vinita Silaparasetty
Good to know
Ideal for absolute beginners
Learners code alongside the instructor
Explores machine learning concepts in an easy-to-understand manner
Employs a real-world dataset
Benefits learners seeking a strong foundation in logistic regression
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