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Vinita Silaparasetty
This guided project is for those who want to learn how to use Julia for linear regression and multiple linear regression. You will learn what linear regression is, how to build linear regression models in Julia and how to test the performance of your model.
Read more
This guided project is for those who want to learn how to use Julia for linear regression and multiple linear regression. You will learn what linear regression is, how to build linear regression models in Julia and how to test the performance of your model.
Read more
This guided project is for those who want to learn how to use Julia for linear regression and multiple linear regression. You will learn what linear regression is, how to build linear regression models in Julia and how to test the performance of your model.
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) Work with real-world stock market data.
2) Best practices and tips are provided.
3) You get a copy of the jupyter notebook that you create which acts as a handy reference guide.
Please note that the version of Julia used is 1.0.4
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/is32m8/linear
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What's inside
Syllabus
Project Overview
By the end of this project you will learn how to use Julia for linear regression and multiple linear regression.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Focuses on the practical use of Julia for linear regression, making it suitable for data scientists and analysts who wish to leverage Julia's capabilities
Led by Vinita Silaparasetty, an experienced instructor in data science and machine learning
Provides hands-on coding experience through cloud desktops, enabling learners to actively participate in the learning process
Emphasizes industry-relevant skills by utilizing real-world stock market data for exercises
Provides a handy reference guide in the form of a Jupyter notebook, ensuring continued access to the learned material
Targets learners who are comfortable with basic programming concepts and have an interest in applying Julia for linear regression
Save this course
Save Linear Regression and Multiple Linear Regression in Julia to your list so you can find it easily later:
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Reviews summary
Instruction focused course
Learners say this instruction-set-based course is more about input than explanation. Reviews are largely negative, citing poor explanations and misinformation.
Incorrect information provided.
"Even quite a few mistakes/ misinformation."
Concepts not explained well; information not clear.
"Little information and very poorly explained."
"very poorly explained"
Course geared toward instructions over explanations.
"It's more a set of instructions on what to type rather than an explanation."
"not an explanation."
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 Linear Regression and Multiple Linear Regression in Julia with these
activities:
Find a mentor
Show steps
Provides you with guidance and support from someone who has already been through what you are going through.
Browse courses on
Linear Regression
Show steps
-
Identify someone who has experience in the field you are interested in.
-
Reach out to them and ask if they would be willing to mentor you.
Go over the prerequisites
Show steps
Warms you up with some of the fundamental concepts covered in the course.
Browse courses on
Linear Regression
Show steps
-
Review the basics of linear algebra.
-
Go over some basic statistical concepts, such as mean, median, and standard deviation.
-
Recall basic probability concepts.
Watch some video tutorials
Show steps
Provides you with a more visual and interactive way to learn the material.
Browse courses on
Linear Regression
Show steps
-
Find some video tutorials on linear regression and multiple linear regression.
-
Watch the tutorials and take notes.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Solve some practice problems
Show steps
Helps you solidify your understanding of the concepts by solving some practice problems.
Browse courses on
Linear Regression
Show steps
-
Solve the practice problems at the end of each chapter.
-
Find some online practice problems and solve them.
Join a study group
Show steps
Provides you with an opportunity to discuss the material with other students and get help with any questions you have.
Browse courses on
Linear Regression
Show steps
-
Join a study group or create your own.
-
Meet with your study group regularly to discuss the material and work on practice problems.
Create a cheat sheet
Show steps
Helps you summarize the key concepts and formulas in a concise and easy-to-reference format.
Browse courses on
Linear Regression
Show steps
-
Identify the key concepts and formulas from the course.
-
Create a cheat sheet that summarizes these concepts and formulas.
-
Use your cheat sheet to review the material regularly.
Participate in a Kaggle competition
Show steps
Challenges you to apply your skills to a real-world problem and get feedback from other data scientists.
Browse courses on
Machine Learning
Show steps
-
Find a Kaggle competition that you are interested in.
-
Download the data and explore it.
-
Build a model and submit it to the competition.
-
Analyze the results of your submission.
Build a portfolio project
Show steps
Allows you to apply your skills to a real-world problem and showcase your work to potential employers.
Browse courses on
Linear Regression
Show steps
-
Identify a problem that you can solve using linear regression or multiple linear regression.
-
Collect data and explore it.
-
Build a model and train it on your data.
-
Deploy your model and track its performance.
Find a mentor
Show steps
Provides you with guidance and support from someone who has already been through what you are going through.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Identify someone who has experience in the field you are interested in.
- Reach out to them and ask if they would be willing to mentor you.
Go over the prerequisites
Show steps
Warms you up with some of the fundamental concepts covered in the course.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Review the basics of linear algebra.
- Go over some basic statistical concepts, such as mean, median, and standard deviation.
- Recall basic probability concepts.
Watch some video tutorials
Show steps
Provides you with a more visual and interactive way to learn the material.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Find some video tutorials on linear regression and multiple linear regression.
- Watch the tutorials and take notes.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Solve some practice problems
Show steps
Helps you solidify your understanding of the concepts by solving some practice problems.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Solve the practice problems at the end of each chapter.
- Find some online practice problems and solve them.
Join a study group
Show steps
Provides you with an opportunity to discuss the material with other students and get help with any questions you have.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Join a study group or create your own.
- Meet with your study group regularly to discuss the material and work on practice problems.
Create a cheat sheet
Show steps
Helps you summarize the key concepts and formulas in a concise and easy-to-reference format.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Identify the key concepts and formulas from the course.
- Create a cheat sheet that summarizes these concepts and formulas.
- Use your cheat sheet to review the material regularly.
Participate in a Kaggle competition
Show steps
Challenges you to apply your skills to a real-world problem and get feedback from other data scientists.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find a Kaggle competition that you are interested in.
- Download the data and explore it.
- Build a model and submit it to the competition.
- Analyze the results of your submission.
Build a portfolio project
Show steps
Allows you to apply your skills to a real-world problem and showcase your work to potential employers.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Identify a problem that you can solve using linear regression or multiple linear regression.
- Collect data and explore it.
- Build a model and train it on your data.
- Deploy your model and track its performance.
Career center
Learners who complete Linear Regression and Multiple Linear Regression in Julia will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer is a professional who designs and develops machine learning models that can be used for a variety of purposes, such as predicting future outcomes or identifying patterns in data. Linear regression and multiple linear regression are important techniques for Machine Learning Engineers to learn, and this course would provide them with a good foundation in these techniques.
Data Scientist
Data Scientist
A Data Scientist is a professional who uses a variety of techniques to solve business problems, including data analysis, machine learning, and statistical modeling. This course would be very useful for a Data Scientist who wants to learn how to use linear regression and multiple linear regression in their work.
Statistician
Statistician
A Statistician is a professional who collects, analyzes, and interprets data to help businesses and organizations make informed decisions. Linear regression and multiple linear regression are important techniques for Statisticians to learn, and this course would provide them with a good foundation in these techniques.
Data Analyst
Data Analyst
A Data Analyst is a professional who uses their understanding of data and data analysis techniques to solve business problems. They often specialize in working with large, complex data sets and using statistical modeling and machine learning to analyze the data. The course Linear Regression and Multiple Linear Regression in Julia would be very useful for a Data Analyst who wants to learn how to use these techniques in their work.
Financial Analyst
Financial Analyst
A Financial Analyst is a professional who analyzes financial data to help businesses and investors make informed decisions. Linear regression and multiple linear regression are important techniques for Financial Analysts to learn, and this course would provide them with a good foundation in these techniques.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst is a professional who uses mathematical and statistical models to analyze financial data and make investment decisions. Linear regression and multiple linear regression are important techniques for Quantitative Analysts to learn, and this course would provide them with a good foundation in these techniques.
Actuary
Actuary
An Actuary is a professional who uses mathematical and statistical techniques to assess risk and uncertainty. Linear regression and multiple linear regression are important techniques for Actuaries to learn, and this course would provide them with a good foundation in these techniques.
Risk Manager
Risk Manager
A Risk Manager is a professional who identifies, assesses, and manages risk for businesses and organizations. Linear regression and multiple linear regression are important techniques for Risk Managers to learn, and this course would provide them with a good foundation in these techniques.
Epidemiologist
Epidemiologist
An Epidemiologist is a professional who studies the distribution and determinants of disease in populations. Linear regression and multiple linear regression are important techniques for Epidemiologists to learn, and this course would provide them with a good foundation in these techniques.
Biostatistician
Biostatistician
A Biostatistician is a professional who uses statistical and mathematical techniques to analyze biological data. Linear regression and multiple linear regression are important techniques for Biostatisticians to learn, and this course would provide them with a good foundation in these techniques.
Market Researcher
Market Researcher
A Market Researcher is a professional who collects and analyzes data about customers and markets. Linear regression and multiple linear regression are important techniques for Market Researchers to learn, and this course would provide them with a good foundation in these techniques.
Insurance Analyst
Insurance Analyst
An Insurance Analyst is a professional who analyzes insurance data to help insurance companies make informed decisions. Linear regression and multiple linear regression are important techniques for Insurance Analysts to learn, and this course would provide them with a good foundation in these techniques.
Econometrician
Econometrician
An Econometrician is a professional who uses statistical and mathematical techniques to analyze economic data. Linear regression and multiple linear regression are important techniques for Econometricians to learn, and this course would provide them with a good foundation in these techniques.
Survey Researcher
Survey Researcher
A Survey Researcher is a professional who designs and conducts surveys to collect data about populations. Linear regression and multiple linear regression are important techniques for Survey Researchers to learn, and this course would provide them with a good foundation in these techniques.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst is a professional who uses mathematical and statistical techniques to improve the efficiency of business operations. Linear regression and multiple linear regression are important techniques for Operations Research Analysts to learn, and this course would provide them with a good foundation in these techniques.
For more career information including salaries, visit:
OpenCourser.com/course/is32m8/linear
Reading list
We've selected 15 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
Linear Regression and Multiple Linear Regression in Julia.
Provides a comprehensive treatment of multiple linear regression. It valuable resource for anyone who wants to learn more about this topic.
Comprehensive introduction to statistical learning, including linear regression and multiple linear regression. It valuable resource for anyone who wants to learn more about these topics.
More advanced treatment of statistical learning, but it still covers linear regression and multiple linear regression in detail. It valuable resource for anyone who wants to learn more about these topics.
Provides a comprehensive introduction to Bayesian reasoning and machine learning. It covers a wide range of topics, including linear regression and multiple linear regression. It valuable resource for anyone who wants to learn more about these topics.
Provides a practical guide to linear regression. It valuable resource for anyone who wants to learn how to use linear regression to solve real-world problems.
Provides a comprehensive treatment of linear models in R. It valuable resource for anyone who wants to learn how to use R for linear regression and multiple linear regression.
Provides a comprehensive introduction to statistical learning, including linear regression and multiple linear regression. It valuable reference for anyone who wants to learn more about these topics.
Provides a comprehensive introduction to machine learning with Python. It covers a wide range of topics, including linear regression and multiple linear regression. It valuable resource for anyone who wants to learn how to use Python for machine learning.
Provides a comprehensive introduction to machine learning with Python. It covers a wide range of topics, including linear regression and multiple linear regression. It valuable resource for anyone who wants to learn how to use Python for machine learning.
Provides a comprehensive introduction to machine learning with R. It covers a wide range of topics, including linear regression and multiple linear regression. It valuable resource for anyone who wants to learn how to use R for machine learning.
Save
Provides a comprehensive introduction to deep learning. It covers a wide range of topics, including linear regression and multiple linear regression. It valuable resource for anyone who wants to learn more about these topics.
Provides a comprehensive introduction to pattern recognition and machine learning. It covers a wide range of topics, including linear regression and multiple linear regression. It valuable resource for anyone who wants to learn more about these topics.
Provides a comprehensive introduction to machine learning. It covers a wide range of topics, including linear regression and multiple linear regression. It valuable resource for anyone who wants to learn more about these topics.
Provides a comprehensive introduction to machine learning from a probabilistic perspective. It covers a wide range of topics, including linear regression and multiple linear regression. It valuable resource for anyone who wants to learn more about these topics.
Provides a gentle introduction to regression analysis. It valuable resource for anyone who wants to learn the basics of this topic.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/is32m8/linear
Share
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Effort
1 hour 30 minutes
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Vinita Silaparasetty
Language
English
Good to know
Focuses on the practical use of Julia for linear regression, making it suitable for data scientists and analysts who wish to leverage Julia's capabilities
Led by Vinita Silaparasetty, an experienced instructor in data science and machine learning
Provides hands-on coding experience through cloud desktops, enabling learners to actively participate in the learning process
Emphasizes industry-relevant skills by utilizing real-world stock market data for exercises
Provides a handy reference guide in the form of a Jupyter notebook, ensuring continued access to the learned material
Targets learners who are comfortable with basic programming concepts and have an interest in applying Julia for linear regression
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