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Snehan Kekre
Welcome to this project-based course on Linear Regression with NumPy and Python. In this project, you will do all the machine learning without using any of the popular machine learning libraries such as scikit-learn and statsmodels. The aim of this project and is to implement all the machinery, including gradient descent and linear regression, of the various learning algorithms yourself, so you have a deeper understanding of the fundamentals.
Read more
Welcome to this project-based course on Linear Regression with NumPy and Python. In this project, you will do all the machine learning without using any of the popular machine learning libraries such as scikit-learn and statsmodels. The aim of this project and is to implement all the machinery, including gradient descent and linear regression, of the various learning algorithms yourself, so you have a deeper understanding of the fundamentals.
Read more
Welcome to this project-based course on Linear Regression with NumPy and Python. In this project, you will do all the machine learning without using any of the popular machine learning libraries such as scikit-learn and statsmodels. The aim of this project and is to implement all the machinery, including gradient descent and linear regression, of the various learning algorithms yourself, so you have a deeper understanding of the fundamentals.
This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, NumPy, and Seaborn pre-installed.
Register for this course and see more details by visiting:
OpenCourser.com/course/98ns3y/linear
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Syllabus
Project: Linear Regression with NumPy and Python
Welcome to this project-based course on Linear Regression with NumPy and Python. In this project, you will do all the machine learning without using any of the popular machine learning libraries such as scikit-learn and statsmodels. The aim of this project and is to implement all the machinery of the various algorithms yourself, so you have a deeper understanding of the fundamentals.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops deep learning concepts and fundamentals for those without a strong math and programming background
Lays a solid foundation in linear regression with NumPy and Python, suitable for beginners
Teaches essential machine learning concepts without relying on popular libraries, fostering a deep understanding of algorithms
Requires learners to come in with some programming experience and basic Python knowledge to grasp the presented concepts
Uses pre-configured cloud desktops, providing a seamless hands-on learning experience
Save this course
Save Linear Regression with NumPy and Python to your list so you can find it easily later:
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Reviews summary
Linear regression made practical
Students largely agree that this highly rated course is a good way to learn the basics of linear regression in Python and NumPy. Engaging guided projects provide learners with an opportunity to practice what they learn and apply it to real-world problems. The instructor is knowledgeable and presents the material in a clear and concise manner. However, some learners have complained about the slow speed of the course platform and the lack of detailed explanations in some areas.
The instructor presents the content in a clear and concise way.
"The instructor is very well equipped with the knowledge and guides us through the project very well."
"Simple and efficient."
"Being able to code along with the video was so helpful, too."
Hands-on guided projects help students understand the concepts better.
"Guided projects are just amazing while working around Hands-on side by side which gives quite Good Understanding."
"This project was just the right one to get me started on my path to machine learning."
"I have read many articles and enrolled in several courses attempting to teach linear regression from scratch. This course provides the best balance of sufficient math to enable a deeper understanding and the practicality of seeing a simple implementation of the algorithm actually working in numpy."
Some learners have complained about the lack of detail in some of the explanations.
"The rhyme was very slow.....and in poor quality...video quality was intermediate but software quality is poor."
"The instructor is very well equipped with the knowledge and guides us through the project very well, its only the slow loading of the instructors video that made the learning experience bad."
"It was helpful as recently I had seen a linear regression problem which was too complicated. But this project helped me understand the basics properly to continue my interest in Python language. It was interesting. Thank You"
The course platform can be slow.
"The server is too slow."
"The important points should be discussed thoroughly, The instructor was not able to make the user understand why he was using this and that part, he lacked some teaching skills."
"The course would have been better if the project was a little bigger and not just the coding of gradient descent."
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 with NumPy and Python with these
activities:
Review your previous knowledge of algebra
Show steps
Refresh your knowledge of algebra to fill in any gaps in your understanding before the course begins.
Browse courses on
Algebra
Show steps
-
Go over your notes from previous algebra courses.
-
Take practice problems to test your understanding.
Review the course textbook
Show steps
Get a head start on the course material by reviewing the course textbook before the class starts.
View
Introduction to Linear Algebra (Gilbert Strang)
on Amazon
Show steps
Organize your notes and course materials
Show steps
Stay organized by compiling your notes, assignments, and other course materials into a central location for easy reference and review.
Browse courses on
Linear Algebra
Show steps
-
Create a dedicated folder or notebook for the course.
-
Regularly add and organize your notes, assignments, and any relevant resources.
-
Review your organized materials periodically to reinforce your understanding.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Watch video tutorials on linear algebra concepts
Show steps
Supplement your learning by watching video tutorials that provide clear explanations and visual demonstrations of linear algebra concepts.
Browse courses on
Linear Algebra
Show steps
-
Search for video tutorials on YouTube or other online platforms.
-
Choose tutorials that align with the course topics and your learning style.
-
Take notes and pause the videos when needed to fully understand the concepts.
Practice solving linear equations and systems of equations
Show steps
Gain proficiency in solving linear equations and systems of equations, which are fundamental concepts in linear algebra.
Browse courses on
Linear Equations
Show steps
-
Find practice problems online or in textbooks.
-
Set aside time each week to practice solving problems.
-
Check your solutions against answer keys or online resources.
Develop a cheat sheet of formulas and concepts
Show steps
Create a personalized resource summarizing key formulas and concepts, which can aid in understanding and retention.
Browse courses on
Linear Algebra
Show steps
-
Gather formulas and concepts from course materials.
-
Organize and condense the information into a concise cheat sheet.
-
Review the cheat sheet regularly to reinforce your understanding.
Join a study group or participate in online forums
Show steps
Engage with peers to discuss course material, ask questions, and clarify concepts, fostering a deeper understanding.
Browse courses on
Linear Algebra
Show steps
-
Find a study group or join online forums related to linear algebra.
-
Actively participate in discussions and ask thoughtful questions.
-
Collaborate with others to solve problems and exchange ideas.
Contribute to open-source linear algebra projects
Show steps
Gain hands-on experience by contributing to open-source projects related to linear algebra, deepening your understanding and practical skills.
Browse courses on
Linear Algebra
Show steps
-
Identify open-source linear algebra projects on platforms like GitHub.
-
Choose a project that aligns with your interests and skill level.
-
Read the project documentation and contribute code or documentation improvements.
Review your previous knowledge of algebra
Show steps
Refresh your knowledge of algebra to fill in any gaps in your understanding before the course begins.
Browse courses on
Algebra
Show steps
Show steps
Show steps
- Go over your notes from previous algebra courses.
- Take practice problems to test your understanding.
Review the course textbook
Show steps
Get a head start on the course material by reviewing the course textbook before the class starts.
View
Introduction to Linear Algebra (Gilbert Strang)
on Amazon
Show steps
Show steps
Show steps
Organize your notes and course materials
Show steps
Stay organized by compiling your notes, assignments, and other course materials into a central location for easy reference and review.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Create a dedicated folder or notebook for the course.
- Regularly add and organize your notes, assignments, and any relevant resources.
- Review your organized materials periodically to reinforce your understanding.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Watch video tutorials on linear algebra concepts
Show steps
Supplement your learning by watching video tutorials that provide clear explanations and visual demonstrations of linear algebra concepts.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Search for video tutorials on YouTube or other online platforms.
- Choose tutorials that align with the course topics and your learning style.
- Take notes and pause the videos when needed to fully understand the concepts.
Practice solving linear equations and systems of equations
Show steps
Gain proficiency in solving linear equations and systems of equations, which are fundamental concepts in linear algebra.
Browse courses on
Linear Equations
Show steps
Show steps
Show steps
- Find practice problems online or in textbooks.
- Set aside time each week to practice solving problems.
- Check your solutions against answer keys or online resources.
Develop a cheat sheet of formulas and concepts
Show steps
Create a personalized resource summarizing key formulas and concepts, which can aid in understanding and retention.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Gather formulas and concepts from course materials.
- Organize and condense the information into a concise cheat sheet.
- Review the cheat sheet regularly to reinforce your understanding.
Join a study group or participate in online forums
Show steps
Engage with peers to discuss course material, ask questions, and clarify concepts, fostering a deeper understanding.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Find a study group or join online forums related to linear algebra.
- Actively participate in discussions and ask thoughtful questions.
- Collaborate with others to solve problems and exchange ideas.
Contribute to open-source linear algebra projects
Show steps
Gain hands-on experience by contributing to open-source projects related to linear algebra, deepening your understanding and practical skills.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Identify open-source linear algebra projects on platforms like GitHub.
- Choose a project that aligns with your interests and skill level.
- Read the project documentation and contribute code or documentation improvements.
Career center
Learners who complete Linear Regression with NumPy and Python will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use their strong background in mathematics and statistics to extract knowledge from data. They work closely with other teams to communicate the meaning of this data. The ability to implement linear regression models is a must-have for Data Scientists. This course will help you build a foundation in linear regression, so you may be more effective in this role.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use their strong background in mathematics and statistics to develop and implement financial models. They work closely with other teams to communicate the meaning of these models. The ability to implement linear regression models is a must-have for Quantitative Analysts. This course will help you build a foundation in linear regression, so you may be more effective in this role.
Statistician
Statistician
Statisticians use their strong background in mathematics and statistics to collect, analyze, and interpret data. They work closely with other teams to communicate the meaning of this data. The ability to implement linear regression models is a must-have for Statisticians. This course will help you build a foundation in linear regression, so you may be more effective in this role.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers create the models that are used to make predictions. They use their strong background in computer science and statistics to find patterns in data. The ability to implement linear regression models is a must-have for Machine Learning Engineers. This course will help you build a foundation in linear regression, so you may be more effective in this role.
Data Analyst
Data Analyst
Data Analysts use their strong background in mathematics to clean up and interpret raw data. They work closely with other teams to communicate the meaning of this data. The ability to implement linear regression models will greatly assist in performing these tasks. This course will help you build a foundation in linear regression, so you may be more effective at performing these tasks.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain computer software. They use their strong background in computer science to create software applications that meet the needs of users. The ability to implement linear regression models may be helpful in various software engineering roles. This course may help you build a foundation in linear regression, so you may be more effective in this role.
Financial Analyst
Financial Analyst
Financial Analysts use their strong background in finance and economics to analyze financial data. They work closely with other teams to communicate the meaning of this data. The ability to implement linear regression models may be helpful for Financial Analysts. This course may help you build a foundation in linear regression, so you may be more effective in this role.
Actuary
Actuary
Actuaries use their strong background in mathematics and statistics to assess risk. They work closely with other teams to communicate the meaning of this risk. The ability to implement linear regression models may be helpful for Actuaries. This course may help you build a foundation in linear regression, so you may be more effective in this role.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use their strong background in mathematics and statistics to solve problems. They work closely with other teams to communicate the meaning of these solutions. The ability to implement linear regression models may be helpful for Operations Research Analysts. This course may help you build a foundation in linear regression, so you may be more effective in this role.
Business Analyst
Business Analyst
Business Analysts use their strong background in business and technology to solve problems. They work closely with other teams to communicate the meaning of these solutions. The ability to implement linear regression models may be helpful for Business Analysts. This course may help you build a foundation in linear regression, so you may be more effective in this role.
Market Researcher
Market Researcher
Market Researchers use their strong background in marketing and research to collect and analyze data. They work closely with other teams to communicate the meaning of this data. The ability to implement linear regression models may be helpful for Market Researchers. This course may help you build a foundation in linear regression, so you may be more effective in this role.
Economist
Economist
Economists use their strong background in economics to analyze data. They work closely with other teams to communicate the meaning of this data. The ability to implement linear regression models may be helpful for Economists. This course may help you build a foundation in linear regression, so you may be more effective in this role.
For more career information including salaries, visit:
OpenCourser.com/course/98ns3y/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 with NumPy and Python.
More advanced treatment of statistical learning than the previous book. It covers a wider range of topics, including linear and nonlinear regression, classification, and clustering.
Save
Provides a comprehensive overview of deep learning. It covers a wide range of topics, including convolutional neural networks, recurrent neural networks, and generative adversarial networks.
Provides a comprehensive overview of reinforcement learning. It covers a wide range of topics, including Markov decision processes, dynamic programming, and deep reinforcement learning.
Is an introduction to statistical learning, which branch of statistics that deals with the analysis of data. It valuable reference for anyone who wants to learn more about linear regression and other machine learning techniques.
Comprehensive guide to linear models in R. It covers a wide range of topics, including linear regression, ANOVA, and generalized linear models. It valuable resource for anyone who wants to learn more about linear models.
Provides a comprehensive overview of machine learning from a probabilistic perspective. It covers a wide range of topics, including linear regression, Bayesian inference, and deep learning.
Provides a mathematical foundation for machine learning. It covers topics such as linear algebra, calculus, and probability theory. It valuable resource for anyone who wants to understand the mathematical underpinnings of machine learning.
Practical guide to linear regression. It covers a wide range of topics, including simple linear regression, multiple linear regression, and ANOVA. It valuable resource for anyone who wants to learn more about linear regression.
Provides a comprehensive overview of statistical methods used in the atmospheric sciences. It covers a wide range of topics, including linear regression, ANOVA, and time series analysis.
Save
Provides a comprehensive overview of epidemiology. It covers a wide range of topics, including linear regression, logistic regression, and survival analysis.
Provides a comprehensive overview of biostatistics. It covers a wide range of topics, including linear regression, ANOVA, and nonparametric tests.
Provides a comprehensive overview of econometrics. It covers a wide range of topics, including linear regression, ANOVA, and time series analysis.
Practical guide to regression modeling. It covers a wide range of topics, including linear regression, generalized linear models, and survival analysis. It valuable resource for anyone who wants to learn more about regression modeling.
Provides a comprehensive overview of statistical methods used in psychology. It covers a wide range of topics, including linear regression, ANOVA, and nonparametric tests.
Save
Provides a comprehensive overview of statistical methods used in the health sciences. It covers a wide range of topics, including linear regression, ANOVA, and survival analysis.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/98ns3y/linear
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Effort
1.5 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Snehan Kekre
Language
English
Good to know
Develops deep learning concepts and fundamentals for those without a strong math and programming background
Lays a solid foundation in linear regression with NumPy and Python, suitable for beginners
Teaches essential machine learning concepts without relying on popular libraries, fostering a deep understanding of algorithms
Requires learners to come in with some programming experience and basic Python knowledge to grasp the presented concepts
Uses pre-configured cloud desktops, providing a seamless hands-on learning experience
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